共生视角下经济发达地区初生型城中村更新策略探析——以深汕特别合作区赤石村为例

在新型城镇化与乡村振兴双轮驱动的背景下,许多发达地区城郊村呈现向城中村转变的态势,发展矛盾日益凸显。文章以深汕特别合作区赤石村为例,基于城村共生的理念,从人居环境共宜、配套设施共享、民生福祉共增、历史文化共扬、产业经济共兴五个方面,探讨如何在其初生阶段通过规划的介入与引导,保护和传承村庄历史文化,实现未来与城市和谐共生,同时为其他经济发达地区初生型城中村更新提供借鉴。

初生型创业者职业选择研究:基于自我效能的观点

为什么现实中有人会选择创业,而其他人则选择就业,这是创业研究领域一直关注的问题。学者们从不同的角度进行了大量理论和实证研究,但一直未获得公认的结论。运用社会心理学家Bandura的自我效能观点和三元交互理论,从自我效能与创业环境互动的角度考察初生型创业者的职业选择,为该领域的实证研究提供理论基础。

模型水轮机初生空化的特征谱提取识别方法

目前水轮机模型初生空化识别方法仍采用人工识别法,这种方法获得结果的周期较长、主观性强、准确度及效率都较低。针对于此,对水轮机模型初生空化识别方法进行了研究与优化创新,提出了一种基于炮声谱与特殊脉动谱特征提取的水轮机空化智能识别方法,即多态泡音智能识别方法 PSVFR。该方法依据自主开发的水轮机空化噪声多态算法MTCSPC,对数据进行处理,通过采集初生空化音态特征向量,建立矩阵模型,与样本数据库中的定性矩阵进行特征比对、计算、判断,以帮助机器完成对模型水轮机空化噪声的学习和识别。与现有技术相比,该方法能够提高机器对水轮机初生空化现象的识别准确度和识别效率,识别效率可达80%。

中国绿岩带型金矿床类型和地质特征

绿岩带型金矿床是世界和我国重要的金矿床类型。中国绿岩带型金矿床可分为同构造晚期初生型金矿床和构造期后再生型金矿床二类。这二类金矿床的成矿地质环境、矿床地质地球化学均具有明显的不同。同构造晚期初生型金矿床不仅产出在中低变质绿岩带中，而且在高级变质绿岩带中分布很普遍和发育。构造期后再生型热液金矿床分布广泛、储量极大，是具有中国地质特色的金矿床类型。

华北陆块北缘中段金矿床类型分布和地质特征

华北陆块北缘中段金矿床分布较多 ,产出较为集中 ,也是我国重要的黄金生产基地。本区是我国地质历史演化最久 ,又经历了多期、多阶段的构造 -岩浆活动的叠加 ,因而金矿床的形成具有多期性、多样性、继承性和新生性等特点。本区金矿床主要产在绿岩带构造 -岩浆活动的有利部位 ,统称绿岩带型金矿床 ,可分为同构造晚期初生型金矿床和构造期后再生型热液金矿床两类 ,又可细分为若干亚类 ,其中再生型热液金矿床分布较广 ,规模较大 ,具有较大找矿潜力。由于区内发育多期、多阶段的成矿构造 -岩浆活动 ,因而在一个金矿床密集区内 ,有时在不同的构造部位 ,分别产出初生型和再生型热液金矿床 。

铸造镍基高温合金重熔工艺的研究进展

重熔浇注工艺对浇注试棒的力学性能影响较大。本文归纳了重熔浇注工艺过程中不同熔体处理温度、时间和浇注温度、模壳温度、砂箱温度及浇注试棒形状对浇注试棒显微组织的影响,并分析了原因。对未来重熔铸造镍基合金浇注试棒的重点关注方向进行了展望。

GIS-based Analysis for Hotspot Identification of Tradeoff Between Ecosystem Services： A Case Study in Yanhe Basin, China

Although the quantification and valuation of ecosystem services have been studied for a long time, few studies have specifi- cally focused on the quantification of tradeoffs between ecosystem services and tradeoff hotspots, Based on previous studies of ecosys- tem service assessment, we proposed a feasible method to analyze the tradeoffs between ecosystem services, including determination of their relationship, quantification of tradeoffs, and identification of tradeoff hotspots. Potential influencing factors were then further ana- lyzed. The Yanhe Basin in the Loess Plateau was selected as an example to demonstrate the application process. Firstly, the amounts of net primary production （NPP） and water yield （WY） in 2000 and 2008 were estimated by using biophysical models, Secondly, correla- tion analysis was used to indicate the tradeoffs between NPP and WY. Thirdly, tradeoff index （TINpp/wy） was established to quantify the extent of tradeoffs between NPP and WY, and the average value of TINpp/wy is 24.4 g/（mm·m2） for the Yanhe Basin between 2000 and 2008. Finally, the tradeoff hotspots were identified. The results indicated that the area of lowest tradeoff index concentrated in the mid- dle part of the Yanhe Basin and marginal areas of the southern basin. Map overlapping was used for preliminary analysis to seek poten- tial influencing factors, and the results showed that shrub was the best suited for growing in the Yanhe Basin, but also was a potential irtfluencing factor for formulatiort of the tradeoff hotspots. The concept of tradeoff index could also be used to quantify the degree of synergy between different ecosystem services. The method to identify the tradeoff hotspots could help us to narrow the scope of study area for further research on the relationship among ecosystem services and concentrate on the potential factors for formation of tradeoff between ecosystem services, enhance the capacity to maintain the sustainability of ecosystem.

Vegetation NPP Distribution Based on MODIS Data and CASA Model——A Case Study of Northern Hebei Province

Net Primary Productivity （NPP） is one of the important biophysical variables of vegetation activity, and it plays an important role in studying global carbon cycle, carbon source and sink of ecosystem, and spatial and temporal distribution of CO2. Remote sensing can provide broad view quickly, timely and multi-temporally, which makes it an attractive and powerful tool for studying ecosystem primary productivity, at scales ranging from local to global. This paper aims to use Moderate Resolution Imaging Spectroradiometer （MODIS） data to estimate and analyze spatial and temporal distribution of NPP of the northern Hebei Province in 2001 based on Carnegie-Ames-Stanford Approach （CASA） model. The spatial distribution of Absorbed Photosynthetically Active Radiation （APAR） of vegetation and light use efficiency in three geographical subregions, that is, Bashang Plateau Region, Basin Region in the northwestern Hebei Province and Yanshan Mountainous Region in the Northern Hebei Province were analyzed, and total NPP spatial distribution of the study area in 2001 was discussed. Based on 16-day MODIS Fraction of Photosynthetically Active Radiation absorbed by vegetation （FPAR） product, 16-day composite NPP dynamics were calculated using CASA model; the seasonal dynamics of vegetation NPP in three subreglons were also analyzed. Result reveals that the total NPP of the study area in 2001 was 25.1877 × 10^6gC/（m^2.a）, and NPP in 2001 ranged from 2 to 608gC/（m^2-a）, with an average of 337.516gC/（m^2.a）. NPP of the study area in 2001 accumulated mainly from May to September （DOY 129-272）, high NIP values appeared from June to August （DOY 177-204）, and the maximum NPP appeared from late July to mid-August （DOY 209-224）.

Effect of climate change on seasonal water use efficiency in subalpine Abies fabri

Abies fabri is a typical subalpine dark coniferous forest in southwestern China. Air temperature increases more at high elevation areas than that at low elevation areas in mountainous regions,and climate change ratio is also uneven in different seasons. Carbon gain and the response of water use efficiency(WUE) to annual and seasonal increases in temperature with or without CO_2 fertilization were simulated in Abies fabri using the atmospheric-vegetation interaction model(AVIM2). Four future climate scenarios(RCP2.6,RCP4.5,RCP6.0 and RCP8.5) from the Coupled Model Intercomparison Project Phase 5(CMIP5) were selectively investigated. The results showed that warmer temperatures have negative effects on gross primary production(GPP) and net primary production(NPP) in growing seasons and positive effects in dormant seasons due to the variation in the leaf area index. Warmer temperatures tend to generate lower canopy WUE and higher ecosystem WUE in Abies fabri. However,warmer temperature together with rising CO_2 concentrations significantlyincrease the GPP and NPP in both growing and dormant seasons and enhance WUE in annual and dormant seasons because of the higher leaf area index(LAI) and soil temperature. The comparison of the simulated results with and without CO_2 fertilization shows that CO_2 has the potential to partially alleviate the adverse effects of climate warming on carbon gain and WUE in subalpine coniferous forests.

Sensitivity of the Terrestrial Ecosystem to Precipitation and Temperature Variability over China

In this study, the sensitivities of net primary production(NPP), soil carbon, and vegetation carbon to precipitation and temperature variability over China are discussed using the state-of-the-art Lund-Potsdam-Jena dynamic global vegetation model(LPJ DGVM). The impacts of the sensitivities to precipitation variability and temperature variability on NPP, soil carbon, and vegetation carbon are discussed. It is shown that increasing precipitation variability, representing the frequency of extreme precipitation events, leads to losses in NPP, soil carbon, and vegetation carbon over most of China, especially in North and Northeast China where the dominant plant functional types(i.e., those with the largest simulated areal cover) are grass and boreal needle-leaved forest. The responses of NPP, soil carbon, and vegetation carbon to decreasing precipitation variability are opposite to the responses to increasing precipitation variability. The variations in NPP, soil carbon, and vegetation carbon in response to increasing and decreasing precipitation variability show a nonlinear asymmetry. Increasing precipitation variability results in notable interannual variation of NPP. The sensitivities of NPP, soil carbon, and vegetation carbon to temperature variability, whether negative or positive, meaning frequent hot and cold days, are slight. The present study suggests, based on the LPJ model, that precipitation variability has a more severe impact than temperature variability on NPP, soil carbon, and vegetation carbon.

Satellite-based Estimation of Gross Primary Production in an Alpine Swamp Meadow on the Tibetan Plateau:A Multi-model Comparison

Alpine swamp meadows on the Tibetan Plateau,with the highest soil organic carbon content across the globe,are extremely vulnerable to climate change.To accurately and continually quantify the gross primary production（GPP） is critical for understanding the dynamics of carbon cycles from site-scale to global scale.Eddy covariance technique（EC） provides the best approach to measure the site-specific carbon flux,while satellite-based models can estimate GPP from local,small scale sites to regional and global scales.However,the suitability of most satellite-based models for alpine swamp meadow is unknown.Here we tested the performance of four widely-used models,the MOD17 algorithm（MOD）,the vegetation photosynthesis model（VPM）,the photosynthetic capacity model（PCM）,and the alpine vegetation model（AVM）,in providing GPP estimations for a typical alpine swamp meadow as compared to the GPP estimations provided by EC-derived GPP.Our results indicated that all these models provided good descriptions of the intra-annual GPP patterns（R〉20.89,P〈0.0001）,but hardly agreed with the inter-annual GPP trends.VPM strongly underestimated the GPP of alpine swamp meadow,only accounting for 54.0% of GPP_EC.However,the other three satellite-based GPP models could serve as alternative tools for tower-based GPP observation.GPP estimated from AVM captured 94.5% of daily GPP_EC with the lowest average RMSE of 1.47 g C m^（-2）.PCM slightly overestimated GPP by 12.0% while MODR slightly underestimated by 8.1% GPP compared to the daily GPP_EC.Our results suggested that GPP estimations for this alpine swamp meadow using AVM were superior to GPP estimations using the other relatively complex models.

A CLASSIFICATION INDICES-BASED MODEL FOR NET PRIMARY PRODUCTIVITY （NPP） AND POTENTIAL PRODUCTIVITY OF VEGETATION IN CHINA

Net Primary Productivity （NPP） is an important parameter, which is closely connected with global climate change, the global carbon balance and cycle. The study of climate- vegetation interaction is the basis for research on the responses of terrestrial ecosystemto global change and mainly comprises two important components： climate vegetation classification and the NPP of the natural vegetation. Comparing NPP estimated from the classification indices-based model with NPP derived from measurements at 3767 sites in China indicated that the classification indices-based model was capable of estimating large scale NPP. Annual cumulative temperature above 0~C and a moisture index, two main factors affecting NPP, were spatially plotted with the ArcGIS grid tool based on measured data in 2348 meteorological stations from 1961 to 2006. The distribution of NPP for potential vegetation classes under present climate conditions was simulated by the classification indices-based model. The model estimated the total NPP of potential terrestrial vegetation of China to fluctuate between 1.93 and 4.54 Pg C year-1. It pro- vides a reliable means for scaling-up from site to regional scales, and the findings could potentially favor China＇s position in reducing global warming gases as outlined in the Kyoto Protocol in order to fulfill China＇s commitment of reducing greenhouse gases.

Improvement of predicting ecosystem productivity by modifying carbon-water-nitrogen coupling processes in a temperate grassland

Aims Prediction of changes in ecosystem gross primary productivity(GPP)in response to climatic variability is a core mission in the field of global change ecology.However,it remains a big challenge for the model community to reproduce the interannual variation(IAV)of GPP in arid ecosystems.Accurate estimates of soil water content(SWC)and GPP sensitivity to SWC are the two most critical aspects for predicting the IAV of GPP in arid ecosystems.Methods We took a widely used model Biome-BGC as an example,to improve the model performances in a temperate grassland ecosystem.Firstly,we updated the estimation of SWC by modifying modules of evapotrainspiration,SWC vertical profile and field capacity.Secondly,we modified the function of controlling water-nitrogen relation,which regulates the GPP-SWC sensitivity.Important Findings The original Biome-BGC overestimated the SWC and underestimated the IAV of GPP sensitivity,resulting in lower IAV of GPP than the observations,e.g.it largely underestimated the reduction of GPP in drought years.In comparison,the modified model accurately reproduced the observed seasonal and IAVs in SWC,especially in the surface layer.Simulated GPP-SWC sensitivity was also enhanced and became closer to the observations by optimizing parameter controlling nitrogen mineralization.Consequently,the model's capability of reproducing IAV of GPP has been largely improved by the modifications.Our results demonstrate that SWC in the surface layer and the consequent effects on nitrogen availability should be among the first considerations for accurate modeling IAV of GPP in arid ecosystems.