**3. Research on integrated hillslope coevolution to improve predictions of landscape-scale change**

The LEO landscapes and their extensive instrumentation and control capabilities allow us to track every step along the evolutionary trajectory of a ZOB-scale system, from purely abiotic substrate to living, breathing ecosystems. Emulating their real-world archetypes, the convergent topography of these landscapes promotes spatially variable substrate and resource availability, which is anticipated to eventually facilitate biological diversity and influence how the landscapes filter precipitation and sequester carbon from the atmosphere. Refining our understanding of and our ability to predict how these and other significant ecosystem services are affected by landscape evolution, climatic variability, and long-term environmental change is the central goal of Earth scientists working in the LEO project. This section discusses current foci of research at LEO that target this goal by advancing understanding of how hydrological and geochemical (Section 3.2), microbiological (Section 3.3), and plant-ecological (Section 3.4) processes interact to drive the coevolution of incipient hillslopes and their mass and energy cycling. Concepts underlying these research foci and early results from initial experiments are presented. The process research at LEO is complemented by the development of hillslope-integrated parametrizations and distributed coupled-process modeling approaches that are hoped to ultimately allow improved prediction of real-world systems' behavior in a changing environment. These modeling approaches are described first (Section 3.1) and with emphasis on hydrologic predictions.

#### **3.1. Changing paradigms for hydrologic prediction at the hillslope scale**

In recent years, there has been a paradigm shift in our understanding of flow and transport at watershed scales and in our approaches to prediction. The complexity and heterogeneity of water movement within individual landscape units have been recognized in hillslopes [73, 74], riparian areas [75, 76], and within streams and their hyporheic zone [77]. This has led to calls for new predictive approaches that go beyond the traditional continuum models (i.e., Richards and Saint-Venant equations for flow and convection-dispersion/diffusion equations for transport) [40, 78–80], as these generally rely on calibrated "effective" property values to replace the spatially distributed properties of the landscape—those are essentially unknowable at catchment scales using current technology.

New approaches have sought ways to represent flow and transport directly at the scales of interest, with the expectation that the new equations may differ in form, not just in the parameters [79], from the continuum-scale equations. The concept of a representative elementary watershed, or REW [78, 81–83], provides a framework for representing flow through individual landscape elements and in a river network based on a rigorous time-space averaging of the conservation laws for mass, energy, momentum, and entropy. However, these equations are not complete. They require specification of "closure relations" that specify the boundary fluxes exchanged between these landscape elements in terms of their states and are parameterized by measurable properties of the landscape. These closure relations must represent the aggregate effect of the unresolved sub-REW heterogeneities and flow complexity without resolving them explicitly, and they have been termed the "Holy Grail" of scientific hydrology [78].

An overarching objective of the LEO project is to develop closure relations for hillslope-scale hydrologic flux and transport [7]. These closure relations are parameterizations of the fluxes that cross boundaries between hydrologically relevant units of the landscape [78, 81]—an elementary example is a storage-discharge relation [84, 85]. Taken broadly, such parameterizations are a component of all hydrologic models, but LEO is a useful experimental tool for developing closure relations at the scale of hillslopes [7]. At LEO, it is possible to observe boundary fluxes—and how they emerge from the distribution of internal state variables—with a precision not possible in real landscapes and at a scale not possible in bench-top experiments. Through experimentation and iterative modeling using both lumped and highly resolved models, efforts at LEO are driving toward a suite of hillslope closure relationships that (ideally) can be parameterized on the basis of the observable physical structure of the landscape.

**3. Research on integrated hillslope coevolution to improve** 

(*x*-coordinate) of the hillslope through its central plane (i.e., *y*-coordinate equal to zero).

The LEO landscapes and their extensive instrumentation and control capabilities allow us to track every step along the evolutionary trajectory of a ZOB-scale system, from purely abiotic substrate to living, breathing ecosystems. Emulating their real-world archetypes, the convergent topography of these landscapes promotes spatially variable substrate and resource availability, which is anticipated to eventually facilitate biological diversity and influence how the landscapes filter precipitation and sequester carbon from the atmosphere. Refining our understanding of and our ability to predict how these and other significant ecosystem services are affected by landscape evolution, climatic variability, and long-term environmental change is the central goal of Earth scientists working in the LEO project. This section discusses current foci of research at LEO that target this goal by advancing understanding of how hydrological and geochemical (Section 3.2), microbiological (Section 3.3), and plant-ecological (Section 3.4) processes interact to drive the coevolution of incipient hillslopes and their mass and energy cycling. Concepts underlying

**Figure 12.** Distribution of organic carbon (a), inorganic carbon (b), and total nitrogen (c) over a cross section of a LEO hillslope 3 years after inception (preliminary data). The cross section spans the depth (*z*-coordinate) and length

**predictions of landscape-scale change**

54 Hydrology of Artificial and Controlled Experiments

Work at LEO has focused on developing hillslope-scale closure relationships for discharge and for transport both by building on and testing existing theory, as well as by developing new approaches. Hillslope closure relations predicting discharge have been developed from the principles of hydraulic groundwater theory [86–88] and are being compared to both the results of physical experiments and numerical models. Three-dimensional Richards equationbased models have been implemented to simulate flow and transport dynamics in the LEO hillslopes ([41, 49]; see also Section 2.1.7) and calibrated to reproduce the flow data with the physically justifiable parameter sets. **Figure 13** illustrates the observed storage-discharge relationship from one of the LEO hillslopes and a modeled relationship. The observed relationship shows a large degree of hysteresis, and the simulated relationship captures most of the features of this relationship. This type of hysteresis can be captured by the theoretical frameworks of Troch [88] and others, but not by the typical one-to-one storage-discharge relationships used in hydrologic models to simulate baseflow.

an identical fashion every 3.5 days over the course of 4 weeks—a total of about 580 mm irriga-

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These LEO experimental results can be extended to a wider class of idealized hillslopes through fine resolution modeling of system-scale flow and transport dynamics in "virtual hillslopes". The effect of variations in hillslope morphology, soil properties, and climate forcing on flow and transport closures can be examined using a three-dimensional Richards equation-based model and particle tracking algorithm validated against the LEO dataset. Moreover, the sensitivity of the parameterizations to observable physical properties (and

However, LEO was inspired in part by efforts to go beyond the typical approach in hydrology of treating hydrologic properties as fixed features of the landscape, and instead ask deeper questions about why a landscape has the properties it does and how it came to be that way [91]. Physical, chemical, and biological data are also collected at LEO to connect the hydrologic behavior to interacting critical zone processes and ultimately to the coevolution of the system [3]. We aim to understand how the landscape internal structure evolves over time, feeding back on the flow and transport processes and modifying the emergent behavior that

To develop improved predictive ability of the hydrological as well as biogeochemical and ecological responses in evolving landscapes, a second focus of the modeling at LEO is therefore the development of a coupled-process modeling framework. This modeling framework, referred to as TIMS (Section 2.1.7), aims to not only parameterize water flow and transport as functions of static landscape properties. Instead, it focuses specifically on coupling hydrological, microbial, geochemical, geomorphological, and ecological processes and considers the landscape properties themselves as dynamic. By attempting to predict dynamic landscape parameters based on a fundamental understanding of how they are created in the coevolution of a landscape, we may stand a chance to overcome the need for assumptions on unmeasurable "effective" model parameters and the inherent inability of most current modeling tools

At present, however, TIMS and other state-of-the-art Earth system models are still inadequate to represent many key Earth system processes that control long-term land-atmosphere exchanges of energy, water, and carbon. This is due to a lack of predictive understanding of the interactions between the relevant physical, chemical, and biological processes. Therefore, the modeling system will be continuously extended, synthesizing results from the physical experimentation at LEO to develop representations of critical process couplings that are currently not adequately represented. These include, for example, schemes to represent the evolution of landscape heterogeneity associated with transport and deposition of particles (colloids and sediments) and biogeochemical weathering, as well as associated feedbacks with the hydraulic properties of the LEO soils. Other areas of active model development include parameterizing leaf and root dynamics adaptive to environmental changes (e.g., high temperature and drought) and soil carbon dynamics as affected by geochemical reactions, microbial enzymes, climate, and water flow [33].

The observational and modeling results of experiments conducted under the present conditions will eventually be compared to results of identical experiments performed after the hillslopes

tions. The results are revealing much about the nature of hillslope rSAS functions.

their associated uncertainties) can be deduced.

is the basis of flow and transport closure relations.

to represent integral adaptation to change (e.g., in climate).

Projects at LEO have also driven the development of new parameterizations of hillslope-based transport that build on the concept of rank-storage selection functions (rSAS; [89]). This is a highly promising approach for a new generation of transport models [90]. rSAS theory depends on parameterization of probability distributions that capture the emergent effect of finer-scale processes determining transport through the hillslope. These functions extend the idea of a storagedischarge relationship: the function specifies not the overall discharge as a function of storage but rather the way the age distribution of discharge is selected from the age distribution of storage.

The PERTH (PERiodic Tracer Hierarchy) was developed to allow rSAS functions to be directly determined from the results of physical tracer experiments [14]. The key requirement is that the flow varies in a periodic way, so that the progressive breakthrough of a single tracer injection reveals information about the contribution to discharge of multiple ages at each time in the cycle. The LEO hillslope hydrodynamics can be controlled to allow precise observations of the flow and transport dynamics, and a large-scale PERTH-type experiment was conducted between 1 December 2016 and 28 December 2016. All three slopes were irrigated in

**Figure 13.** Observed and model storage-discharge relationship of a LEO landscape (for the period of 1–30 June 2015).

an identical fashion every 3.5 days over the course of 4 weeks—a total of about 580 mm irrigations. The results are revealing much about the nature of hillslope rSAS functions.

results of physical experiments and numerical models. Three-dimensional Richards equationbased models have been implemented to simulate flow and transport dynamics in the LEO hillslopes ([41, 49]; see also Section 2.1.7) and calibrated to reproduce the flow data with the physically justifiable parameter sets. **Figure 13** illustrates the observed storage-discharge relationship from one of the LEO hillslopes and a modeled relationship. The observed relationship shows a large degree of hysteresis, and the simulated relationship captures most of the features of this relationship. This type of hysteresis can be captured by the theoretical frameworks of Troch [88] and others, but not by the typical one-to-one storage-discharge relation-

Projects at LEO have also driven the development of new parameterizations of hillslope-based transport that build on the concept of rank-storage selection functions (rSAS; [89]). This is a highly promising approach for a new generation of transport models [90]. rSAS theory depends on parameterization of probability distributions that capture the emergent effect of finer-scale processes determining transport through the hillslope. These functions extend the idea of a storagedischarge relationship: the function specifies not the overall discharge as a function of storage but rather the way the age distribution of discharge is selected from the age distribution of storage. The PERTH (PERiodic Tracer Hierarchy) was developed to allow rSAS functions to be directly determined from the results of physical tracer experiments [14]. The key requirement is that the flow varies in a periodic way, so that the progressive breakthrough of a single tracer injection reveals information about the contribution to discharge of multiple ages at each time in the cycle. The LEO hillslope hydrodynamics can be controlled to allow precise observations of the flow and transport dynamics, and a large-scale PERTH-type experiment was conducted between 1 December 2016 and 28 December 2016. All three slopes were irrigated in

**Figure 13.** Observed and model storage-discharge relationship of a LEO landscape (for the period of 1–30 June 2015).

ships used in hydrologic models to simulate baseflow.

56 Hydrology of Artificial and Controlled Experiments

These LEO experimental results can be extended to a wider class of idealized hillslopes through fine resolution modeling of system-scale flow and transport dynamics in "virtual hillslopes". The effect of variations in hillslope morphology, soil properties, and climate forcing on flow and transport closures can be examined using a three-dimensional Richards equation-based model and particle tracking algorithm validated against the LEO dataset. Moreover, the sensitivity of the parameterizations to observable physical properties (and their associated uncertainties) can be deduced.

However, LEO was inspired in part by efforts to go beyond the typical approach in hydrology of treating hydrologic properties as fixed features of the landscape, and instead ask deeper questions about why a landscape has the properties it does and how it came to be that way [91]. Physical, chemical, and biological data are also collected at LEO to connect the hydrologic behavior to interacting critical zone processes and ultimately to the coevolution of the system [3]. We aim to understand how the landscape internal structure evolves over time, feeding back on the flow and transport processes and modifying the emergent behavior that is the basis of flow and transport closure relations.

To develop improved predictive ability of the hydrological as well as biogeochemical and ecological responses in evolving landscapes, a second focus of the modeling at LEO is therefore the development of a coupled-process modeling framework. This modeling framework, referred to as TIMS (Section 2.1.7), aims to not only parameterize water flow and transport as functions of static landscape properties. Instead, it focuses specifically on coupling hydrological, microbial, geochemical, geomorphological, and ecological processes and considers the landscape properties themselves as dynamic. By attempting to predict dynamic landscape parameters based on a fundamental understanding of how they are created in the coevolution of a landscape, we may stand a chance to overcome the need for assumptions on unmeasurable "effective" model parameters and the inherent inability of most current modeling tools to represent integral adaptation to change (e.g., in climate).

At present, however, TIMS and other state-of-the-art Earth system models are still inadequate to represent many key Earth system processes that control long-term land-atmosphere exchanges of energy, water, and carbon. This is due to a lack of predictive understanding of the interactions between the relevant physical, chemical, and biological processes. Therefore, the modeling system will be continuously extended, synthesizing results from the physical experimentation at LEO to develop representations of critical process couplings that are currently not adequately represented. These include, for example, schemes to represent the evolution of landscape heterogeneity associated with transport and deposition of particles (colloids and sediments) and biogeochemical weathering, as well as associated feedbacks with the hydraulic properties of the LEO soils. Other areas of active model development include parameterizing leaf and root dynamics adaptive to environmental changes (e.g., high temperature and drought) and soil carbon dynamics as affected by geochemical reactions, microbial enzymes, climate, and water flow [33].

The observational and modeling results of experiments conducted under the present conditions will eventually be compared to results of identical experiments performed after the hillslopes have been altered either through endogenous changes or through the introduction of new factors, such as the establishment of plants. As the LEO hillslopes evolve over time, we will iterate the physical and the numerical experiments to test hypotheses about the feedbacks between flow and transport dynamics and hillslope evolution. Lessons learned from failure and success in reproducing observations in the physical landscapes (see, e.g., [41]) will then be used to refine the mathematical representations and reduce uncertainties in model structures and parameters. The LEO landscapes and diverse modeling approaches are thereby anticipated to help fill the gap between plot-scale studies and larger scale (hillslope to global) model developments by constructing relationships between varying fluxes and states at the ZOB-scale, both through direct inference of closure relations and scaling of coupled-process modeling schemes. These developments may finally serve to project impacts of climate change on water resources as well as ecological processes and landscape evolution in varied environmental contexts.

Since the distribution of microorganisms and vegetation on the slopes in space and time is driven by water and nutrient availability (Sections 3.3 and 3.4), biota will further complicate the already complex relationships between water flow and weathering in evolving hillslopes.

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Precipitation of secondary minerals during incongruent weathering decreases the particle size of the soil, which affects its pore size distribution and hydraulic conductivity, and consequently the water transit times [100, 101]. Mineral precipitation can also feedback to further weathering [102, 103], nutrient and carbon retention, and microbial and plant distribution. Coupled geochemical-hydrological modeling (combined with pedotransfer functions) was performed for the LEO hillslopes to estimate mineral transformations and changes in soil hydraulic properties over a 10-year period with rainfall amount derived from several real-world locations in the southwestern United States [8]. The predictions suggested a significant increase in the fraction of secondary minerals and, as a result, a decrease in hydraulic conductivity over time. Predicted changes were highly variable in space, closely mirroring flow and saturation patterns on the hillslopes. It was further observed that solution-phase concentrations of lithogenic elements calculated by the models could provide early indication of the soil formation processes, even before changes in the solid phase would have become readily measurable. Since then, the physical experiments performed at LEO [54, 55] confirm both development of heterogeneity in the solution composition as a function of flow patterns and spatially resolved precipitation of secondary minerals as indicated by saturation indices for a number of minerals that would influence soil structure and flow patterns. For example, it can be seen for calcite (**Figure 14**) that there is pronounced spatial heterogeneity in the measurements linked to soil water content, and that there is a temporal progression in the saturation indices as the hillslope gets drier. Results from LEO soil cores further support spatially resolved precipitation of inorganic

The geochemical evolution of a hillslope is also strongly linked to runoff generation. The fraction of incoming rainfall that eventually leaves the landscapes as seepage outflow (or overland flow, if any) affects the export of inorganic and organic compounds from the landscapes. This export process presents an integral part of soil formation. When soils age, their composition shifts toward elements such as iron and aluminum that form poorly soluble oxides and hydroxides, as these are retained. More soluble elements in turn are lost, such as silicon, which presents an originally predominant element on the landscape. Seepage generation additionally has significant implications for the carbon balance in the environment. The export of inorganic carbon that was previously captured in the landscape as a result of weathering (predominantly as bicarbonate and carbonate), and its transport and further storage in the oceans, serve as important mechanisms of carbon sequestration from the atmosphere. Weathering of basaltic rocks, such as those comprising the LEO landscapes, occurs rapidly [104], and our experiments showed that as much as 5 kg of carbon was lost from the hillslopes in a single rain event (see **Figure 8**; [54]). Finally, feedbacks between water flow, mineral weathering and soil formation, and biological activity can have important implications for water quality. These feedbacks control the off-site transport not only of lithogenic and biogenic compounds but also of potential anthropogenic and possibly hazardous compounds

carbon through direct measurements (Section 2.5).

into streams and other downstream landscape elements.

#### **3.2. Linking hydrological and geochemical processes in evolving landscapes**

The nature of hydrological and geochemical interactions is a primary determinant of hillslope structure formation, available water quantity, and water quality along the entire evolutionary trajectory of a landscape―from pristine abiotic substrate subjected to a first rain pulse, when microbial (Section 3.3) and vascular plant (Section 3.4) life only begin to establish, to a matured landscape adjusting to variable climatic forcing. Examining and predicting the time evolution of subsurface structure through biogeochemical processes and its effect on hydrological partitioning and water residence time are therefore a key focus of the research at LEO. The LEO experiment provides unprecedented capability to examine the complex interactions that are integral to the hillslope evolution and soil formation processes because of the high density of hydrological and geochemical measurements in space and time, control of some of the drivers of weathering such as rainfall or temperature, and the ability to perform coupled hydrological-biogeochemical modeling.

In any developing landscape, including the LEO landscapes, the spatial structure of flow pathways along hillslopes determines the rate, extent, and distribution of geochemical reactions (and biological colonization) that drive weathering, the transport and precipitation of solutes and sediments, and the evolution of soil structure. The resulting structure and process evolution, in turn, induces spatiotemporal variability of hydrological states and flow pathways. Weathering reactions are strongly influenced by the time water spends along the subsurface flow paths [92, 93] at any stage of the landscape development. Dissolution of primary silicates is kinetically limited, and increasing residence time, and thus the duration of contact time of water with rocks, therefore potentially increases the concentration of lithogenic elements in the soil solution. The resulting relative saturation of soil solution (as measured by the saturation index) with respect to soil minerals will affect both primary mineral dissolution and secondary mineral precipitation [94]. The farther a solution is from equilibrium, the higher the rates of both dissolution and precipitation processes. In addition to water residence times, rock dissolution rates are also influenced by formation of secondary minerals [95]. Plants and microorganisms can also strongly influence weathering rates through production of organic acids and other complexing agents [72, 96], through respiration and uptake of water and dissolving nutrients [97], and through enzymatic promotion of bicarbonate formation from CO<sup>2</sup> [98, 99]. Since the distribution of microorganisms and vegetation on the slopes in space and time is driven by water and nutrient availability (Sections 3.3 and 3.4), biota will further complicate the already complex relationships between water flow and weathering in evolving hillslopes.

have been altered either through endogenous changes or through the introduction of new factors, such as the establishment of plants. As the LEO hillslopes evolve over time, we will iterate the physical and the numerical experiments to test hypotheses about the feedbacks between flow and transport dynamics and hillslope evolution. Lessons learned from failure and success in reproducing observations in the physical landscapes (see, e.g., [41]) will then be used to refine the mathematical representations and reduce uncertainties in model structures and parameters. The LEO landscapes and diverse modeling approaches are thereby anticipated to help fill the gap between plot-scale studies and larger scale (hillslope to global) model developments by constructing relationships between varying fluxes and states at the ZOB-scale, both through direct inference of closure relations and scaling of coupled-process modeling schemes. These developments may finally serve to project impacts of climate change on water resources as well as ecological processes and landscape evolution in varied environmental contexts.

**3.2. Linking hydrological and geochemical processes in evolving landscapes**

coupled hydrological-biogeochemical modeling.

58 Hydrology of Artificial and Controlled Experiments

The nature of hydrological and geochemical interactions is a primary determinant of hillslope structure formation, available water quantity, and water quality along the entire evolutionary trajectory of a landscape―from pristine abiotic substrate subjected to a first rain pulse, when microbial (Section 3.3) and vascular plant (Section 3.4) life only begin to establish, to a matured landscape adjusting to variable climatic forcing. Examining and predicting the time evolution of subsurface structure through biogeochemical processes and its effect on hydrological partitioning and water residence time are therefore a key focus of the research at LEO. The LEO experiment provides unprecedented capability to examine the complex interactions that are integral to the hillslope evolution and soil formation processes because of the high density of hydrological and geochemical measurements in space and time, control of some of the drivers of weathering such as rainfall or temperature, and the ability to perform

In any developing landscape, including the LEO landscapes, the spatial structure of flow pathways along hillslopes determines the rate, extent, and distribution of geochemical reactions (and biological colonization) that drive weathering, the transport and precipitation of solutes and sediments, and the evolution of soil structure. The resulting structure and process evolution, in turn, induces spatiotemporal variability of hydrological states and flow pathways. Weathering reactions are strongly influenced by the time water spends along the subsurface flow paths [92, 93] at any stage of the landscape development. Dissolution of primary silicates is kinetically limited, and increasing residence time, and thus the duration of contact time of water with rocks, therefore potentially increases the concentration of lithogenic elements in the soil solution. The resulting relative saturation of soil solution (as measured by the saturation index) with respect to soil minerals will affect both primary mineral dissolution and secondary mineral precipitation [94]. The farther a solution is from equilibrium, the higher the rates of both dissolution and precipitation processes. In addition to water residence times, rock dissolution rates are also influenced by formation of secondary minerals [95]. Plants and microorganisms can also strongly influence weathering rates through production of organic acids and other complexing agents [72, 96], through respiration and uptake of water and dissolving

nutrients [97], and through enzymatic promotion of bicarbonate formation from CO<sup>2</sup>

[98, 99].

Precipitation of secondary minerals during incongruent weathering decreases the particle size of the soil, which affects its pore size distribution and hydraulic conductivity, and consequently the water transit times [100, 101]. Mineral precipitation can also feedback to further weathering [102, 103], nutrient and carbon retention, and microbial and plant distribution. Coupled geochemical-hydrological modeling (combined with pedotransfer functions) was performed for the LEO hillslopes to estimate mineral transformations and changes in soil hydraulic properties over a 10-year period with rainfall amount derived from several real-world locations in the southwestern United States [8]. The predictions suggested a significant increase in the fraction of secondary minerals and, as a result, a decrease in hydraulic conductivity over time. Predicted changes were highly variable in space, closely mirroring flow and saturation patterns on the hillslopes. It was further observed that solution-phase concentrations of lithogenic elements calculated by the models could provide early indication of the soil formation processes, even before changes in the solid phase would have become readily measurable. Since then, the physical experiments performed at LEO [54, 55] confirm both development of heterogeneity in the solution composition as a function of flow patterns and spatially resolved precipitation of secondary minerals as indicated by saturation indices for a number of minerals that would influence soil structure and flow patterns. For example, it can be seen for calcite (**Figure 14**) that there is pronounced spatial heterogeneity in the measurements linked to soil water content, and that there is a temporal progression in the saturation indices as the hillslope gets drier. Results from LEO soil cores further support spatially resolved precipitation of inorganic carbon through direct measurements (Section 2.5).

The geochemical evolution of a hillslope is also strongly linked to runoff generation. The fraction of incoming rainfall that eventually leaves the landscapes as seepage outflow (or overland flow, if any) affects the export of inorganic and organic compounds from the landscapes. This export process presents an integral part of soil formation. When soils age, their composition shifts toward elements such as iron and aluminum that form poorly soluble oxides and hydroxides, as these are retained. More soluble elements in turn are lost, such as silicon, which presents an originally predominant element on the landscape. Seepage generation additionally has significant implications for the carbon balance in the environment. The export of inorganic carbon that was previously captured in the landscape as a result of weathering (predominantly as bicarbonate and carbonate), and its transport and further storage in the oceans, serve as important mechanisms of carbon sequestration from the atmosphere. Weathering of basaltic rocks, such as those comprising the LEO landscapes, occurs rapidly [104], and our experiments showed that as much as 5 kg of carbon was lost from the hillslopes in a single rain event (see **Figure 8**; [54]). Finally, feedbacks between water flow, mineral weathering and soil formation, and biological activity can have important implications for water quality. These feedbacks control the off-site transport not only of lithogenic and biogenic compounds but also of potential anthropogenic and possibly hazardous compounds into streams and other downstream landscape elements.

and geochemical signatures. Hydrological flux processes themselves (e.g., infiltration, lateral redistribution of vadose zone water and groundwater, and evapotranspiration) and resulting soil moisture spatial and temporal dynamics are primarily controlled by structural properties of the subsurface and the driving forces of surface exchange. Hydrological dynamics, in turn, impact the time spent by a parcel of water in contact with microbial cells, the dissolution and flow of nutrients, and the movement of microbes in the system [107]. Additionally, hydrogeochemical processes, such as weathering and dissolution of primary minerals, dissolution of reactive phases, and reprecipitation of weathered elements, influence nutrient availability and may facilitate microbial colonization [108]. The concurrent microbial signatures of growth and function impact pore structure, weathering rate, and labile carbon deposition and thereby establish feedbacks of coupled hydro-biogeochemical processes influencing soil formation. Microbial life is therefore both a follower and facilitator of water flow paths in

Controlled Experiments of Hillslope Coevolution at the Biosphere 2 Landscape Evolution…

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The heavily instrumented LEO landscapes, with the dense network of sensors and samplers and diverse modeling approaches, provide the opportunity to develop a mechanistic understanding of integrated hydrological and microbial processes occurring in the hillslopes as a result of their mutual interactions and to identify signatures that can be used to quantitatively characterize hydrology-microbiology relationships. For instance, it is important to understand how microbial cells and the biomolecules produced by the microbes are transported with the water. The time spent by a microbial community in a unique microenvironment impacts species-species interactions and accessibility of nutrients. Water movement also impacts the turnover of microbial community assemblies and has been shown to influence microbial ecological assembly processes [109]. However, many open questions remain regarding the nature of these interactions between hydrology and microbiology. Using **Figure 11** as an example, an unexpected trend was observed in the relative microbial abundances in the miniLEO smallscale model. While *Actinobacteria* were predominantly abundant at the surface, *Cyanobacteria* were found at greater depths in the model system. *Cyanobacteria* are photoautotrophs, i.e., they need sunlight to survive, and yet they occurred preferentially in deep and dark layers of the soil (highest abundance at 40 cm below the surface). In contrast, *Actinobacteria* were consistent in their localization toward the surface despite the potential for transport with water from the surface to deeper layers. Hypothesizing that water flow caused *Cyanobacteria* to travel deeper fails to explain why *Actinobacteria* maintained a constant relative abundance at the surface. So, are microbial species transported passively with the water depending on specific parameters such as cell size, or do they differ in their active movement (i.e., motility) and surface attachment (e.g., in biofilms)? Or are the relative abundance patterns simply imposed by the local environment and maintained by constant cell turnover rates, despite water flow? Tackling these questions requires combining hydrological tools with microbiological methods. For example, it will be relevant to incorporate microbial processes into reactive transport models and to evaluate average water volumes, transit times, and upstream flow path lengths associated with the microbial abundance patterns (e.g., for each voxel in the example **Figure 11**). Such distributed indices may facilitate the interfacing of hydrology and

incipient landscape systems.

microbiology to study subsurface ecosystems.

**Figure 14.** Three-dimensional interpolations of volumetric soil water content (SWC; left) and calcite saturation index (right) projected onto a LEO hillslope model at two depth intervals (0.20–0.50 m and 85–1.00 m) and two sampling dates (DOY 52 and DOY 59). For SWC: warm colors represent areas of relatively dry conditions while cool colors represent wet conditions. For calcite saturation index: warm colors represent areas of supersaturation while cool colors represent areas of reduced supersaturation or undersaturation. Note the unique scales for SWC (%) and saturation index (i.e., log *Q*/*K*).

#### **3.3. Linking hydrological and microbial processes in evolving landscapes**

Microorganisms typically represent the life pioneering initially abiotic, developing hillslopes. Microbial populations affect the trajectory and speed of evolution of physicochemical landscape properties and functions and may be critical in facilitating the establishment of vascular plant life (Section 3.4). Yet, we lack a clear picture of the manifold feedbacks between evolving patterns of microbial (and plant) ecology and physicochemical system dynamics across scales. In particular, our understanding of early-stage oligotrophic landscape elements is limited, as we rarely have the opportunity for their study in nature (but see related work by, e.g., [105, 106]) and typically lack the multifaceted observational density required. Microbial dynamics across incipient landscapes, such as the LEO hillslopes, are tightly linked to hydrological and geochemical signatures. Hydrological flux processes themselves (e.g., infiltration, lateral redistribution of vadose zone water and groundwater, and evapotranspiration) and resulting soil moisture spatial and temporal dynamics are primarily controlled by structural properties of the subsurface and the driving forces of surface exchange. Hydrological dynamics, in turn, impact the time spent by a parcel of water in contact with microbial cells, the dissolution and flow of nutrients, and the movement of microbes in the system [107]. Additionally, hydrogeochemical processes, such as weathering and dissolution of primary minerals, dissolution of reactive phases, and reprecipitation of weathered elements, influence nutrient availability and may facilitate microbial colonization [108]. The concurrent microbial signatures of growth and function impact pore structure, weathering rate, and labile carbon deposition and thereby establish feedbacks of coupled hydro-biogeochemical processes influencing soil formation. Microbial life is therefore both a follower and facilitator of water flow paths in incipient landscape systems.

The heavily instrumented LEO landscapes, with the dense network of sensors and samplers and diverse modeling approaches, provide the opportunity to develop a mechanistic understanding of integrated hydrological and microbial processes occurring in the hillslopes as a result of their mutual interactions and to identify signatures that can be used to quantitatively characterize hydrology-microbiology relationships. For instance, it is important to understand how microbial cells and the biomolecules produced by the microbes are transported with the water. The time spent by a microbial community in a unique microenvironment impacts species-species interactions and accessibility of nutrients. Water movement also impacts the turnover of microbial community assemblies and has been shown to influence microbial ecological assembly processes [109]. However, many open questions remain regarding the nature of these interactions between hydrology and microbiology. Using **Figure 11** as an example, an unexpected trend was observed in the relative microbial abundances in the miniLEO smallscale model. While *Actinobacteria* were predominantly abundant at the surface, *Cyanobacteria* were found at greater depths in the model system. *Cyanobacteria* are photoautotrophs, i.e., they need sunlight to survive, and yet they occurred preferentially in deep and dark layers of the soil (highest abundance at 40 cm below the surface). In contrast, *Actinobacteria* were consistent in their localization toward the surface despite the potential for transport with water from the surface to deeper layers. Hypothesizing that water flow caused *Cyanobacteria* to travel deeper fails to explain why *Actinobacteria* maintained a constant relative abundance at the surface. So, are microbial species transported passively with the water depending on specific parameters such as cell size, or do they differ in their active movement (i.e., motility) and surface attachment (e.g., in biofilms)? Or are the relative abundance patterns simply imposed by the local environment and maintained by constant cell turnover rates, despite water flow? Tackling these questions requires combining hydrological tools with microbiological methods. For example, it will be relevant to incorporate microbial processes into reactive transport models and to evaluate average water volumes, transit times, and upstream flow path lengths associated with the microbial abundance patterns (e.g., for each voxel in the example **Figure 11**). Such distributed indices may facilitate the interfacing of hydrology and microbiology to study subsurface ecosystems.

**3.3. Linking hydrological and microbial processes in evolving landscapes**

index (i.e., log *Q*/*K*).

60 Hydrology of Artificial and Controlled Experiments

Microorganisms typically represent the life pioneering initially abiotic, developing hillslopes. Microbial populations affect the trajectory and speed of evolution of physicochemical landscape properties and functions and may be critical in facilitating the establishment of vascular plant life (Section 3.4). Yet, we lack a clear picture of the manifold feedbacks between evolving patterns of microbial (and plant) ecology and physicochemical system dynamics across scales. In particular, our understanding of early-stage oligotrophic landscape elements is limited, as we rarely have the opportunity for their study in nature (but see related work by, e.g., [105, 106]) and typically lack the multifaceted observational density required. Microbial dynamics across incipient landscapes, such as the LEO hillslopes, are tightly linked to hydrological

**Figure 14.** Three-dimensional interpolations of volumetric soil water content (SWC; left) and calcite saturation index (right) projected onto a LEO hillslope model at two depth intervals (0.20–0.50 m and 85–1.00 m) and two sampling dates (DOY 52 and DOY 59). For SWC: warm colors represent areas of relatively dry conditions while cool colors represent wet conditions. For calcite saturation index: warm colors represent areas of supersaturation while cool colors represent areas of reduced supersaturation or undersaturation. Note the unique scales for SWC (%) and saturation

#### **3.4. Linking hydrological and plant ecological processes in evolving landscapes**

LEO is ideally suited to investigate the coevolution of water flow paths and plant ecological processes, soil properties, and microbial dynamics. After an initial period of bare soil conditions, seeds will be dispersed on the hillslope surfaces and vascular plant growth will strongly modify the surface and subsurface properties of the LEO landscapes. Differences in available water and nutrients in the subsurface, resulting from the coevolution of hydrological and biogeochemical processes prior to vegetation, will create niches that different plant species can occupy. These adaptation and selection mechanisms will result in whole-ecosystem dynamics that are different from a uniform distribution of plant species across the slopes. The aboveground and belowground instrumentation at LEO will allow the detailed monitoring of water, carbon, and energy fluxes at the land-atmosphere interface and throughout the hillslopes. Understanding these connections between the physical environment and germination and survival rates of various species will yield important information on characteristics of plant establishment. Following these patterns through time will allow for insights into those key processes of coevolution of plant-soil dynamics in the

Controlled Experiments of Hillslope Coevolution at the Biosphere 2 Landscape Evolution…

http://dx.doi.org/10.5772/intechopen.72325

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This chapter has presented the research infrastructure, facilities, and initial experimental results of the Landscape Evolution Observatory (LEO) project at the Biosphere 2. LEO is a carefully designed and massively outfitted macrocosm experiment of an unprecedented scale and ambition of scope. Each of the three model landscapes emulates a pristine, sloping zero-order basin consisting of more than 500 metric tons of homogenous basaltic tephra housed within climate-controlled bays. The infrastructure operates dense arrays of more than 1900 sensors and samplers per landscape that are complemented by state-of-the-art research support systems, including an isotope and trace gas analysis network, electrical resistivity tomography instrumentation, high-resolution remote imaging systems, and advanced analytical capabilities for analyzing liquids and solids. The combined capacity of these structures allows tracking states, fluxes, and pathways of water, energy, and critical elements such as carbon at sub-meter to landscape scales, arguably representing the most successful attempt to closing hydro-biogeochemical budgets for a hillslope-size system to date. The system importantly allows key developmental processes to be rigorously tracked, including changes in subsurface and the development of microbial and ultimately vascular

LEO is fully operational and has recently (October 2016) entered its 10-year long institutional experiment. During this experiment, variable climate forcing (mainly rainfall treatments) will drive the initially simple, abiotic model landscapes into life-sustaining ecosystems. After a first phase of bare soil surface conditions that is scheduled to last approximately 2 to 3 years, the landscapes will be colonized by vascular plants able to germinate and grow on the poorly developed LEO soil. While the landscapes evolve to increasingly complex states, Earth scientists will have the opportunity to iteratively build knowledge on the interactions

profile and planform.

plant communities.

**4. Conclusions and outlook**

The spatiotemporal interplay between water and vegetation dynamics, and their feedbacks to physicochemical landscape (Section 3.2) and microbial community (Section 3.3) organization, profoundly impacts the evolution of a hillslope and its cycling of mass and energy. Hydrological processes exert primary controls on the establishment, distribution, structure, and function of ecosystems, while biotic processes directly (e.g., through transpiration) and indirectly (e.g., through alteration of soil properties) affect the cycling of water through the landscape [110, 111]. A substantial body of ecohydrological research has examined plantwater interactions with respect to one-dimensional (vertical) water and nutrient fluxes at the patch scale [112], however, without incorporating lateral redistribution of water and nutrients imposed by hillslope morphology. Similarly, biogeography has been mindful of spatial patterns of drivers of vegetation distribution but has paid little attention to the inherent coevolution of the physical and biological realms as a process that drives the form and function of the biological and physical landscape. Due to their large scale and imposed gradients in topography and environmental conditions, as well as their climate control and monitoring capabilities, the LEO hillslopes provide the unique opportunity to tackle the gaps in our understanding of how physical-biological interactions drive landscape evolution in space and time. Working at the hillslope scale forces integration between one- and two-dimensional conceptualizations of ecohydrological processes and provides the topological structures that connect patches on the landscape by gravitational fluxes organized by hillslope morphology. From the patch or pedon perspective, the hillslope provides nonlocal controls on water and nutrient fluxes, while local controls at the patch scale influence downslope patches in the ecohydrological system.

The basic elements that define hillslope morphology, such as shape, gradient, aspect, and slope complexity (i.e., nonuniformity), affect water and energy availability (e.g., [3]). Hillslope shape expresses the convergence or divergence of surface flow paths in the planform (across slope) and profile (normal to slope) directions and therefore relates directly to soil moisture redistribution. Hillslope shape and gradient both affect runoff processes and erosion rates. Hillslope aspect directly influences irradiance and hence energy availability for evapotranspiration. Complex interactions at the hillslope scale between topography, soil development, runoff processes, and vegetation create self-reinforcing positive feedbacks in ecohydrological processes that must be considered to develop a comprehensive understanding of ecohydrological patterns and processes.

The effects of vegetation on physical processes will depend on the structure of the community [25]. One might hypothesize that shallow-rooted plants would have a much less significant impact on the ecohydrology of a hillslope than a deep-rooted shrub because they lack a physical integration with as much of the soil profile. But are ecohydrological process more influenced by percent cover of the soil (presumably higher in a lower-stature forb or grassland system), higher photosynthetic function that derives organic acids that can drive soil processes, or simply the water use efficiency of the vegetation, regardless of type? Abovegroundbelowground linkages are so inherently complex that when we add discussion of connections among soil pedons in space or consider the various members of a vegetative community, how little we know about the ecohydrology at the hillslope scale becomes glaringly apparent.

LEO is ideally suited to investigate the coevolution of water flow paths and plant ecological processes, soil properties, and microbial dynamics. After an initial period of bare soil conditions, seeds will be dispersed on the hillslope surfaces and vascular plant growth will strongly modify the surface and subsurface properties of the LEO landscapes. Differences in available water and nutrients in the subsurface, resulting from the coevolution of hydrological and biogeochemical processes prior to vegetation, will create niches that different plant species can occupy. These adaptation and selection mechanisms will result in whole-ecosystem dynamics that are different from a uniform distribution of plant species across the slopes. The aboveground and belowground instrumentation at LEO will allow the detailed monitoring of water, carbon, and energy fluxes at the land-atmosphere interface and throughout the hillslopes. Understanding these connections between the physical environment and germination and survival rates of various species will yield important information on characteristics of plant establishment. Following these patterns through time will allow for insights into those key processes of coevolution of plant-soil dynamics in the profile and planform.
