Woodland structure and function in response to increasing aridity

Woodlands, characterised by a matrix of trees, shrubs and open interspaces, are important biomes on Earth. Woodlands support a number of important ecosystem functions such as primary production, carbon fixation and nutrient cycling, and provide multiple ecosystem services that are essential for human livelihoods. Woodland structure and function are regulated by both large-scale shifts in climate, and smaller-scale variation in species interactions, resource availability and land management. Predicted changes in climate are expected to increase dryness and intensify management activities (e.g., grazing, plant removal), imposing substantial challenges on the functioning of woodlands and their dependent biota. Exploring how woodlands change across a climatic (aridity) gradient and among different management practices is essential for understanding how they adapt to drier climates and intensified woodland management, and to predict the ecological consequences of increasing aridity on their functions. This thesis examines the response of woodland structure and function to increasing aridity and woody plant removal, and the impact of biotic (e.g., plant traits, competition, grazing) and abiotic (e.g., climate, soil) drivers at both microsite and sub-continental scales, based on meta-analysis and field survey. Chapter 1 provides an overview of woodland structure and function, and their biotic and abiotic driving factors, highlighting important findings on the impact of increasing dryness on woodland structure (e.g., trees, shrubs, and open interspaces) and function (e.g., fertile islands). Chapter 2 examines the allometric response of different Australian woody plant genera to increasing dryness. Chapters 3 and 4 describe variation in biocrust cover and the fertile island effect beneath perennial vegetation across different patch types at the microsite scale, and with increasing aridity at the sub-continental scale. Chapters 5 and 6 synthesise the ecosystem outcomes of removing woody plants and the impact of woody plant traits, climatic regimes, and removal practices on the effectiveness of woody plant removal across the globe. Chapter 7 provides a synopsis of previous chapters, highlighting the adaptation strategies of woodlands to predicted drier climates, suggesting alternative woodland management under changing climates, and providing direction for future work in this field

Variations of deep soil moisture under different vegetation types and influencing factors in a watershed of the Loess Plateau, China

Soil moisture in deep soil layers is a relatively stable water resource for vegetation growth in the semi-arid Loess Plateau of China. Characterizing the variations in deep soil moisture and its influencing factors at a moderate watershed scale is important to ensure the sustainability of vegetation restoration efforts. In this study, we focus on analyzing the variations and factors that influence the deep soil moisture (DSM) in 80–500 cm soil layers based on a soil moisture survey of the Ansai watershed in Yan'an in Shanxi Province. Our results can be divided into four main findings. (1) At the watershed scale, higher variations in the DSM occurred at 120–140 and 480–500 cm in the vertical direction. At the comparable depths, the variation in the DSM under native vegetation was much lower than that in human-managed vegetation and introduced vegetation. (2) The DSM in native vegetation and human-managed vegetation was significantly higher than that in introduced vegetation, and different degrees of soil desiccation occurred under all the introduced vegetation types. Caragana korshinskii and black locust caused the most serious desiccation. (3) Taking the DSM conditions of native vegetation as a reference, the DSM in this watershed could be divided into three layers: (i) a rainfall transpiration layer (80–220 cm); (ii) a transition layer (220–400 cm); and (iii) a stable layer (400–500 cm). (4) The factors influencing DSM at the watershed scale varied with vegetation types. The main local controls of the DSM variations were the soil particle composition and mean annual rainfall; human agricultural management measures can alter the soil bulk density, which contributes to higher DSM in farmland and apple orchards. The plant growth conditions, planting density, and litter water holding capacity of introduced vegetation showed significant relationships with the DSM. The results of this study are of practical significance for vegetation restoration strategies, especially for the choice of vegetation types, planting zones, and proper human management measures

Nonlinearly Shaped Pulses in Photoinjectors and Free-Electron Lasers

Photoinjectors and Free Electron Lasers (FEL) are amongst the most advanced systems in accelerator physics and have consistently pushed the boundaries of emittance and x-ray peak power. In this paper, laser shaping at the cathode is proposed to further lower the emittance and reduce electron beam tails, which would result in brighter x-ray production. Using dispersion controlled nonlinear shaping (DCNS), laser pulses and beam dynamics were simulated in LCLS-II. The photoinjector emittance was optimized and the resulting e-beam profiles were then simulated and optimized in the linac. Finally, the expected FEL performance is estimated and compared to the current technology: Gaussian laser pulses on the cathode. The e-beams produced by DCNS pulses show a potential for 35% increase in x-ray power per pulse during SASE when compared to the standard Gaussian laser pulses

Towards Plausible Differentially Private ADMM Based Distributed Machine Learning

The Alternating Direction Method of Multipliers (ADMM) and its distributed version have been widely used in machine learning. In the iterations of ADMM, model updates using local private data and model exchanges among agents impose critical privacy concerns. Despite some pioneering works to relieve such concerns, differentially private ADMM still confronts many research challenges. For example, the guarantee of differential privacy (DP) relies on the premise that the optimality of each local problem can be perfectly attained in each ADMM iteration, which may never happen in practice. The model trained by DP ADMM may have low prediction accuracy. In this paper, we address these concerns by proposing a novel (Improved) Plausible differentially Private ADMM algorithm, called PP-ADMM and IPP-ADMM. In PP-ADMM, each agent approximately solves a perturbed optimization problem that is formulated from its local private data in an iteration, and then perturbs the approximate solution with Gaussian noise to provide the DP guarantee. To further improve the model accuracy and convergence, an improved version IPP-ADMM adopts sparse vector technique (SVT) to determine if an agent should update its neighbors with the current perturbed solution. The agent calculates the difference of the current solution from that in the last iteration, and if the difference is larger than a threshold, it passes the solution to neighbors; or otherwise the solution will be discarded. Moreover, we propose to track the total privacy loss under the zero-concentrated DP (zCDP) and provide a generalization performance analysis. Experiments on real-world datasets demonstrate that under the same privacy guarantee, the proposed algorithms are superior to the state of the art in terms of model accuracy and convergence rate.Comment: Comments: Accepted for publication in CIKM'2

The spatial distribution and temporal variation of desert riparian forests and their influencing factors in the downstream Heihe River basin, China

Desert riparian forests are the main restored vegetation community in Heihe River basin. They provide critical habitats and a variety of ecosystem services in this arid environment. Since desert riparian forests are also sensitive to disturbance, examining the spatial distribution and temporal variation of these forests and their influencing factors is important to determine the limiting factors of vegetation recovery after long-term restoration. In this study, field experiment and remote sensing data were used to determine the spatial distribution and temporal variation of desert riparian forests and their relationship with the environmental factors. We classified five types of vegetation communities at different distances from the river channel. Community coverage and diversity formed a bimodal pattern, peaking at the distances of 1000 and 3000 m from the river channel. In general, the temporal normalized difference vegetation index (NDVI) trend from 2000 to 2014 was positive at different distances from the river channel, except for the region closest to the river bank (i.e. within 500 m from the river channel), which had been undergoing degradation since 2011. The spatial distribution of desert riparian forests was mainly influenced by the spatial heterogeneity of soil properties (e.g. soil moisture, bulk density and soil particle composition). Meanwhile, while the temporal variation of vegetation was affected by both the spatial heterogeneity of soil properties (e.g. soil moisture and soil particle composition) and to a lesser extent, the temporal variation of water availability (e.g. annual average and variability of groundwater, soil moisture and runoff). Since surface (0–30 cm) and deep (100–200 cm) soil moisture, bulk density and the annual average of soil moisture at 100 cm obtained from the remote sensing data were regarded as major determining factors of community distribution and temporal variation, conservation measures that protect the soil structure and prevent soil moisture depletion (e.g. artificial soil cover and water conveyance channels) were suggested to better protect desert riparian forests under climate change and intensive human disturbance

Research on Liquefaction Resistance of Bucket Foundation for Offshore Wind Turbines

[Introduction] With the increasing demand for clean energy, the offshore wind power sector has seen a spurt of progress in recent years, and the bucket foundation has become the preferred choice for offshore wind turbines considering its good economy, convenient construction, and recyclability. Due to the widespread distribution of seismic zones in China, the seismic performance of bucket foundation is a crucial consideration for structural design. The bucket foundation is featured by high structure stiffness, so that the probability of structure damage caused by earthquake is low, and the failure under earthquake is mainly caused by the liquefaction of the foundation soil. For this purpose, the paper focuses on the seismic performance of bucket foundation in sandy soil. [Method] The liquefaction resistance of sandy soil for bucket foundation was analyzed by shaking table tests in this paper. The study objects included four types of bucket foundation in sandy soil, namely mono-bucket foundation (MBF), composite bucket foundation (CBF), three-bucket jacket foundation (TBJF) and four-bucket jacket foundation (FBJF). [Result] By carrying out shaking table tests, the excess pore pressure ratios of sandy soil for different types of bucket foundation under earthquake are obtained, and the impact mechanism of bucket foundation on the anti-liquefaction performance of sand soil is clarified. [Conclusion] The bucket foundation can improve the liquefaction resistance of sand, since the additional load effect of the superstructure and the hoop effect of bucket skirt weakens its shear shrinkage. The test results of MBF are compared with those of CBF, and the test results of TBJF are compared with those of FBJF. It is found that the seismic performance of CBF and FBJF is respectively superior to that of MBF and TBJF

Design and Estimation of Coded Exposure Point Spread Functions

Abstract-We address the problem of motion deblurring using coded exposure. This approach allows for accurate estimation of a sharp latent image via wellposed deconvolution and avoids lost image content that cannot be recovered from images acquired with a traditional shutter. Previous work in this area has used either manual user input or alpha matting approaches to estimate the coded exposure Point Spread Function (PSF) from the captured image. In order to automate deblurring and to avoid the limitations of matting approaches, we propose a Fourier-domain statistical approach to coded exposure PSF estimation that allows us to estimate the latent image in cases of constant velocity, constant acceleration, and harmonic motion. We further demonstrate that previously used criteria to choose a coded exposure PSF do not produce one with optimal reconstruction error, and that an additional 30 percent reduction in Root Mean Squared Error (RMSE) of the latent image estimate can be achieved by incorporating natural image statistics