Search for: All records

ABSTRACT To accurately predict earth system response to global change, we must be able to predict the responses of important properties of that system, such as the depths over which plant roots are distributed. In 2008, H. J. Schenk proposed a model for the depth distribution of plant roots based on a simple hydrological scheme and the assumptions that plants will take up the shallowest water available first and will distribute their roots in proportion to long‐term mean uptake at each depth. Here, we derive an analytical solution to the Schenk model under an idealised climate (in which infiltration events are treated as a marked Poisson process), explore properties of the result and compare with data. The solution suggests that in very humid and arid climates, the soil wetting and drying cycles induced by root water uptake are generally confined to a characteristic depth below the surface. This depth depends on the typical magnitude of rainfall events (most strongly so in arid climates), the typical total transpiration demand between rainfall events (most strongly in humid climates) and the plant‐available water holding capacity of the soil. Root water uptake (and thus predicted root density) in very humid and arid landscapes decreases exponentially with depth at a rate determined by this characteristic depth. However, in a mesic climate, soils may be wet or dry to greater depths below the near‐surface, and the duration spent in each state increases with depth. Consequently, root water uptake and root density in mesic climates more closely resemble a power law distribution. When the aridity index is exactly 1, the characteristic depth diverges and the mean rooting depth approaches infinity. This suggests that the most skewed root depth distributions might occur in mesic environments. We compared this model to another analytical solution and a compiled database of root distributions (159 combined locations). For a larger comparison dataset, we also compared 99th percentile rooting depth to rooting depths modeled by two other frameworks and a database of observed rooting depths (1271 combined locations). Results demonstrate that the analytical formulation of the Schenk model performs well as a shallow bound on rooting depths and captures something of the nonexponential form of root distributions, and its error is similar to or less than that of other modeling frameworks. Errors may be partly explained by the deviation of real climate from the idealisations used to obtain an analytical solution (exponentially distributed infiltration events and no seasonality). 

Abstract Within Earth's critical zone, weathering processes influence landscape evolution and hillslope hydrology by creating porosity in bedrock, transforming it into saprolite and eventually soil. In situ weathering processes drive much of this transformation while preserving the rock fabric of the parent material. Inherited rock fabric in regolith makes the critical zone anisotropic, affecting its mechanical and hydrological properties. Therefore, quantifying and studying anisotropy is an important part of characterising the critical zone, yet doing so remains challenging. Seismic methods can be used to detect rock fabric and infer mechanical and hydrologic conductivity anisotropy across landscapes. We present a novel way of measuring seismic anisotropy in the critical zone using Rayleigh and Love surface waves. This method leverages multi‐component surface seismic data to create a high‐resolution model of seismic anisotropy, which we compare with a nuclear magnetic resonance log measured in a nearby borehole. The two geophysical data sets show that seismic anisotropy and porosity develop at similar depths in weathered bedrock and both reach their maximum values in saprolite, implying that in situ weathering enhances anisotropy while concurrently generating porosity in the critical zone. We bolster our findings with in situ measurements of seismic and hydrologic conductivity anisotropy made in a 3 m deep soil excavation. Our study offers a fresh perspective on the importance of rock fabric in the development and function of the critical zone and sheds new insights into how weathering processes operate. 

Abstract Features of landscape morphology—including slope, curvature, and drainage dissection—are important controls on runoff generation in upland landscapes. Over long timescales, runoff plays an essential role in shaping these same features through surface erosion. This feedback between erosion and runoff generation suggests that modeling long‐term landscape evolution together with dynamic runoff generation could provide insight into hydrological function. Here we examine the emergence of variable source area runoff generation in a new coupled hydro‐geomorphic model that accounts for water balance partitioning between surface flow, subsurface flow, and evapotranspiration as landscapes evolve over millions of years. We derive a minimal set of dimensionless numbers that provide insight into how hydrologic and geomorphic parameters together affect landscapes. Across the parameter space we investigated, model results collapsed to a single inverse relationship between the dimensionless relief and the ratio of catchment quickflow to discharge. Furthermore, we found an inverse relationship between the Hillslope number, which describes topographic relief relative to aquifer thickness, and the proportion of the landscape that was variably saturated. While the model generally produces fluvial topography visually similar to simpler landscape evolution models, certain parameter combinations produce wide valley bottom wetlands and non‐dendritic, trellis‐like drainage networks, which may reflect real conditions in some landscapes where aquifer gradients become decoupled from topography. With these results, we demonstrate the power of hydro‐geomorphic models for generating new insights into hydrological processes, and also suggest that subsurface hydrology may be integral for modeling aspects of long‐term landscape evolution. 

Abstract Topography is a key control on runoff generation, as topographic slope affects hydraulic gradients and curvature affects water flow paths. Simultaneously, runoff generation shapes topography through erosion, affecting landscape morphology over long timescales. Previous modeling efforts suggest that subsurface hydrological properties, relative to climate, are key mediators of this relationship. Specifically, when subsurface transmissivity and water storage capacity are low, (a) saturated areas and storm runoff should be larger and more variable, and (b) hillslopes shorter and lower relief, assuming other geomorphic factors are held constant. However, it remains uncertain whether subsurface properties can exert such strong controls on emergent properties in real landscapes. We compared emergent hydrological function and topography in two watersheds with very similar climatic and tectonic history, but very different subsurface properties due to contrasting bedrock lithology. We found that hillslopes were systematically shorter and saturated areas more dynamic at the lower transmissivity site. To test whether these features could be the result of coevolution between topography, hydrological function, and subsurface properties, we estimated all parameters of a coupled groundwater‐landscape evolution model for each site. Limitations were revealed in the model's ability to reproduce aspects of morphology and hydrologic behavior, however, model results suggested differences in hillslope length and variably saturated area between the sites could be explained by differences in subsurface properties, and not by differences in geomorphic process rates alone. This work demonstrates one way subsurface hydrology can profoundly affect landscape evolution. 

Abstract Controls on the physical and chemical architecture of the subsurface critical zone are somewhat controversial, with multiple hypotheses proposed to account for variations in the depth of weathering between sites, and with landscape position at a site. In the Piedmont region of the Mid‐Atlantic US weathering of crystalline bedrock has been observed to extend tens of meters below the surface and groundwater in a'bow‐tie’ shape – i.e. weathering extends to lower elevations below ridges than below channels. The chemical and physical structure of a hillslope transect in the Maryland Piedmont was explored with a 45 m borehole in the ridge, as well as shallow bedrock boreholes at the toe of the slope and valley. Chemical weathering fronts were characterized using elemental abundances and mineralogical analysis. The ridge borehole did not extend deeper than the chemically and physically weathered rock. Surface and borehole geophysics and density measurements were used to characterize the weathered rock and saprolite. Na and Ca results suggest that plagioclase feldspar weathering is similar between samples collected from 45 m under the ridge and 2.2 m under the valley bottom. A narrow Fe oxidation garnet weathering front co‐insides with the transition from weathered bedrock to saprolite, suggesting that this reaction may generate initial saprolite porosity. Muscovite weathering co‐occurs with complete depletion of plagioclase a few meters above the Fe oxidation front. These nested weathering fronts in the saprolite appear to follow a subdued version of the surface topography. The location and shape of the nested saprolite weathering fronts may be controlled by the feedback between the transport of reactants and solutes and reaction‐generated porosity, consistent with the conceptual “valve” hypothesis. Differing dominant control mechanisms on deep bedrock weathering and saprolite initiating reactions may explain the thickness and structure of the critical zone at our site. 

Key Points Formulae are derived for age‐ranked storage in, and solute transport through, unsteady hydrologic systems under shifted‐uniform selection The StorAge Selection function's single parameter indicates where a system falls along a continuum between plug‐flow and uniform sampling Model predictions are concordant with published measurements of solute breakthrough in a sloping lysimeter subject to periodic wetting 

Process-based modelling offers interpretability and physical consistency in many domains of geosciences but struggles to leverage large datasets efficiently. Machine-learning methods, especially deep networks, have strong predictive skills yet are unable to answer specific scientific questions. In this Perspective, we explore differentiable modelling as a pathway to dissolve the perceived barrier between process-based modelling and machine learning in the geosciences and demonstrate its potential with examples from hydrological modelling. ‘Differentiable’ refers to accurately and efficiently calculating gradients with respect to model variables or parameters, enabling the discovery of high-dimensional unknown relationships. Differentiable modelling involves connecting (flexible amounts of) prior physical knowledge to neural networks, pushing the boundary of physics-informed machine learning. It offers better interpretability, generalizability, and extrapolation capabilities than purely data-driven machine learning, achieving a similar level of accuracy while requiring less training data. Additionally, the performance and efficiency of differentiable models scale well with increasing data volumes. Under data-scarce scenarios, differentiable models have outperformed machine-learning models in producing short-term dynamics and decadal-scale trends owing to the imposed physical constraints. Differentiable modelling approaches are primed to enable geoscientists to ask questions, test hypotheses, and discover unrecognized physical relationships. Future work should address computational challenges, reduce uncertainty, and verify the physical significance of outputs.