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1.
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.  相似文献   
2.
王涵  赵文武  刘月  刘焱序 《地理研究》2019,74(4):831-843
森林病虫害由于在森林资源中造成的重大破坏而被人们称为“不冒烟的火灾”,其对生态系统的研究具有重要意义。现有基于遥感数据的病虫害研究多集中在森林病虫害的监测、爆发原因以及发病区域内生产力的变化情况,而对于森林病虫害发生后森林中植被指数与叶面积指数之间的相关性的变化情况还相对较少,处于需要持续性深入探讨的阶段。以加拿大不列颠哥伦比亚地区2002—2012年森林病虫害数据为基础,分析不同严重程度的病虫害对叶面积指数(LAI)与归一化植被指数(NDVI)和增强型植被指数(EVI)的影响。结果表明:① 受病虫害感染的像元在轻度(Light)、中度(Moderate)和重度(Severe)三个严重级别中,NDVI与LAI之间的相关性由弱变强,又由强变弱;② EVI与LAI之间的相关性,在轻度(Light)、中度(Moderate)和重度(Severe)三个严重级别的像元中则依次变强。这一研究将为今后利用遥感数据识别病虫害、评价生态系统影响提供基础。  相似文献   
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曹贤忠  曾刚 《热带地理》2019,39(3):472-478
创新是引领经济发展的第一动力,创新与区域增长之间的关系成为经济地理学者关注的重点领域。文章通过梳理近年来有关创新网络测度、创新网络与区域增长关系、创新网络作用于区域增长方式等方面的文献发现:网络资本可以弥补社会资本在解释企业创新结网经济价值方面的不足,区域增长呈现出网络化特征已成为学界共识,知识流与邻近性能较好地解释创新网络与区域增长的关系机理。然而,当前研究对社会资本如何促进区域增长,网络资本与区域增长关系模型如何构建,不同类型的邻近性与知识对区域增长的影响有何差异等问题尚不明确,建议重视网络资本对区域增长的作用并实证检验二者的关联,同时还应重视创新网络中企业家精神、创新个体心理行为特征等因素对区域增长的影响。  相似文献   
5.
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.  相似文献   
6.
生态水文学是20世纪90年代兴起的一门研究生态过程和生态格局水文机制的新学科。湖泊湿地作为中国5大类天然湿地类型之一,湖泊湿地生态水文学的研究业已成为生态水文学的研究的一个重要对象和分支,其核心在于研究湖泊湿地生态系统中多时空尺度的水文与生物格局、过程的耦合特征及其相互作用。论文首先概述了湖泊湿地生态水文学的研究进展,包括其基本理论,内涵、外延及其主要研究内容;凝练了湖泊湿地生态水文学研究的方法体系、思路及框架;针对目前湖泊湿地生态水文学研究中存在的问题及薄弱环节,提出了湖泊湿地生态水文学未来研究的发展趋势和亟需加强研究的重点方向。在此基础上,以长江中游的典型通江湖泊湿地——鄱阳湖湿地为例,通过开展的湖泊湿地生态水文过程与模拟研究的典型案例,阐述了鄱阳湖湖泊湿地生态水文过程的变化及其植被响应研究的最新进展和研究成果。论文对于构建涵盖湖泊湿地水资源、湖泊湿地生态景观格局与流域管理、湖泊湿地生物多样性保育以及湿地资源可持续利用与生态管理等方向在内的战略研究体系,完善湿地生态水文研究与流域生态与管理的技术支撑体系具有重要的指导意义和实践价值。  相似文献   
7.
The formal opportunity to learn geography in the United States is unevenly distributed across space, creating possible geography deserts. Data on the number of exams taken in Advanced Placement Human Geography (APHG) and bachelor’s degrees earned in geography are mapped at the state and regional scales. Normalized rates are ranked and grouped into quintiles. For APHG exams, states in the southeastern region of the United States are in the uppermost quintiles while states in the northeastern region are in the lowermost quintiles. The pattern for bachelor’s degrees in geography is somewhat the spatial inverse of that for APHG.  相似文献   
8.
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.  相似文献   
9.
Kiacatoo Man, a large, rugged Aboriginal adult buried in the Lachlan riverine plains of southeastern Australia, was discovered in 2011. Laser‐ablation uranium series analysis on bone yielded a minimum age for the burial of 27.4 ± 0.4 ka (2σ). Single‐grain, optically stimulated luminescence ages on quartz sediment in which the grave had been dug gave a weighted mean age of 26.4 ± 1.5 ka (1σ). Luminescence samples from the grave infill and from sediment beneath the grave exhibit overdispersed dose distributions consistent with bioturbation or other disturbance, which has obscured the burial signal. The overlap between the minimum (U‐series) and maximum (luminescence) ages places the burial between 27.0 and 29.4 ka (2σ). Luminescence ages obtained from the channel belt of between 28 ± 2 and 25 ± 3 ka indicate that fluvial sedimentation was occurring before the Last Glacial Maximum, which is consistent with the broader geomorphic setting. Together, these results are internally and regionally consistent, and indicate that Kiacatoo Man was one of the more ancient individuals so far identified in Australia. His remains are important to our understanding of patterns of biological variation and other processes that have shaped people in the Murray‐Darling Basin through time. Copyright © 2019 John Wiley & Sons, Ltd.  相似文献   
10.
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