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Recent geomorphic evidence from the inner shelf and shoreface to the east of Long Island's barrier island system indicates that reworking of glacial outwash deposits at the inner shelf-shoreface transition, as sea level rises, may be supplying much of the sediment needed to maintain barrier islands to the west. A conceptual model describing sediment dispersal from outwash source areas was developed from this hypothesis. It was reasoned that outwash sediments ranging from silts to coarse gravels would be subject to differential transport paths across and along the shoreface upon reworking. Coarser grain sizes would move onshore toward the intertidal beach, whereas finer sediments would move offshore. Sand of intermediate grain size would be concentrated in the surf zone an move alongshore in wave-generated longshore currents. To test this model, 400 samples from the beach and shoreface of Long Island were analyzed for grain-size frequency distribution and each grain-size class was examined for frequency of occurrence in the cross-shore and alongshore directions. On a spatially averaged basis, grain-size classes displayed peak abundance in specific zones across the shoreface as predicted by the model, but alongshore trends could not be recognized among the "noisy" data. Therefore, empirical orthogonal functions (EOF) were used to examine uncorrelated (orthogonal) modes of variability in the occurrence of each grain-size class in the alongshore direction. The first function, representing more than 60% of the variability among the data, showed that grain sizes subject to longshore transport in the surf zone i crease in frequency in the alongshore direction relative to coarser grain sizes. Results also show that peak concentrations of coarse sediments correspond to zones subject to frequent overwashing. It is concluded that EOF analysis of individual grain-size classes holds promise for extracting trends from noisy data sets. End_of_Article - Last_Page 318------------

Individual trees have been shown to exhibit strong relationships between DBH, height and volume. Often such studies are cited as justification for forest volume or standing biomass estimation through remote sensing. With resolution of common satellite remote sensing systems generally too low to resolve individuals, and a need for larger coverage, these systems rely on descriptive heights, which account for tree collections in forests. For remote sensing and allometric applications, this height is not entirely understood in terms of its location. Here, a forest growth model (SERA) analyzes forest canopy height relationships with forest wood volume. Maximum height, mean, H₁₀₀, and Lorey's height are examined for variability under plant number density, resource and species. Our findings, shown to be allometrically consistent with empirical measurements for forested communities world-wide, are analyzed for implications to forest remote sensing techniques such as LiDAR and RADAR. Traditional forestry measures of maximum height, and to a lesser extent H₁₀₀ and Lorey's, exhibit little consistent correlation with forest volume across modeled conditions. The implication is that using forest height to infer volume or biomass from remote sensing requires species and community behavioral information to infer accurate estimates using height alone. SERA predicts mean height to provide the most consistent relationship with volume of the height classifications studied and overall across forest variations. This prediction agrees with empirical data collected from conifer and angiosperm forests with plant densities ranging between 10²-10⁶ plants/hectare and heights 6-49 m. Height classifications investigated are potentially linked to radar scattering centers with implications for allometry. These findings may be used to advance forest biomass estimation accuracy through remote sensing. Furthermore, Lorey's height with its specific relationship to remote sensing physics is recommended as a more universal indicator of volume when using remote sensing than achieved using either maximum height or H₁₀₀.

Abstract This paper investigates the hydrodynamics of a seabed-mounted, bottom-hinged, flap-type wave energy converter in shallow water. A conceptual model of the hydrodynamics of the device has been formulated and shows that, as the motion of the flap is highly constrained, the magnitude of the wave force on the flap is the key determinant of power capture. The results from a physical modelling program have been used in conjunction with numerical data from WAMIT to validate the conceptual model. The work finds that designing the device to increase the wave force is more profitable than designing it to be tuned to the incident wave climate. As wave force is the primary driver of device performance it is shown that the flap should fill the water column and pierce the water surface to reduce decoupling due to wave overtopping. It is concluded that, in order to maximize capture factor at a typical North Atlantic site, the flap should be approximately 20–30 m wide, with large diameter rounded side edges, having its pivot close to the seabed and its top edge piercing the water surface.

AbstractChina is under pressure to improve its agricultural productivity to keep up with the demands of a growing population with increasingly resource‐intensive diets. This productivity improvement must occur against a backdrop of carbon intensity reduction targets, and a highly fragmented, nutrient‐inefficient farming system. Moreover, the Chinese government increasingly recognizes the need to rationalize the management of the 800 million tonnes of agricultural crop straw that China produces each year, up to 40% of which is burned in‐field as a waste. Biochar produced from these residues and applied to land could contribute to China's agricultural productivity, resource use efficiency and carbon reduction goals. However competing uses for China's straw residues are rapidly emerging, particularly from bioenergy generation. Therefore it is important to understand the relative economic viability and carbon abatement potential of directing agricultural residues to biochar rather than bioenergy. Using cost‐benefit analysis (CBA) and life‐cycle analysis (LCA), this paper therefore compares the economic viability and carbon abatement potential of biochar production via pyrolysis, with that of bioenergy production via briquetting and gasification. Straw reincorporation and in‐field straw burning are used as baseline scenarios. We find that briquetting straw for heat energy is the most cost‐effective carbon abatement technology, requiring a subsidy of $7 MgCO2e−1 abated. However China's current bioelectricity subsidy scheme makes gasification (NPV $12.6 million) more financially attractive for investors than both briquetting (NPV $7.34 million), and pyrolysis ($−1.84 million). The direct carbon abatement potential of pyrolysis (1.06 MgCO2e per odt straw) is also lower than that of briquetting (1.35 MgCO2e per odt straw) and gasification (1.16 MgCO2e per odt straw). However indirect carbon abatement processes arising from biochar application could significantly improve the carbon abatement potential of the pyrolysis scenario. Likewise, increasing the agronomic value of biochar is essential for the pyrolysis scenario to compete as an economically viable, cost‐effective mitigation technology.

This chapter uses data on Intended Nationally Determined Contributions (INDCs) to examine the nature of climate change mitigation and adaptation actions being pursued in African countries and assesses the extent to which preferred mitigation and adaptation priorities advance the cause of sustainable development on the continent. The prospective synergies between mitigation and adaptation approaches and sustainable development are assessed. Also, the pathways through which resource constraints and institutional and policy environment affect Africa’s ability to mitigate and adapt to climate change are examined, as well as the degree to which these constraints are being addressed. It is argued that Africa’s ability to benefit from sustainable development synergies embedded in the mitigation and adaptation strategies in the INDCs will be greatly limited by institutional and policy environment that hinders funding, capacity building, and technological innovation systems development. The slow pace of efforts to address these impediments further erodes confidence that climate adaptation in Africa will be effective at sufficiently contributing to a reduction in climate change risks to the continent.

Urban vacant lots are often a contentious feature in cities, seen as overgrown, messy eyesores that plague neighborhoods. We propose a shift in this perception to locations of urban potential, because vacant lots may serve as informal greenspaces that maximize urban biodiversity while satisfying residents’ preferences for their design and use. Our goal was to assess what kind of vacant lots are ecologically valuable by assessing their biotic contents and residents’ preferences within a variety of settings. We surveyed 150 vacant lots throughout Baltimore, Maryland for their plant and bird communities, classified the lot’s setting within the urban matrix, and surveyed residents. Remnant vacant lots had greater vegetative structure and bird species richness as compared to other lot origins, while vacant lot settings had limited effects on their contents. Residents preferred well-maintained lots with more trees and less artificial cover, support of which may increase local biodiversity in vacant lots. Collectively, we propose that vacant lots with a mixture of remnant and planted vegetation can act as sustainable urban greenspaces with the potential for some locations to enhance urban tree cover and bird habitat, while balancing the needs and preferences of city residents.

AbstractAutotrophs play an essential role in the cycling of carbon and nutrients, yet disease‐ecosystem relationships are often overlooked in these dynamics. Importantly, the availability of elemental nutrients like nitrogen and phosphorus impacts infectious disease in autotrophs, and disease can induce reciprocal effects on ecosystem nutrient dynamics. Relationships linking infectious disease with ecosystem nutrient dynamics are bidirectional, though the interdependence of these processes has received little attention. We introduce disease‐mediated nutrient dynamics (DND) as a framework to describe the multiple, concurrent pathways linking elemental cycles with infectious disease. We illustrate the impact of disease–ecosystem feedback loops on both disease and ecosystem nutrient dynamics using a simple mathematical model, combining approaches from classical ecological (logistic and Droop growth) and epidemiological (susceptible and infected compartments) theory. Our model incorporates the effects of nutrient availability on the growth rates of susceptible and infected autotroph hosts and tracks the return of nutrients to the environment following host death. While focused on autotroph hosts here, the DND framework is generalizable to higher trophic levels. Our results illustrate the surprisingly complex dynamics of host populations, infection patterns, and ecosystem nutrient cycling that can arise from even a relatively simple feedback between disease and nutrients. Feedback loops in disease‐mediated nutrient dynamics arise via effects of infection and nutrient supply on host stoichiometry and population size. Our model illustrates how host growth rate, defense, and tissue chemistry can impact the dynamics of disease–ecosystem relationships. We use the model to motivate a review of empirical examples from autotroph–pathogen systems in aquatic and terrestrial environments, demonstrating the key role of nutrient–disease and disease–nutrient relationships in real systems. By assessing existing evidence and uncovering data gaps and apparent mismatches between model predictions and the dynamics of empirical systems, we highlight priorities for future research intended to narrow the persistent disciplinary gap between disease and ecosystem ecology. Future empirical and theoretical work explicitly examining the dynamic linkages between disease and ecosystem ecology will inform fundamental understanding for each discipline and will better position the field of ecology to predict the dynamics of disease and elemental cycles in the context of global change.

Resource-use efficiency and crop yield are significant factors in the management of agricultural greenhouse. Appropriate modeling methods effectively improve the control performance and efficiency of the greenhouse system and are conducive to the design of water and energy-saving strategies. Meanwhile, the extreme environment could be forecasted in advance, which reduces pests and diseases as well as provides high-quality food. Accordingly, the interest of the scientific community in greenhouse modeling and optimizing has grown considerably. The objective of this work is to provide guidance and insight into the topic by reviewing 73 representative articles and to further support cleaner and sustainable crop production. Compared to the existing literature review, this work details the approaches to improve the greenhouse model in the aspects of parameter identification, structure and process optimization, and multi-model integration to better model complex greenhouse system. Furthermore, a statistical study has been carried out to summarize popular technology and future trends. It was found that dynamic and neural network techniques are most commonly used to establish the greenhouse model and the heuristic algorithm is popular to improve the accuracy and generalization ability of the model. Notably, deep learning, the combination of “knowledge” and “data”, and coupling between the greenhouse system elements have been considered as future valuable development.