Modeling the relationship between air temperature and grapefruit quality traits.

BACKGROUND: Consumers of grapefruit require consistent fruit quality with a good physical appearance and taste. The air temperature during the growing season affects both the external (external color index (ECI)) and internal (titratable acidity (TA) and total soluble solids ratio (TSS/TA)) fruit quality of grapefruit. The objective of this study was to develop computer models that encompass the relationship between preharvest air temperature and fruit quality to predict fruit quality of grapefruit at harvest. RESULTS: There was a logarithmic relationship between the number of days with a daily minimum air temperature ≤13 °C and ECI, with a greater number of days resulting in higher ECI. In addition, there was a second-order polynomial relationship between the number of hours ≥21 °C and both TA and TSS/TA, with a greater number of hours resulting in lower TA and higher TSS/TA. Model performance for predicting the ECI, TA, and TSS/TA during 2004-05 and 2005-06 growing seasons was good, with Nash and Sutcliffe coefficient of efficiency (NSE) values for each season of 0.835 and 0.917 respectively for ECI, 0.896 and 0.965 respectively for TA and 0.898 and 0.966 respectively for TSS/TA. Applying the model to statistical survey data covering 13 growing seasons demonstrated that the TSS/TA model was robust. CONCLUSION: Statistical models were developed that predicted the development of grapefruit ECI, TA, and TSS/TA. The TSS/TA model was confirmed after application to long-term statistical survey data covering 13 growing seasons. © 2022 Society of Chemical Industry.

Wetland hydropattern and vegetation greenness predict avian populations in Palo Verde, Costa Rica.

Many wetlands around the world that occur at the base of watersheds are under threat from land-use change, hydrological alteration, nutrient pollution, and invasive species. A relevant measure of whether the ecological character of these ecosystems has changed is the species diversity of wetland-dependent waterbirds, especially those of conservation value. Here, we evaluate the potential mechanisms controlling variability over time and space in avian species diversity of the wetlands in the Palo Verde National Park, a Ramsar Site of international importance in Costa Rica. To do so, we assessed the relative importance of several key wetland condition metrics (i.e., surface water depth, wetland extent, and vegetation greenness), and temporal fluctuations in these metrics, in predicting the abundance of five waterbirds of high conservation value as well as overall waterbird diversity over a 9-yr period. Generalized additive models revealed that mean NDVI, an indicator of vegetation greenness, combined with a metric used to evaluate temporal fluctuations in the wetland extent best predicted four of the five waterbird species of high conservation value as well as overall waterbird species richness and diversity. Black-bellied Whistling-ducks, which account for over one-half of all waterbird individuals, and all waterbird species together were better predicted by including surface water depth along with wetland extent and its fluctuations. Our calibrated species distribution model confidently quantified monthly averages of the predicted total waterbird abundances in seven of the 10 sub-wetlands making up the Ramsar Site and confirmed that the biophysical diversity of this entire wetland system is important to supporting waterbird populations both as a seasonal refuge and more permanently. This work further suggests that optimizing the timing and location of ongoing efforts to reduce invasive vegetation cover may be key to avian conservation by increasing waterbird habitat.

Parsimonious Mechanistic Modeling of Bacterial Runoff into Irrigation Ponds To Inform Food Safety Management of Agricultural Water Quality.

Pond irrigation water comprises a major pathway of pathogenic bacteria to fresh produce. Current regulatory methods have been shown to be ineffective in assessing this risk when variability of bacterial concentrations is large. This paper proposes using mechanistic modeling of bacterial transport as a way to identify improved strategies for mitigating this risk pathway. If the mechanistic model is successfully tested against observed data, global sensitivity analysis (GSA) can identify important mechanisms to inform alternative, preventive bacterial control practices. Model development favored parsimony and prediction of peak bacterial concentration events. Data from two highly variable surface water irrigation ponds showed that the model performance was similar or superior to that of existing pathogen transport models, with a Nash-Sutcliffe efficiency of 0.48 and 0.18 for the two ponds. GSA quantified bacterial sourcing and hydrology as the most important processes driving pond bacterial contamination events. Model analysis has two main implications for improved regulatory methods: that peak concentration events are associated with runoff-producing rainfall events and that intercepting bacterial runoff transport may be the best option to prevent bacterial contamination of surface water irrigation ponds and thus fresh produce. This research suggests the need for temporal management strategies. IMPORTANCE Preventive management of agricultural waters requires understanding of the drivers of bacterial contamination events. We propose mechanistic modeling as a way forward to understand and predict such events and have developed and tested a parsimonious model for rain-driven surface runoff contributing to generic Escherichia coli contamination of irrigation ponds in Central Florida. While the model was able to predict the timing of peak events reasonably well, the highly variable magnitude of the peaks was less well predicted. This indicates the need to collect more data on the fecal contamination inputs of these ponds and the use of mechanistic modeling and global sensitivity analysis to identify the most important data needs.

Importance of genetic parameters and uncertainty of MANIHOT, a new mechanistic cassava simulation model.

We identified the most sensitive genotype-specific parameters (GSPs) and their contribution to the uncertainty of the MANIHOT simulation model. We applied a global sensitivity and uncertainty analysis (GSUA) of the GSPs to the simulation outputs for the cassava development, growth, and yield in contrasting environments. We compared enhanced Sampling for Uniformity, a qualitative screening method new to crop simulation modeling, and Sobol, a quantitative, variance-based method. About 80% of the GSPs contributed to most of the variation in maximum leaf area index (LAI), yield, and aboveground biomass at harvest. Relative importance of the GSPs varied between warm and cool temperatures but did not differ between rainfed and no water limitation conditions. Interactions between GSPs explained 20% of the variance in simulated outputs. Overall, the most important GSPs were individual node weight, radiation use efficiency, and maximum individual leaf area. Base temperature for leaf development was more important for cool compared to warm temperatures. Parameter uncertainty had a substantial impact on model predictions in MANIHOT simulations, with the uncertainty 2-5 times larger for warm compared to cool temperatures. Identification of important GSPs provides an objective way to determine the processes of a simulation model that are critical versus those that have little relevance.

Revealing Biotic and Abiotic Controls of Harmful Algal Blooms in a Shallow Subtropical Lake through Statistical Machine Learning.

Harmful algal blooms are a growing human and environmental health hazard globally. Eco-physiological diversity of the cyanobacteria genera that make up these blooms creates challenges for water managers tasked with controlling the intensity and frequency of blooms, particularly of harmful taxa (e.g., toxin producers, N2 fixers). Compounding these challenges is the ongoing debate over the efficacy of nutrient management strategies (phosphorus-only versus nitrogen and phosphorus), which increases decision-making uncertainty. To improve our understanding of how different cyanobacteria respond to nutrient levels and other biophysical factors, we analyzed a unique 17 year data set comprising monthly observations of cyanobacteria genera and zooplankton abundances, water quality, and flow in a bloom-impacted, subtropical, flow-through lake in Florida (United States). Using the Random Forests machine learning algorithm, an ensemble modeling approach, we characterized and quantified relationships among environmental conditions and five dominant cyanobacteria genera. Results highlighted nonlinear relationships and critical thresholds between cyanobacteria genera and environmental covariates, the potential for hydrology and temperature to limit the efficacy of cyanobacteria bloom management actions, and the importance of a dual nutrient management strategy for reducing bloom risk in the long term.

Using Cluster Analysis to Compartmentalize a Large Managed Wetland Based on Physical, Biological, and Climatic Geospatial Attributes.

Hierarchical and partitional cluster analyses were used to compartmentalize Water Conservation Area 1, a managed wetland within the Arthur R. Marshall Loxahatchee National Wildlife Refuge in southeast Florida, USA, based on physical, biological, and climatic geospatial attributes. Single, complete, average, and Ward's linkages were tested during the hierarchical cluster analyses, with average linkage providing the best results. In general, the partitional method, partitioning around medoids, found clusters that were more evenly sized and more spatially aggregated than those resulting from the hierarchical analyses. However, hierarchical analysis appeared to be better suited to identify outlier regions that were significantly different from other areas. The clusters identified by geospatial attributes were similar to clusters developed for the interior marsh in a separate study using water quality attributes, suggesting that similar factors have influenced variations in both the set of physical, biological, and climatic attributes selected in this study and water quality parameters. However, geospatial data allowed further subdivision of several interior marsh clusters identified from the water quality data, potentially indicating zones with important differences in function. Identification of these zones can be useful to managers and modelers by informing the distribution of monitoring equipment and personnel as well as delineating regions that may respond similarly to future changes in management or climate.

Accounting for the Impact of Management Scenarios on Typha Domingensis (Cattail) in an Everglades Wetland.

The coupled regional simulation model, and the transport and reaction simulation engine were recently adapted to simulate ecology, specifically Typha domingensis (Cattail) dynamics in the Everglades. While Cattail is a native Everglades species, it has become invasive over the years due to an altered habitat over the last few decades, taking over historically Cladium jamaicense (Sawgrass) areas. Two models of different levels of algorithmic complexity were developed in previous studies, and are used here to determine the impact of various management decisions on the average Cattail density within Water Conservation Area 2A in the Everglades. A Global Uncertainty and Sensitivity Analysis was conducted to test the importance of these management scenarios, as well as the effectiveness of using zonal statistics. Management scenarios included high, medium and low initial water depths, soil phosphorus concentrations, initial Cattail and Sawgrass densities, as well as annually alternating water depths and soil phosphorus concentrations, and a steadily decreasing soil phosphorus concentration. Analysis suggests that zonal statistics are good indicators of regional trends, and that high soil phosphorus concentration is a pre-requisite for expansive Cattail growth. It is a complex task to manage Cattail expansion in this region, requiring the close management and monitoring of water depth and soil phosphorus concentration, and possibly other factors not considered in the model complexities. However, this modeling framework with user-definable complexities and management scenarios, can be considered a useful tool in analyzing many more alternatives, which could be used to aid management decisions in the future.

Brief history of agricultural systems modeling.

Agricultural systems science generates knowledge that allows researchers to consider complex problems or take informed agricultural decisions. The rich history of this science exemplifies the diversity of systems and scales over which they operate and have been studied. Modeling, an essential tool in agricultural systems science, has been accomplished by scientists from a wide range of disciplines, who have contributed concepts and tools over more than six decades. As agricultural scientists now consider the "next generation" models, data, and knowledge products needed to meet the increasingly complex systems problems faced by society, it is important to take stock of this history and its lessons to ensure that we avoid re-invention and strive to consider all dimensions of associated challenges. To this end, we summarize here the history of agricultural systems modeling and identify lessons learned that can help guide the design and development of next generation of agricultural system tools and methods. A number of past events combined with overall technological progress in other fields have strongly contributed to the evolution of agricultural system modeling, including development of process-based bio-physical models of crops and livestock, statistical models based on historical observations, and economic optimization and simulation models at household and regional to global scales. Characteristics of agricultural systems models have varied widely depending on the systems involved, their scales, and the wide range of purposes that motivated their development and use by researchers in different disciplines. Recent trends in broader collaboration across institutions, across disciplines, and between the public and private sectors suggest that the stage is set for the major advances in agricultural systems science that are needed for the next generation of models, databases, knowledge products and decision support systems. The lessons from history should be considered to help avoid roadblocks and pitfalls as the community develops this next generation of agricultural systems models.

Towards a new generation of agricultural system data, models and knowledge products: Design and improvement.

This paper presents ideas for a new generation of agricultural system models that could meet the needs of a growing community of end-users exemplified by a set of Use Cases. We envision new data, models and knowledge products that could accelerate the innovation process that is needed to achieve the goal of achieving sustainable local, regional and global food security. We identify desirable features for models, and describe some of the potential advances that we envisage for model components and their integration. We propose an implementation strategy that would link a "pre-competitive" space for model development to a "competitive space" for knowledge product development and through private-public partnerships for new data infrastructure. Specific model improvements would be based on further testing and evaluation of existing models, the development and testing of modular model components and integration, and linkages of model integration platforms to new data management and visualization tools.

Toward a new generation of agricultural system data, models, and knowledge products: State of agricultural systems science.

We review the current state of agricultural systems science, focusing in particular on the capabilities and limitations of agricultural systems models. We discuss the state of models relative to five different Use Cases spanning field, farm, landscape, regional, and global spatial scales and engaging questions in past, current, and future time periods. Contributions from multiple disciplines have made major advances relevant to a wide range of agricultural system model applications at various spatial and temporal scales. Although current agricultural systems models have features that are needed for the Use Cases, we found that all of them have limitations and need to be improved. We identified common limitations across all Use Cases, namely 1) a scarcity of data for developing, evaluating, and applying agricultural system models and 2) inadequate knowledge systems that effectively communicate model results to society. We argue that these limitations are greater obstacles to progress than gaps in conceptual theory or available methods for using system models. New initiatives on open data show promise for addressing the data problem, but there also needs to be a cultural change among agricultural researchers to ensure that data for addressing the range of Use Cases are available for future model improvements and applications. We conclude that multiple platforms and multiple models are needed for model applications for different purposes. The Use Cases provide a useful framework for considering capabilities and limitations of existing models and data.

Colloid filtration in surface dense vegetation: experimental results and theoretical predictions.

Understanding colloid and colloid-facilitated contaminant transport in overland flow through dense vegetation is important to protect water quality in the environment, especially for water bodies receiving agricultural and urban runoff. In previous studies, a single-stem efficiency theory for rigid and clean stem systems was developed to predict colloid filtration by plant stems of vegetation in laminar overland flow. Hence, in order to improve the accuracy of the single-stem efficiency theory to real dense vegetation system, we incorporated the effect of natural organic matter (NOM) on the filtration of colloids by stems. Laboratory dense vegetation flow chamber experiments and model simulations were used to determine the kinetic deposition (filtration) rate of colloids under various conditions. The results show that, in addition to flow hydrodynamics and solution chemistry, steric repulsion afforded by NOM layer on the plants stem surface also plays a significant role in controlling colloid deposition on vegetation in overland flow. For the first time, a refined single-stem efficiency theory with considerations of the NOM effect is developed that describes the experimental data with good accuracy. This theory can be used to not only help construct and refine mathematical models of colloid transport in real vegetation systems in overland flow, but also inform the development of theories of colloid deposition on NOM-coated surfaces in natural, engineered, and biomedical systems.

Analytical and experimental analysis of solute transport in heterogeneous porous media.

Knowledge of solute transport in heterogeneous porous media is crucial to monitor contaminant fate and transport in soil and groundwater systems. In this study, we present new findings from experimental and mathematical analysis to improve current understanding of solute transport in structured heterogeneous porous media. Three saturated columns packed with different sand combinations were used to examine the breakthrough behavior of bromide, a conservative tracer. Experimental results showed that bromide had different breakthrough responses in the three types of sand combinations, indicating that heterogeneity in hydraulic conductivity has a significant effect on the solute transport in structured heterogeneous porous media. Simulations from analytical solutions of a two-domain solute transport model matched experimental breakthrough data well for all the experimental conditions tested. Experimental and model results show that under saturated flow conditions, advection dominates solute transport in both fast-flow and slow-flow domains. The sand with larger hydraulic conductivity provided a preferential flow path for solute transport (fast-flow domain) that dominates the mass transfer in the heterogeneous porous media. Importantly, the transport in the slow-flow domain and mass exchange between the domains also contribute to the flow and solute transport processes and thus must be considered when investigating contaminant transport in heterogeneous porous media.

Incorporating the benefits of vegetative filter strips into risk assessment and risk management of pesticides.

The pesticide registration process in North America, including the USA and Canada, involves conducting a risk assessment based on relatively conservative modeling to predict pesticide concentrations in receiving waterbodies. The modeling framework does not consider some commonly adopted best management practices that can reduce the amount of pesticide that may reach a waterbody, such as vegetative filter strips (VFS). Currently, VFS are being used by growers as an effective way to reduce off-site movement of pesticides, and they are being required or recommended on pesticide labels as a mitigation measure. Given the regulatory need, a pair of multistakeholder workshops were held in Raleigh, North Carolina, to discuss how to incorporate VFS into pesticide risk assessment and risk management procedures within the North American regulatory framework. Because the risk assessment process depends heavily on modeling, one key question was how to quantitatively incorporate VFS into the existing modeling approach. Key outcomes from the workshops include the following: VFS have proven effective in reducing pesticide runoff to surface waterbodies when properly located, designed, implemented, and maintained; Vegetative Filter Strip Modeling System (VFSMOD), a science-based and widely validated mechanistic model, is suitable for further vetting as a quantitative simulation approach to pesticide mitigation with VFS in current regulatory settings; and VFSMOD parametrization rules need to be developed for the North American aquatic exposure assessment. Integr Environ Assess Manag 2024;20:454-464. © 2023 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

DLVO interactions of carbon nanotubes with isotropic planar surfaces.

Knowledge of the interaction between carbon nanotubes (CNTs) and planar surfaces is essential to optimizing CNT applications as well as reducing their environmental impact. In this work, the surface element integration (SEI) technique was coupled with the DLVO theory to determine the orientation-dependent interaction energy between a single-walled carbon nanotube (SWNT) and an infinite isotropic planar surface. For the first time, an analytical formula was developed to describe accurately the interaction between not only pristine but also surface-charged CNTs and planar surfaces with arbitrary rotational angles. Compared to other methods, the new analytical formulas were either more convenient or more accurate in describing the interaction between CNTs and planar surfaces, especially with respect to arbitrary angles. The results revealed the complex dependences of both force and torque between SWNTs and planar surfaces on the separation distances and rotational angles. With minor modifications, the analytical formulas derived for SWNTs can also be applied to multiwalled carbon nanotubes (MWNTs). The new analytical expressions presented in this work can be used as a robust tool to describe the DLVO interaction between CNTs and planar surfaces under various conditions and thus to assist in the design and application of CNT-based products.

Aggregation kinetics of graphene oxides in aqueous solutions: experiments, mechanisms, and modeling.

Although graphene oxide (GO) has been used in many applications to improve human life quality, its environmental fate and behavior are still largely unknown. In this work, a novel approach that combines experimental measurements and theoretical calculations was used to determine the aggregation kinetics of GO sheets in aqueous solutions under different chemistry conditions (e.g., cation valence and pH). Experimental data showed that both cation valence and pH showed significant effect on the aggregation of GO sheets. The measured critical coagulation concentrations were in good agreement with the predictions of the extended Schulze-Hardy rule. Ca(2+) and Mg(2+) were more effective than Na(+) in aggregating the GO sheets, which could be attributed to the cross-linking between GO sheets by the divalent cations through "bridging" the functional groups at the edges of the GO sheets. When solution pH increases, deprotonation of carboxylic groups was found to play a key role in increasing GO sheet stability and surface charge development. These results suggested that edge-to-edge and face-to-face interactions were the dominant modes of GO aggregation in the presence of divalent metal ions and H(+), respectively. A modified attachment efficiency (α) model was developed on the basis of the Maxwell approach with considerations of both primary and secondary minima. The model predictions matched the experimental measurements of the aggregation kinetics of GO sheets in aqueous solutions under all of the tested experimental conditions well.

Quantifying the contribution of external loadings and internal hydrodynamic processes to the water quality of Lake Okeechobee.

The water quality of a waterbody is determined by internal hydrodynamic processes as well as external loadings. Understanding the interaction between the external loading and internal process of a waterbody is essential for efficient water management and water quality improvement. Studies and efforts have focused on water and nutrient loading from drainage watersheds, but the contribution of the waterbody's internal process to water quality is often ignored and not well documented. This study investigated how the water quality of Lake Okeechobee is controlled by external and internal factors using statistical and numerical modeling approaches. Water quantity and quality observed at the outlets of the Lake Okeechobee drainage basins and 19 monitoring sites located within the lake were statistically analyzed using multilinear regression. A three-dimensional numerical model, namely Environmental Fluid Dynamics Code (EFDC), was calibrated to the observations to mathematically represent the lake's internal hydrodynamic process. The multilinear regression found that the water quality was the most sensitive to air temperature, the total phosphorus (TP) concentration of inflow entering the lake from the Kissimmee River basins, and the amount of outflow discharged from the lake among external factors. However, the regression models and their explanatory power were substantially varied by the monitoring stations. The model parameter sensitivity analysis of the calibrated EFDC model showed that model parameters related to the lake's internal algal processes including algal growth, predation, and basal metabolism rates had greater impacts on algal biomass than other model parameters controlling nutrient-related processes such as nutrient half-saturation and hydrolysis rates. The EFDC input data sensitivity analysis found that wind (speed) is the major driving force for the internal hydrodynamic processes; its impact on algal biomass was greater than those of the external loadings. In addition, the algal biomass was found to have an inverse relationship with wind-induced horizontal currents. The results demonstrate the dynamic contribution of the internal and external drivers to the water quality of Lake Okeechobee, suggesting the need to consider both internal hydrodynamic and external loading processes for efficient water quality improvement of the lake.

Dynamic prediction of effective runoff sediment particle size for improved assessment of erosion mitigation efficiency with vegetative filter strips.

The most widely implemented mitigation measure to reduce transfer of surface runoff pesticides and other pollutants to surface water bodies are vegetative filter strips (VFS). The most commonly used dynamic model for quantifying the reduction by VFS of surface runoff, eroded sediment, pesticides and other pollutants is VFSMOD, which simulates reduction of total inflow (∆Q) and of incoming eroded sediment load (∆E) mechanistically during the rainfall-runoff event. These variables are subsequently used to calculate the reduction of pesticide load by the VFS (∆P). Since errors in ∆Q and ∆E propagate into ∆P, for strongly-sorbing compounds an accurate prediction of ∆E is crucial for a reliable prediction of ∆P. The most important incoming sediment characteristic for ∆E is the median particle diameter (d50). Current d50 estimation methods are simplistic, yielding fixed d50 based on soil properties and ignoring specific event characteristics and dynamics. We derive an improved dynamic d50 parameterization equation for use in regulatory VFS scenarios based on an extensive dataset of 93 d50 values and 17 candidate explanatory variables compiled from heterogeneous data sources and methods. The dataset was analysed first using machine learning techniques (Random Forest, Gradient Boosting) and Global Sensitivity Analysis (GSA) as a dimension reduction technique and to identify potential interactions between explanatory variables. Using the knowledge gained, a parsimonious multiple regression equation with 6 predictors was developed and thoroughly tested. Since three of the predictors are event-specific (eroded sediment yield, rainfall intensity and peak runoff rate), predicted d50 vary dynamically across event magnitudes and intensities. Incorporation of the improved d50 parameterization equation in higher-tier pesticide assessment tools with VFSMOD provides more realistic quantitative mitigation in regulatory US-EPA and EU FOCUS pesticide risk assessment frameworks. The equation is also readily applicable to other erosion management problems.

Single collector attachment efficiency of colloid capture by a cylindrical collector in laminar overland flow.

Little research has been conducted to investigate the fate and transport of colloids in shallow overland flow through dense vegetation under unfavorable chemical conditions. In this work, the single collector attachment efficiency (α) of colloid capture by a simulated plant stem (i.e., cylindrical collector) in laminar overland flow was measured directly in laboratory flow chamber experiments. Fluorescent microspheres of two sizes were used as experimental colloids. The colloid suspensions flowed toward a glass cylindrical rod installed in a small size flow channel at different laminar flow rates. Different solution ionic strengths (IS) were used in the experiments to simulate unfavorable attachment conditions. Our results showed that α increased with IS and decreased with flow velocity. Existing theoretical and empirical models of colloid attachment efficiency for porous media were used to simulate the experimental measurements in α and found to fall short in matching the experimental data. A new dimensionless (regression) equation was proposed that predicts the α of colloid capture by a cylindrical collector in laminar overland flow with reasonable accuracy. In addition, the equation was also effective in predicting the attachment efficiency of colloid deposition in porous media.

Experimental evidence that rill-bed morphology is governed by emergent nonlinear spatial dynamics.

Past experimental work found that rill erosion occurs mainly during rill formation in response to feedback between rill-flow hydraulics and rill-bed roughness, and that this feedback mechanism shapes rill beds into a succession of step-pool units that self-regulates sediment transport capacity of established rills. The search for clear regularities in the spatial distribution of step-pool units has been stymied by experimental rill-bed profiles exhibiting irregular fluctuating patterns of qualitative behavior. We hypothesized that the succession of step-pool units is governed by nonlinear-deterministic dynamics, which would explain observed irregular fluctuations. We tested this hypothesis with nonlinear time series analysis to reverse-engineer (reconstruct) state-space dynamics from fifteen experimental rill-bed profiles analyzed in previous work. Our results support this hypothesis for rill-bed profiles generated both in a controlled lab (flume) setting and in an in-situ hillside setting. The results provide experimental evidence that rill morphology is shaped endogenously by internal nonlinear hydrologic and soil processes rather than stochastically forced; and set a benchmark guiding specification and testing of new theoretical framings of rill-bed roughness in soil-erosion modeling. Finally, we applied echo state neural network machine learning to simulate reconstructed rill-bed dynamics so that morphological development could be forecasted out-of-sample.

Experimental analysis of colloid capture by a cylindrical collector in laminar overland flow.

Although colloid-facilitated contaminant transport in water flow is a well-known contamination process, little research has been conducted to investigate the transport of colloidal particles through emergent vegetation in overland flow. In this work, a series of laboratory experiments were conducted to measure the single-collector contact efficiency (η(0)) of colloid capture by a simulated plant stem in laminar lateral flow. Fluorescent microspheres of various sizes were used as experimental colloids. The colloid suspensions were applied to a glass cylinder installed in a small size flow chamber at different flow rates. Two cylinder sizes were tested in the experiment and silicone grease was applied to the cylinder surface to make it favorable for colloid deposition. Our results showed that increases in flow rate and collector size reduced the value of η(0) and a minimum value of η(0) might exist for a colloid size. The experimental data were compared to theoretical predictions of different single-collector contact efficiency models. The results indicated that existing single-collector contact efficiency models underestimated the η(0) of colloid capture by the cylinders in laminar overland flow. A regression equation of η(0) as a function of collector Reynolds number (Re(c)) and Peclet number (N(Pe)) was developed and fit the experimental data very well (R(2) > 0.98). This regression equation can be used to help construct and refine mathematical models of colloid transport and filtration in laminar overland flow on vegetated surfaces.