Mark S. Bartlett

Coupled carbon and water fluxes in CAM photosynthesis: modeling quantification of water use efficiency and productivity.

Background and AimsDue to their high water use efficiency, Crassulacean acid metabolism (CAM) plants are of environmental and economic importance in the arid and semiarid regions of the world. Moreover, many CAM plants (e.g., Agave tequilana) have attractive qualities for biofuel production such as a relatively low lignin content and high amount of soluble carbohydrates. However, the current estimates of CAM productivity are based on empirical stress indices that create large uncertainties. As a first step towards a more accurate quantification of CAM productivity, this paper introduces a new model that couples both soil and atmosphere conditions to CAM photosynthesis.MethodsThe new CAM model is based upon well established C3 photosynthesis models coupled to a nonlinear circadian rhythm oscillator for the control of the photosynthesis carbon fluxes. The leaf-level dynamics are coupled to a simple, yet realistic description of the soil-plant-atmosphere continuum, including a plant water capacitance module.ResultsThe resulting model reproduces the four phases of CAM photosynthes is and the evolution of their dynamics during a soil moisture drydown, as a function of soil type, plant features, and climatic conditions.ConclusionThe results help quantify the impact of soil water availability on CAM carbon assimilation and transpiration flux.

Beyond the SCS‐CN method: A theoretical framework for spatially lumped rainfall‐runoff response

Acknowledgments This work was supported through the USDA Agricultural Research Service cooperative agreement 58-6408-3-027; and National Science Foundation (NSF) grants CBET-1033467, EAR-1331846, FESD-1338694, EAR-1316258, and the Duke WISeNet grant DGE-1068871. The data used for Figure 9 are reproduced from Tedela et al. [2011, 2008]. Processed data and code are available by e-mail from the corresponding author. We thank the reviewers for their useful and constructive comments that helped improve the paper.

Stochastic rainfall-runoff model with explicit soil moisture dynamics

Stream runoff is perhaps the most poorly represented process in ecohydrological stochastic soil moisture models. Here we present a rainfall-runoff model with a new stochastic description of runoff linked to soil moisture dynamics. We describe the rainfall-runoff system as the joint probability density function (PDF) of rainfall, soil moisture and runoff forced by random, instantaneous jumps of rainfall. We develop a master equation for the soil moisture PDF that accounts explicitly for a general state-dependent rainfall-runoff transformation. This framework is then used to derive the joint rainfall-runoff and soil moisture-runoff PDFs. Runoff is initiated by a soil moisture threshold and a linear progressive partitioning of rainfall based on the soil moisture status. We explore the dependence of the PDFs on the rainfall occurrence PDF (homogeneous or state-dependent Poisson process) and the rainfall magnitude PDF (exponential or mixed-exponential distribution). We calibrate the model to 63 years of rainfall and runoff data from the Upper Little Tennessee watershed (USA) and show how the new model can reproduce the measured runoff PDF.

Nonlinear dynamics of the CAM circadian rhythm in response to environmental forcing

Crassulacean acid metabolism (CAM) photosynthesis functions as an endogenous circadian rhythm coupled to external environmental forcings of energy and water availability. This paper explores the nonlinear dynamics of a new CAM photosynthesis model (Bartlett et al., 2014) and investigates the responses of CAM plant carbon assimilation to different combinations of environmental conditions. The CAM model (Bartlett et al., 2014) consists of a Calvin cycle typical of C3 plants coupled to an oscillator of the type employed in the Van der Pol and FitzHugh-Nagumo systems. This coupled system is a function of environmental variables including leaf temperature, leaf moisture potential, and irradiance. Here, we explore the qualitative response of the system and the expected carbon assimilation under constant and periodically forced environmental conditions. The model results show how the diurnal evolution of these variables entrains the CAM cycle with prevailing environmental conditions. While constant environmental conditions generate either steady-state or periodically oscillating responses in malic acid uptake and release, forcing the CAM system with periodic daily fluctuations in light exposure and leaf temperature results in quasi-periodicity and possible chaos for certain ranges of these variables. This analysis is a first step in quantifying changes in CAM plant productivity with variables such as the mean temperature, daily temperature range, irradiance, and leaf moisture potential. Results may also be used to inform model parametrization based on the observed fluctuating regime.

Framework for event‐based semidistributed modeling that unifies the SCS‐CN method, VIC, PDM, and TOPMODEL

Hydrologists and engineers may choose from a range of semidistributed rainfall-runoff models such as VIC, PDM, and TOPMODEL, all of which predict runoff from a distribution of watershed properties. However, these models are not easily compared to event-based data and are missing ready-to-use analytical expressions that are analogous to the SCS-CN method. The SCS-CN method is an event-based model that describes the runoff response with a rainfall-runoff curve that is a function of the cumulative storm rainfall and antecedent wetness condition. Here, we develop an event-based probabilistic storage framework and distill semidistributed models into analytical, event-based expressions for describing the rainfall-runoff response. The event-based versions called VICx, PDMx, and TOPMODELx also are extended with a spatial description of the runoff concept of “pre-threshold” and “threshold-excess” runoff, which occur, respectively, before and after infiltration exceeds a storage capacity threshold. For total storm rainfall and antecedent wetness conditions, the resulting ready-to-use analytical expressions define the source areas (fraction of the watershed) that produce runoff by each mechanism. They also define the probability density function (PDF) representing the spatial variability of runoff depths that are cumulative values for the storm duration, and the average unit area runoff, which describes the so-called runoff curve. These new event-based semidistributed models and the traditional SCS-CN method are unified by the same general expression for the runoff curve. Since the general runoff curve may incorporate different model distributions, it may ease the way for relating such distributions to land use, climate, topography, ecology, geology, and other characteristics. This article is protected by copyright. All rights reserved.

The role of plant water storage and hydraulic strategies in relation to soil moisture availability

Background and AimsPlants rely on water storage capacity to increase accessibility of water for transpiration, reduce competition for water with neighboring plants, and buffer water supply during dry periods. The resulting benefits, typically a decrease in plant water stress and increase in productivity, are highly climate dependent and vary with soil moisture, vapor pressure deficit, and solar radiation. This paper analyzes the effects of plant water storage capacity on the relationship between soil moisture and carbon assimilation in woody plants.MethodsA resistance-capacitance model is used to examine the role of plant water storage at various soil moisture levels. Hydraulic traits are co-varied according to empirical relationships, and effects of sapwood volume and wood density on carbon assimilation are explored. The time scales of plant water storage and withdrawal are analyzed as a function of plant hydraulic capacitance, water storage capacity, and resistance to transport between water storage tissue and xylem.ResultsThe effects of plant water storage on carbon assimilation are found to depend strongly on soil moisture levels. The theoretically optimal sapwood volume lies near naturally occurring ranges and increases with increasing soil moisture. The theoretically optimal wood density also lies within expected ranges and decreases with increasing soil moisture.ConclusionsA large portion of sapwood volume appears to be justified by its role in buffering diurnal variability in evaporative demand. The outlined coordination between soil moisture and optimal hydraulic traits is consistent with observed increases in sapwood capacitance and decreases in wood density across increasing rainfall gradients. This coordination provides support for the drought-tolerance vs. drought-avoidance hypothesis.

Reply to Comment by Fred L. Ogden et al. on "Beyond the SCS-CN Method: A Theoretical Framework for Spatially Lumped Rainfall-Runoff Response"

Though Ogden et al. list several shortcomings of the original SCS-CN method, fit for purpose is a key consideration in hydrological modelling, as shown by the adoption of SCS-CN method in many design standards. The theoretical framework of Bartlett et al. [2016a] reveals a family of semidistributed models, of which the SCS-CN method is just one member. Other members include event-based versions of the Variable Infiltration Capacity (VIC) model and TOPMODEL. This general model allows us to move beyond the limitations of the original SCS-CN method under different rainfall-runoff mechanisms and distributions for soil and rainfall variability. Future research should link this general model approach to different hydrogeographic settings, in line with the call for action proposed by Ogden et al.

Similarities in the evolution of plants and cars

While one system is animate and the other inanimate, both plants and cars are powered by a highly successful process which has evolved in a changing environment. Each process (the photosynthetic pathway and the car engine, respectively) originated from a basic scheme and evolved greater efficiency by adding components to the existing structure, which has remained largely unchanged. Here we present a comparative analysis of two variants on the original C3 photosynthetic pathway (C4 and CAM) and two variants on the internal combustion engine (the turbocharger and the hybrid electric vehicle). We compare the timeline of evolution, the interaction between system components, and the effects of environmental conditions on both systems. This analysis reveals striking similarities in the development of these processes, providing insight as to how complex systems—both natural and built—evolve and adapt to changing environmental conditions in a modular fashion.

Unified representation of the C3, C4, and CAM photosynthetic pathways with the Photo3 model

Recent interest in crassulacean acid metabolism (CAM) photosynthesis has resulted in new, physiologically based CAM models. These models show promise, yet typically are not developed with a basis that is compatible with widely used models of C3 and C4 photosynthesis. Indeed, most efforts to assess the potential of CAM still rely on empirically based environmental productivity indices, which makes uniform comparisons between CAM and non-CAM species difficult. In order to represent C3, C4, and CAM photosynthesis in a consistent, physiologically based manner, we introduce the Photo3 model. Photo3 unites a common photosynthetic and hydraulic core with components depicting the circadian rhythm of CAM photosynthesis and the carbon-concentrating mechanism of C4 photosynthesis. This work allows consistent comparisons of the three photosynthetic types for the first time. It also allows the representation of intermediate C3-CAM behavior through the adjustment of a single model parameter. Model simulations of Opuntia ficus-indica (CAM), Sorghum bicolor (C4), and Triticum aestivum (C3) capture the diurnal behavior of each species as well as the cumulative effects of long-term water limitation. These results show the models potential for evaluating the tradeoffs between C3, C4, and CAM photosynthesis, and for better understanding CAM productivity, ecology, and climate feedbacks.