罗布泊盐湖沉积物磁性特征及其环境意义

以罗布泊盐湖沉积物为研究对象,以环境磁学为主要研究方法,探讨罗布泊盐湖沉积物磁性特征的影响因素和环境意义.根据磁性特征可将罗布泊LOP1剖面沉积物分为两类:磁铁矿主导和铁硫化物(胶黄铁矿和黄铁矿)主导.磁铁矿主要来源于物源区塔里木盆地.胶黄铁矿和黄铁矿是早期还原成岩作用的产物,对磁性特征产生显著影响,胶黄铁矿主导层具有...     

BUILDING CLIMATE-RESILIENT FOOD SYSTEMS IN EAST AND SOUTHEAST ASIA: VULNERABILITIES,RESPONSES AND FINANCING

Food system resilience to climate change is uniquely imperative for bringing Sustainable Development Goals within reach and leaving no one behind. Food systems in East and Southeast Asia are interacting with planetary boundaries and are adversely affected by extreme weather-related events. A practical question for East and Southeast Asian stakeholders is how to foster climateresilient food systems in the face of lingering food system vulnerabilities and policy gaps. This paper reviews food syste...     

水稻拔节期和抽穗期低温对稻米品质影响

【目的】为了探讨低温冷害对水稻品质的影响,以便为气候变化背景下辽宁地区水稻的高效栽培和品质认证提供理论依据。【方法】以水稻90-35为供试品种,分别于拔节期和抽穗期通过TRP-1000D型人工智能气候箱进行不同低温处理(比外界低5℃和低3℃,均持续5 d),以正常栽培管理作对照,5个处理,分别记为CK、A1、A2、B1、B2,分析拔节期和抽穗期不同等级低温胁迫处理对水稻的营养品质、研磨品质和外观品质的影响。【结果】低温胁迫下90-35水稻的蛋白质含量、直链淀粉含量较CK显著增加,处理A1、A2、B1、B2的蛋白质含量分别比CK增加3.45%、1.15%、6.90%、8.05%,除处理B1外直链淀粉含量分别比CK增加1.75%、0.44%、0.44%;脂肪酸含量和精米率较CK显著减少,处理A1、A2、B1、B2的脂肪酸含量分别比CK减少17.75%、7.5%、25%、11%,精米率分别比CK减少2.1%、6.95%、0.22%、7.5%。拔节期低温胁迫下糙米率和垩白粒率显著减少,处理A1、A2的糙米率分别比CK减少0.73%、1.09%,垩白粒率分别比CK减少33.3%、22.2%;抽雄期低温胁迫下糙米率和垩白粒率显著增加,处理B1、B2的的糙米率分别比CK增加0.6%、3.02%,垩白粒率分别比CK增加255.6%、133.3%,即低温胁迫使其营养品质和研磨品质中的精米率受到负面影响,而拔节期适当低温胁迫处理对其研磨品质中的糙米率和外观品质有益。【结论】水稻90-35在不同生育时期经受不同低温胁迫处理后,水稻的营养品质、研磨品质和外观品质均受到不同程度的影响。     

Mapping the current and future distributions of Onosma species endemic to Iran

Climate change may cause shifts in the natural range of species especially for those that are geographically restricted and/or endemic species. In this study, the spatial distribution of five endemic and threatened species belonging to the genus Onosma (including O. asperrima, O. bisotunensis, O. kotschyi, O. platyphylla, and O. straussii) was investigated under present and future climate change scenarios: RCP2.6 (RCP, representative concentration pathway; optimistic scenario) and RCP8.5 (pessimistic scenario) for the years 2050 and 2080 in Iran. Analysis was conducted using the maximum entropy (MaxEnt) model to provide a basis for the protection and conservation of these species. Seven environmental variables including aspect, depth of soil, silt content, slope, annual precipitation, minimum temperature of the coldest month, and annual temperature range were used as main predictors in this study. The model output for the potential habitat suitability of the studied species showed acceptable performance for all species (i.e., the area under the curve (AUC)>0.800). According to the models generated by MaxEnt, the potential current patterns of the species were consistent with the observed areas of distributions. The projected climate maps under optimistic and pessimistic scenarios (RCP2.6 and RCP8.5, respectively) of 2050 and 2080 resulted in reductions and expansions as well as positive range changes for all species in comparison to their current predicted distributions. Among all species, O. bisotunensis showed the most significant and highest increase under the pessimistic scenario of 2050 and 2080. Finally, the results of this study revealed that the studied plant species have shown an acute adaptability to environmental changes. The results can provide useful information to managers to apply appropriate strategies for the management and conservation of these valuable Iranian medicinal and threatened plant species in the future.     

Performance and uncertainty analysis of a short-term climate reconstruction based on multi-source data in the Tianshan Mountains region,China

Short-term climate reconstruction, i.e., the reproduction of short-term(several decades) historical climatic time series based on the relationship between observed data and available longer-term reference data in a certain area, can extend the length of climatic time series and offset the shortage of observations. This can be used to assess regional climate change over a much longer time scale. Based on monthly grid climate data from a Coupled Model Inter-comparison Project phase 5(CMIP5) dataset for the period of 1850–2000, the Climatic Research Unit(CRU) dataset for the period of 1901–2000 and the observed data from 53 meteorological stations located in the Tianshan Mountains region(TMR) of China during the period of 1961–2011, we calibrated and validated monthly average temperature(MAT) and monthly accumulated precipitation(MAP) in the TMR using the delta, physical scaling(SP) and artificial neural network(ANN) methods. Performance and uncertainty during the calibration(1971–1999) and verification(1961–1970) periods were assessed and compared using traditional performance indices and a revised set pair analysis(RSPA) method. The calibration and verification processes were subjected to various sources of uncertainty due to the influence of different reconstructed variables, different data sources, and/or different methods used. According to traditional performance indices, both the CRU and CMIP5 datasets resulted in satisfactory calibrated and verified MAT time series at 53 meteorological stations and MAP time series at 20 meteorological stations using the delta and SP methods for the period of 1961–1999. However, the results differed from those obtained by the RSPA method. This showed that the CRU dataset produced a low degree of uncertainty(positive connection degree) during the calibration and verification of MAT using the delta and SP methods compared to the CMIP5 dataset. Overall, the calibrated and verified MAP had a high degree of uncertainty(negative connection degree) regardless of the dataset or reconstruction method used. Therefore, the reconstructed time series of MAT for the period of 1850(or 1901)–1960 based on the CRU and CMIP5 datasets using the delta and SP methods could be used for further study. The results of this study will be useful for short-term(several decades) regional climate reconstruction and longer-term(100 a or more) assessments of regional climate change.     

Impact of waterlogging and temperature on autumn growth,hardening and freezing tolerance of timothy (Phleum pratense)

Precipitation has generally increased in Norway during the last century, and climate projections indicate a further increase. The growing season has also become longer with higher temperatures, particularly in autumn. Previous studies have shown negative effects of high temperatures and, depending upon temperature conditions, contrasting effects of waterlogging on hardening capacity of timothy. We studied effects of waterlogging on seedlings of timothy (Phleum pratense, cv. Noreng) under three pre-acclimation temperatures: 3°C, 7°C, 12°C, and in autumn natural light in a phytotron at Holt, Tromsø (69°N). After temperature treatments, all plants were cold acclimated at 2°C for three weeks under continued waterlogging treatments. Freezing tolerance was determined by intact plants being frozen in pots at incremental temperature decreases in a programmable freezer. Waterlogging resulted in a higher probability of death after freezing, and a significantly reduced regrowth after three weeks at 18°C, 24 hrs light in a greenhouse. Increasing pre-acclimation temperatures also had a clear negative effect on freezing tolerance, but there was no interaction between temperatures and waterlogging. The results indicate that waterlogging may have negative implications for hardening of timothy and may contribute to reduced winter survival under the projected increase in autumn temperatures and precipitation.     

[CO2]升高对粮食作物影响的研究进展

自工业革命以来,由人类活动引起的大气CO_2浓度([CO_2])不断攀升,正驱动着全球气候变化,对全球农业产生重大影响。本文归纳总结了目前作物对高[CO_2]响应的主要研究技术手段,以及作物对高[CO_2]响应的机理研究,并进一步梳理了当前全球关于[CO_2]升高对作物产量和营养品质影响的研究。结果表明:相比封闭式或半封闭式环境控制试验系统,开放式试验系统(如开放式CO_2控制系统FACE)由于其能更加真实地模拟自然条件下作物对未来高[CO_2]的响应和适应情况,被公认为是目前研究作物对高[CO_2]响应的最理想手段。[CO_2]增高会增加C3作物光合速率、生物量和产量,在一定程度上缓解气候变化对农作物产生的负面影响,但是作物对大气[CO_2]的升高存在光合适应现象,当作物长期暴露在高[CO_2]条件下时,高[CO_2]对作物的促进作用会逐渐减缓。近10年的FACE试验发现,对高[CO_2]出现高应答的水稻品种,其光合速率和产量在高[CO_2]下的增加幅度比早期的主要粮食作物FACE试验结果平均高出两倍。此外,高[CO_2]会明显降低大部分非豆科C3作物中蛋白质和矿物质(如锌、铁)以及部分维生素的含量,加剧目前全球约2亿人由于维生素和矿物质元素等营养缺乏导致的健康问题。如何充分利用未来高[CO_2]实现高增产的同时,减缓粮食养分下降的负面影响,是迫切需要解决的科学问题。     

气候变暖对我国水稻生产的综合影响及其应对策略

气候变暖已是不争的事实,预计到21世纪末全球地表平均气温仍将上升1.5℃以上。水稻是我国最重要的口粮作物,全国80%以上的人口以稻米为主食,探明气候变暖对我国“口粮绝对安全”的潜在影响意义重大。作者依据多年的田间增温试验及长期观察,并结合国内外现有研究,阐明了我国粮食主产区气候变暖的基本态势,总结发现温度升高1.5℃对我国水稻生产的潜在影响正负参半,并取决于具体的稻作季节和地区。但是,随着水稻种植制度调整,尤其是南方双季稻种植面积下降,温度升高对我国水稻生产的负面影响将逐步递增。最后,作者提出了应对气候变暖的气候智慧型稻作技术创新建议,并展望了该研究领域的重点内容和方向。     

夏玉米产量时空变化及气候年型分析

定量分析夏玉米同一品种产量及产量构成要素时空变化,探讨造成产量时空差异的气候年型组合变化特征。基于2004～2013年夏玉米种植区郑单958多点田间试验数据分析表明,夏玉米区域平均产量为9 055 kg/hm~2,年际差为1 635 kg/hm~2,变幅为18.1%;地点差4 258 kg/hm~2,变幅为47.1%。夏玉米区域平均千粒重为313 g,年际变幅为13.1%;地点变幅为27.8%。夏玉米区域平均穗粒数为479,年际变幅为18.0%,地点变幅为38.7%。千粒重的增加导致夏玉米产量显著增加。穗粒数显著降低和时空差异大是造成产量波动和时空差异变幅大的主要原因。夏玉米产量和产量构成要素,低产点受各气候要素变化的影响显著;平产点受温度和日最高温度大于33℃的天数影响显著;高产点受降水影响显著。     

基于CLIMEX和ArcGIS的灰茶尺蠖在中国的潜在适生区预测

基于中国820个气象站点数据、灰茶尺蠖已知地理分布资料和生物学数据,结合CLIMEX模型与ArcGIS软件模拟预测灰茶尺蠖在中国目前及未来的潜在分布范围,评估灰茶尺蠖的潜在分布范围及气候变暖对其分布的潜在影响。结果表明,灰茶尺蠖在中国目前的适生区主要分布在3°51′N~40°6′N,适生区面积占全国总面积的34.27%。中国大部分省份的气候条件均适合灰茶尺蠖种群生长。随着气候的变化,灰茶尺蠖在中国的潜在适生区面积总体增幅不大,但其组成变化较大。至2050年,预测灰茶尺蠖高度适生区面积占比达最大（22.23%）。相比各种能源之间的平衡情景（A1B）,较高能源需求情景（A2）下灰茶尺蠖在陕甘宁地区向内陆扩张更快。灰茶尺蠖在中国适生区分布广泛,应加强灰茶尺蠖预测预报,趁早采取防控措施,以保障茶叶优质安全生产。