A biodiversity evaluation framework for restoration of aquatic macrophyte communities in shallow lakes driven by hydrological process management

Macrophyte community diversity and composition respond to ecosystem conservation and local environmental factors. In this study, we developed a multidimensional diversity framework for macrophyte communities, including the taxonomic and functional alpha and beta diversity. We used the framework to explore the relationships among water level regimes and these diversity parameters in a case study of China's Baiyangdian Lake. Analysis of indicators of hydrologic alteration divided the water level from 1959 to 2019 into four regimes (dry, <6.42 m; low, 6.42–7.23 m; medium, 7.23–8.19 m; high, >8.19 m). Alpha and beta diversity were significantly higher in the medium regime than in the low and high regimes. Redundancy analysis indicated that the maximum water depth significantly affected taxonomic alpha diversity, and total nitrogen (TN) and chemical oxygen demand (COD) concentration significantly affected functional alpha diversity, respectively. Mantel tests showed that TN, Secchi depth (SD), and water depth in the high water level regime significantly increased the total beta diversity and turnover components. TN was the main factor that increased total taxonomic beta diversity. Water level regime mainly influenced interspecific relationships by changing the TN and COD concentration. The water level should be maintained between the medium and high water level regimes to promote restoration of the macrophyte community and improve ecosystem stability. The biodiversity evaluation framework would provide a deeper insight into the hydrological process management for restoration of aquatic macrophyte communities in shallow lakes.     

A New Retracking Technique for Brown Peaky Altimetric Waveforms

A new Brown-Peaky (BP) retracker has been developed for peaky waveforms that usually appear within ～10 km to the coastline. The main feature of the BP is that it fits peaky waveforms using the Brown model without introducing a peak function. The retracking strategy first detects the peak location and width of a waveform using an adaptive peak detection method, and then estimates retracking parameters using a weighted least squares (WLS) estimator. The WLS assigns a downsized weight to corrupted waveform gates, but an equal weight to other normal waveform gates. The BP retracker has been applied to 4-year Jason-1 waveform (2002–2006) in two Australian coastal zones. The results retracked by BP, MLE4 and ALES retrackers have been validated against tide-gauge observations located at Burnie, Lorne and Broome. The comparison results show that three retrackers have similar performance over open oceans with the correlation coefficient (～0.7) and RMSE (～13 cm) between altimetric and tide-gauge sea levels for distance >7 km offshore. The main improvement of BP retracker occurs for distance ≤7 km to the coastline, where validation results indicate that data retracked by BP are more accurate (15–21 cm) than those by ALES (16–24 cm) and MLE4 (19–37 cm).     

Modelling the effect of changing precipitation inputs on deep soil water utilization

Forests in the Southeastern United States are predicted to experience future changes in seasonal patterns of precipitation inputs as well as more variable precipitation events. These climate change‐induced alterations could increase drought and lower soil water availability. Drought could alter rooting patterns and increase the importance of deep roots that access subsurface water resources. To address plant response to drought in both deep rooting and soil water utilization as well as soil drainage, we utilize a throughfall reduction experiment in a loblolly pine plantation of the Southeastern United States to calibrate and validate a hydrological model. The model was accurately calibrated against field measured soil moisture data under ambient rainfall and validated using 30% throughfall reduction data. Using this model, we then tested these scenarios: (a) evenly reduced precipitation; (b) less precipitation in summer, more in winter; (c) same total amount of precipitation with less frequent but heavier storms; and (d) shallower rooting depth under the above 3 scenarios. When less precipitation was received, drainage decreased proportionally much faster than evapotranspiration implying plants will acquire water first to the detriment of drainage. When precipitation was reduced by more than 30%, plants relied on stored soil water to satisfy evapotranspiration suggesting 30% may be a threshold that if sustained over the long term would deplete plant available soil water. Under the third scenario, evapotranspiration and drainage decreased, whereas surface run‐off increased. Changes in root biomass measured before and 4 years after the throughfall reduction experiment were not detected among treatments. Model simulations, however, indicated gains in evapotranspiration with deeper roots under evenly reduced precipitation and seasonal precipitation redistribution scenarios but not when precipitation frequency was adjusted. Deep soil and deep rooting can provide an important buffer capacity when precipitation alone cannot satisfy the evapotranspirational demand of forests. How this buffering capacity will persist in the face of changing precipitation inputs, however, will depend less on seasonal redistribution than on the magnitude of reductions and changes in rainfall frequency.     

A New Wavelet Method for Identification of Eddies and Assessment of Incidents on Islands of the Eastern Caribbean

Using two dimensional continuous wavelet transforms, a novel method for identification of mesoscale eddies is presented to facilitate extraction of characteristics for area, amplitude, type, and location from maps of sea level anomalies. In comparison with the previously established growing method for eddy identification, it is found that the wavelet method identifies more than twice the number of eddies and is particularly better at resolving small eddies down to the 0.25 degree resolution of the data. Such research into eddy identification and tracking is significant to the assessment of eddies with potential to impact on coastlines of small islands. The method is applied to the identification of eddies on tracks towards islands of the Eastern Caribbean over 23?years. Spatial and temporal variation in rate of occurrence and magnitude is established. For Barbados there is an average of 9 anticyclonic incidents a year with maximum amplitude of typically 0.22?m in the dry seasons and 0.16?m in the wet seasons. Seasonal variation is reversed for the other islands with twice the number of anticyclonic incidents having maximum amplitudes of about 0.20?m annually.     

Implementation of a coupled plastic damage distinct lattice spring model for dynamic crack propagation in geomaterials

This paper studies dynamic crack propagation by employing the distinct lattice spring model (DLSM) and 3‐dimensional (3D) printing technique. A damage‐plasticity model was developed and implemented in a 2D DLSM. Applicability of the damage‐plasticity DLSM was verified against analytical elastic solutions and experimental results for crack propagation. As a physical analogy, dynamic fracturing tests were conducted on 3D printed specimens using the split Hopkinson pressure bar. The dynamic stress intensity factors were recorded, and crack paths were captured by a high‐speed camera. A parametric study was conducted to find the influences of the parameters on cracking behaviors, including initial and peak fracture toughness, crack speed, and crack patterns. Finally, selection of parameters for the damage‐plasticity model was determined through the comparison of numerical predictions and the experimentally observed cracking features.     

基于纹理特征的围填海SAR图像分水岭分割方法

本文提出了一种基于纹理特征的围填海SAR图像分水岭分割方法,首先对机载MiniSAR图像进行灰度共生矩阵纹理滤波,获得纹理特征图像,再对纹理特征图像进行分水岭算法分割,将获得的形态学重建图像进行门限阈值分割,得到最后的二值化分割结果。该方法一方面通过调整灰度共生矩阵纹理滤波的窗口大小,抑制了斑点噪声的影响;另一方面,利用分水岭算法对边缘模糊杂乱图像的优势,提高了围填海信息提取的准确性。实验结果表明,本方法对高分辨率SAR图像围填海监测图像的分割效果良好。     

Properties of geogrid-reinforced marine slope due to the groundwater level changes

Evaluation of slope stability, especially in the absence of a proper bed such as marine soils, is one of the most important issues in geotechnical engineering. Using geogrid layers to enhance the strength and stability of embankments is regarded as a commendable stabilization method. On the other hand, groundwater level erratically fluctuates in coastal areas. Therefore, the aim of this research is to study the effects of groundwater level changes on stability of a geogrid-reinforced slope on loose marine soils in Qeshm Island, Iran. At first, geotechnical properties of the site were obtained by comprehensive series of geotechnical laboratory and in situ tests. Then, by simultaneous changes of groundwater level and several parameters such as embankment slope, loading, geogrid length, geogrid number, and tensile strength of geogrid, different characteristics such as embankment safety factor (SF), vertical and horizontal displacements at embankment top and embankment base were studied. It was observed that groundwater level had significant effects on behavior of the embankment. For most of the observations, by decreasing the groundwater level, the displacements decreased and consequently safety factor increased. Increasing the length, number, and tensile strength of geogrid led to the reduction of displacements and an increase in the safety factor.     

Developing constitutive models from EPR‐based self‐learning finite element analysis

A constitutive model that captures the material behavior under a wide range of loading conditions is essential for simulating complex boundary value problems. In recent years, some attempts have been made to develop constitutive models for finite element analysis using self‐learning simulation (SelfSim). Self‐learning simulation is an inverse analysis technique that extracts material behavior from some boundary measurements (eg, load and displacement). In the heart of the self‐learning framework is a neural network which is used to train and develop a constitutive model that represents the material behavior. It is generally known that neural networks suffer from a number of drawbacks. This paper utilizes evolutionary polynomial regression (EPR) in the framework of SelfSim within an automation process which is coded in Matlab environment. EPR is a hybrid data mining technique that uses a combination of a genetic algorithm and the least square method to search for mathematical equations to represent the behavior of a system. Two strategies of material modeling have been considered in the SelfSim‐based finite element analysis. These include a total stress‐strain strategy applied to analysis of a truss structure using synthetic measurement data and an incremental stress‐strain strategy applied to simulation of triaxial tests using experimental data. The results show that effective and accurate constitutive models can be developed from the proposed EPR‐based self‐learning finite element method. The EPR‐based self‐learning FEM can provide accurate predictions to engineering problems. The main advantages of using EPR over neural network are highlighted.     

西部省会城市新型城镇化水平综合测度研究

新型城镇化是中国经济社会健康、稳定、可持续发展的根本途径，也是消除中国城乡社会经 济“二元结构”的根本出路。根据新型城镇化的内涵，从人口、经济发展、生活质量、基础设施、资源 环境、城乡统筹 6 个方面，构建了包含 24 项指标的新型城镇化水平综合测度指标体系，运用熵值 法，对西部 11 个省会城市 2005—2015 年的城镇化发展水平进行综合测度。研究表明：西部省会城 市的新型城镇化发展水平存在显著差异，总体上可分为“高等水平”、“中等水平”、“低等水平”3 个 级别，基础设施建设是造成差异的主要驱动力。各城市不同年间的城镇化单项水平影响因素呈现 多元化，在今后的发展中应有所侧重，同时兼顾城镇化质量，推动城乡协调发展。