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Interception is the storage and subsequent evaporation of rainfall by above-ground structures, including canopy and groundcover vegetation and surface litter. Accurately quantifying interception is critical for understanding how ecosystems partition incoming precipitation, but it is difficult and costly to measure, leading most studies to rely on modeled interception estimates. Moreover, forest interception estimates typically focus only on canopy storage, despite the potential for substantial interception by groundcover vegetation and surface litter. In this study, we developed an approach to quantify “total” interception (i.e., including forest canopy, understory, and surface litter layers) using measurements of shallow soil moisture dynamics during rainfall events. Across 34 pine and mixed forest stands in Florida (USA), we used soil moisture and precipitation (P) data to estimate interception storage capacity (βs), a parameter required to estimate total annual interception (Ia) relative to P. Estimated values for βs(mean βs=0.30 cm; 0.01≤βs≤0.62 cm) and Ia∕P (mean Ia/P=0.14; 0.06≤Ia/P≤0.21) were broadly consistent with reported literature values for these ecosystems and were significantly predicted by forest structural attributes (leaf area index and percent ground cover) as well as other site variables (e.g., water table depth). The best-fit model was dominated by LAI and explained nearly 80 % of observed βs variation. These results suggest that whole-forest interception can be estimated using near-surface soil moisture time series, though additional direct comparisons would further support this assertion. Additionally, variability in interception across a single forest type underscores the need for expanded empirical measurement. Potential cost savings and logistical advantages of this proposed method relative to conventional, labor-intensive interception measurements may improve empirical estimation of this critical water budget element.

Human alterations to hydrology have globally impacted wetland ecosystems. Preventing or reversing these impacts is a principal focus of restoration efforts. However, restoration effectiveness is often hampered by limited information on historical landscape properties and hydrologic regime. To help address this gap, we developed a novel statistical approach for inferring flows and inundation frequency (i.e., hydroperiod, HP) in wetlands where changes in spatial vegetation and geomorphic patterns have occurred due to hydrologic alteration. We developed an analytical expression for HP as a transformation of the landscape-scale stage-discharge relationship. We applied this model to the Everglades “ridge-slough” (RS) landscape, a patterned, lotic peatland in southern Florida that has been drastically degraded by compartmentalization, drainage and flow diversions. The new method reliably estimated flow and HP for a range of RS landscape patterns. Crucially, ridge-patch anisotropy and elevation above sloughs were strong drivers of flow-HP relationships. Increasing ridge heights markedly increased flow required to achieve sufficient HP to support peat accretion. Indeed, ridge heights inferred from historical accounts would require boundary flows three to four times greater than today, which agrees with restoration flow estimates from more complex, spatially distributed models. While observed loss of patch anisotropy allows HP targets to be met with lower flows, such landscapes likely fail to support other ecological functions. This work helps inform restoration flows required to restore stable ridge-slough patterning and positive peat accretion in this degraded ecosystem, and, more broadly, provides tools for exploring interactions between landscape and hydrology in lotic wetlands and floodplains.

Interception is the storage and subsequent evaporation of rainfall by above-ground structures, including canopy and groundcover vegetation and surface litter. Accurately quantifying interception is critical for understanding how ecosystems partition incoming precipitation, but it is difficult and costly to measure, leading most studies to rely on modeled interception estimates. Moreover, forest interception estimates typically focus only on canopy storage, despite the potential for substantial interception by groundcover vegetation and surface litter. In this study, we developed an approach to quantify total interception losses (i.e., including forest canopy, understory, and surface litter layers) using measurements of shallow soil moisture dynamics during rainfall events. Across 36 pine and mixed forest stands in Florida (USA), we used soil moisture and rainfall data to estimate the interception storage capacity (βs), a parameter required to estimate total annual interception losses (Ia) relative to rainfall (R). Estimated values for βs (mean βs = 0.30 cm; 0.01 ≤ βs ≤ 0.62 cm) and Ia/R (mean Ia/R = 0.14; 0.06 ≤ Ia/R ≤ 0.21) were consistent with reported literature values for these ecosystems and were significantly predicted by forest structural attributes (leaf area index and percent groundcover), as well as other site variables (e.g., water table depth). The best-fit model was dominated by LAI and explained nearly 80 % of observed βs variation. These results suggest that whole-forest interception can be measured using a single near-surface soil moisture time series and highlight the variability in interception losses across a single forest type, underscoring the need for expanded empirical measurement. Potential cost savings and logistical advantages of this method relative to conventional, labor-intensive interception measurements may improve empirical estimation of this critical water budget element.

Parameter estimation is a critical step in successful application of dynamic crop models to simulate crop growth and yield under various climatic and management scenarios. Although inverse modeling parameterization techniques significantly improve the predictive capabilities of models, whether these approaches can recover the true parameter values of a specific genotype or cultivar is seldom investigated. In this study, we applied a Markov Chain Monte-Carlo (MCMC) method to the DSSAT dry bean model to estimate (recover) the genotype-specific parameters (GSPs) of 150 synthetic recombinant inbred lines (RILs) of dry bean. The synthetic parents of the population were assigned contrasting GSP values obtained from a database, and each of these GSPs was associated with several quantitative trait loci. A standard inverse modeling approach that simultaneously estimated all GSPs generated a set of values that could reproduce the original synthetic observations, but many of the estimated GSP values significantly differed from the original values. However, when parameter estimation was carried out sequentially in a stepwise manner, according to the genetically controlled plant development process, most of the estimated parameters had values similar to the original values. Developmental parameters were more accurately estimated than those related to dry mass accumulation. This new approach appears to reduce the problem of equifinality in parameter estimation, and it is especially relevant if attempts are made to relate parameter values to individual genes. Keywords: Crop models, Equifinality, Genotype-specific parameters, Markov chain Monte-Carlo, Parameterization.

A century of hydrologic modification has altered the physical and biological drivers of landscape processes in the Everglades (Florida, USA). Restoring the ridge–slough patterned landscape, a dominant feature of the historical system, is a priority but requires an understanding of pattern genesis and degradation mechanisms. Physical experiments to evaluate alternative pattern formation mechanisms are limited by the long timescales of peat accumulation and loss, necessitating model-based comparisons, where support for a particular mechanism is based on model replication of extant patterning and trajectories of degradation. However, multiple mechanisms yield a central feature of ridge–slough patterning (patch elongation in the direction of historical flow), limiting the utility of that characteristic for discriminating among alternatives. Using data from vegetation maps, we investigated the statistical features of ridge–slough spatial patterning (ridge density, patch perimeter, elongation, patch size distributions, and spatial periodicity) to establish more rigorous criteria for evaluating model performance and to inform controls on pattern variation across the contemporary system. Mean water depth explained significant variation in ridge density, total perimeter, and length : width ratios, illustrating an important pattern response to existing hydrologic gradients. Two independent analyses (2-D periodograms and patch size distributions) provide strong evidence against regular patterning, with the landscape exhibiting neither a characteristic wavelength nor a characteristic patch size, both of which are expected under conditions that produce regular patterns. Rather, landscape properties suggest robust scale-free patterning, indicating genesis from the coupled effects of local facilitation and a global negative feedback operating uniformly at the landscape scale. Critically, this challenges widespread invocation of scale-dependent negative feedbacks for explaining ridge–slough pattern origins. These results help discern among genesis mechanisms and provide an improved statistical description of the landscape that can be used to compare among model outputs, as well as to assess the success of future restoration projects.

Self-organized landscape patterning can arise in response to multiple processes. Discriminating among alternative patterning mechanisms, particularly where experimental manipulations are untenable, requires process-based models. Previous modeling studies have attributed patterning in the Everglades (Florida, USA) to sediment redistribution and anisotropic soil hydraulic properties. In this work, we tested an alternate theory, the self-organizing-canal (SOC) hypothesis, by developing a cellular automata model that simulates pattern evolution via local positive feedbacks (i.e., facilitation) coupled with a global negative feedback based on hydrology. The model is forced by global hydroperiod that drives stochastic transitions between two patch types: ridge (higher elevation) and slough (lower elevation). We evaluated model performance using multiple criteria based on six statistical and geostatistical properties observed in reference portions of the Everglades landscape: patch density, patch anisotropy, semivariogram ranges, power-law scaling of ridge areas, perimeter area fractal dimension, and characteristic pattern wavelength. Model results showed strong statistical agreement with reference landscapes, but only when anisotropically acting local facilitation was coupled with hydrologic global feedback, for which several plausible mechanisms exist. Critically, the model correctly generated fractal landscapes that had no characteristic pattern wavelength, supporting the invocation of global rather than scale-specific negative feedbacks.

Strong reciprocal interactions exist between landscape patterns and ecological processes. In wetlands, hydrology is the dominant abiotic driver of ecological processes and both controls, and is controlled, by vegetation presence and patterning. We focus on binary patterning in the Everglades ridge-slough landscape, where longitudinally connected flow, principally in sloughs, is integral to landscape function. Patterning controls discharge competence in this low-gradient peatland, with important feedbacks on hydroperiod and thus peat accretion and patch transitions. To quantitatively predict pattern effects on hydrologic connectivity and thus hydroperiod. We evaluated three pattern metrics that vary in their hydrologic specificity. (1) Landscape discharge competence considers elongation and patch-type density that capture geostatistical landscape features. (2) Directional connectivity index (DCI) extracts both flow path and direction based on graph theory. (3) Least flow cost (LFC) is based on a global spatial distance algorithm strongly analogous to landscape water routing, where ridges have higher flow cost than sloughs because of their elevation and vegetation structure. Metrics were evaluated in comparison to hydroperiod estimated using a numerically intensive hydrologic model for synthetic landscapes. Fitted relationships between metrics and hydroperiod for synthetic landscapes were extrapolated to contemporary and historical maps to explore hydroperiod trends in space and time. Both LFC and DCI were excellent predictors of hydroperiod and useful for diagnosing how the modern landscape has reorganized in response to modified hydrology. Metric simplicity and performance indicates potential to provide hydrologically explicit, computationally simple, and spatially independent predictions of landscape hydrology, and thus effectively measure of restoration performance.

We develop conceptual and numerical models of water table dynamics under a subsurface irrigation and drainage system. The numerical model is implemented with distinct drainable and fillable porosity parameters that are estimated by accounting for the unsaturated zone fluxes to and from the shallow water table. The model was applied to two field sites under subsurface irrigation and drainage system in northeast Florida to simulate water table dynamics during potato growing seasons in 2010 and 2011. Simulated water table elevations showed a close agreement with the observed water table dynamics in the fields during both growing seasons. Furrows that act as shallow drains in the field facilitated rapid drawdown of the water table after rainfall events, while the outer, deeper ditches provided little drainage of water from the root-zone. Intermittent irrigation regimes, although could substantially reduce surface runoff from the fields, resulted in relatively deeper water tables during the growing season, suggesting a potential trade-off between water deliveries and root-zone soil moisture availability.

Selected soil physical properties at varying depths in a culti- vated Alfisol in northeast Florida were studied using classical and geo- statistical techniques. The objective was to investigate the variation in the physical properties among and within the sampling depths and un- derstand its implications to field water management in the area. Seventy soil core samples were collected in a grid sampling scheme from five depths: 22.5 cm, 45 cm, 67.5 cm, 90 cm, and 120 cm. Particle size distributions, bulk density (Db), and saturated hydraulic conductivity (Ks) of the soil samples from each depth were measured and statistically compared among depths, and the spatial variation of properties within each sampling depth was determined. Clay content and Db increased significantly with increasing depth, whereas the Ks decreased. The Ks exhibited the maximum variation in both vertical and horizontal direc- tion, whereas Db was the least variable among the three properties. The semivariogram analysis showed a weak spatial autocorrelation in soil properties within the sampling depths. The results suggested that despite significantly low conductivity and high Db of the soil profile at 120 cm, it was not impermeable enough to restrict vertical water movement as opposed to the general perception of the occurrence of a shallow im- permeable layer in the profile and consequent perched water table con- ditions. The results, therefore, provided a definitive insight into the water table management system in the area, which is important for under- standing the irrigation system, its efficiency, and consequent water quality impacts on surface and/or groundwater.

A century of hydrologic modification has altered the physical and biological drivers of landscape processes in the Everglades (southern Florida, USA). Restoring the ridge-slough patterned landscape, a dominant feature of the historical system, is a priority, but requires an understanding of pattern genesis mechanisms. Physical experiments to evaluate alternative pattern formation mechanisms are limited by the time scales of peat accumulation and loss, necessitating model-based comparisons, where support for a particular mechanism is based on model replication of extant patterning and trajectories of degradation. However, multiple mechanisms yield a central feature of ridge-slough patterning (patch elongation in the direction of historical flow), limiting the utility of that characteristic for discriminating among alternatives. Using data from vegetation maps we investigated the statistical features of ridge-slough spatial patterning (ridge density, patch perimeter, elongation, patch-area scaling, and spatial periodicity) to establish rigorous criteria for evaluating model performance, and to inform controls on pattern variation across the contemporary system. Mean water depth explained significant variation in ridge density, total perimeter, and length : width ratios, illustrating significant pattern response to existing hydrologic gradients. Two independent analyses (2-D periodograms and patch size distributions) provide strong evidence against regular patterning, with the landscape exhibiting neither a characteristic wavelength nor a characteristic patch size, both of which are expected under conditions that produce regular patterns. Rather, landscape properties suggest robust scale-free patterning, indicating genesis from the coupled effects of local facilitation and a global negative feedback operating uniformly at the landscape-scale. Critically, this challenges widespread invocation of meso-scale negative feedbacks for explaining ridge-slough pattern origins. These results help discern among genesis mechanisms and provide an improved statistical template against which to compare model outputs, as well as landscape trajectories with future restoration.

[1] In shallow unconfined aquifers, the response of the water table (WT) to input and output water fluxes is controlled by two distinct storage parameters, drainable and fillable porosity, which are applicable for WT drawdown and rise, respectively. However, only the drainable porosity estimated from the hydrostatic soil moisture profile is in common use. In this study, we show that under conditions of evapotranspiration and/or recharge from or to a shallow water table, drainable and fillable porosity have different values. Separate analytical expressions are developed for drainable and fillable porosity accounting for dynamic soil moisture conditions through the assumption of successive steady state fluxes in the unsaturated zone. The equations are expressed in terms of soil hydraulic parameters and matric suction at the soil surface. Parametric evapotranspiration and recharge functions are used to estimate the suction at the soil surface. The final expressions are independent of evapotranspiration or recharge function, thus allowing the use of any appropriate function to estimate the storage parameters. It is shown that the occurrence of unsaturated zone fluxes can result in significantly different values of drainable and fillable porosity, even when hysteresis is neglected. Application of the two parameters in a Boussinesq-type groundwater model resulted in significantly improved estimates of field-measured water table dynamics compared to the hydrostatic, single-parameter model.

The soil profile of Tri-county Agricultural Area (TCAA) located in Northeast Florida consists of an impermeable hardpan at about one meter depth, which restricts the vertical movement of water thus creating a perched water table during rainfall and/or irrigation events. Taking advantage of the shallow hardpan, the potato fields in the TCAA are irrigated with seepage irrigation system. Due to the nature of the system, some extent of subsurface lateral flow above the hardpan layer can be expected. This subsurface flow is of great environmental concern because it is responsible for increased nutrient and chemical loading in the Lower St. Johns River. Therefore, it is important to characterize this phenomenon so that new models of crop and nutrient management practices could be developed in the area.

Diurnal water table fluctuations (DWTFs), normally observed in shallow unconfined aquifers, are commonly used to estimate groundwater evapotranspiration (ET g ) by applying the soil water balance pioneered by W.N. White in 1932. A key element of White-based methods is the drainable porosity (or specific yield), a soil water storage parameter that significantly depends on both the vadose-zone soil moisture fluxes and water table (WT) elevation. However, it has traditionally been treated as either a constant or a function of the WT under hydrostatic soil moisture conditions. Recent research has shown that, at any given shallow WT position, vadose zone fluxes cause the drainable porosity to behave hysteretically, hence requiring estimation of two distinct parameters, drainable (λ d ) and fillable porosity (λ f ), during ET g estimation from DWTFs. We present a White-based method to estimate ET g that implements separate λ d and λ f parameters and hence accounts for both WT elevation and unsaturated zone moisture fluxes. The modified method not only improves the ET g estimates but also extend the applicability of the White method to periods during rainfall, unlike most previous implementations, which have been limited to omitting such periods. The modified method is demonstrated here for estimation of ET g from two crop fields in Northeast Florida for two growing seasons, each approximately 50 d, in 2010 and 2011, and the ET g estimates are compared with the standard Penman–Monteith method. The modified method significantly improved ET g estimates compared with the hydrostatic λ d based method, reducing the root mean square error by 94 and 96% at hourly and daily resolutions, respectively.