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1.
两种常用的春玉米干旱等级指标在东北区域的适用性检验 总被引:7,自引:2,他引:5
作物干旱等级农业气象指标是开展作物干旱监测、预警和评估的科学依据,其适用性关系到农业气象业务质量和防灾减灾效果。应用东北地区31个春玉米代表站1981 2012年逐日气象资料、土壤湿度资料、玉米生长发育观测资料及产量资料,对东北地区春玉米不同生育阶段土壤相对湿度和水分亏缺指数干旱等级指标进行应用效果检验,比较适用性差异,并利用黑龙江省2013—2016年的资料进行干旱实例分析。结果表明,土壤湿度和水分亏缺指数干旱等级指标均适用于东北区域春玉米不同生长时段干旱识别,且有较好的一致性,两套指标判定的春玉米干旱等级完全相同的占75%左右,相同和基本相同的占95%左右。两套指标对历史和近年玉米干旱事件的判识效果都较好,土壤湿度指标和水分亏缺指数的干旱识别正确率分别在80%和75%以上,土壤湿度指标判定效果更好。两套指标在玉米生育前、中期适宜性略好于后期,在东北三省的应用效果要略好于内蒙古东部,部分干旱年份水分亏缺指数判定的灾情等级偏高,因此,当两套指标判定结果存在差异时,应以土壤相对湿度指标判定结果为准。 相似文献
2.
干旱是安徽冬小麦生育期主要的农业气象灾害,对冬小麦生长发育及产量形成均产生重要的影响。利用安徽冬麦区36个气象站1960—2012年逐日气象资料,分别计算冬小麦生育期水分亏缺指数(CWDI)、综合气象干旱指数(CI)、标准化降水指数(SPI)及降水距平百分率(PA),并对上述指标的计算结果进行对比及适应性分析。结果表明:冬小麦生育期4种干旱指数得到的干旱日数时空分布趋势基本一致;SPI和PA指数计算相对简便、稳定,但由于没有考虑水分支出,易导致干旱程度跳跃发展,CI和CWDI指数考虑了最近的降水情况,同时考虑了水分的收支情况,能够较好的表征干旱过程发生发展机制及特征,尤其是CWDI考虑到生长季作物系数,能反映作物水分亏缺状况。通过干旱指数与冬小麦减产率的相关分析,进一步表明CWDI在安徽冬小麦生育期干旱监测评估中具有较好的适用性。 相似文献
3.
水分盈亏指数及其在农业干旱监测中的应用 总被引:8,自引:2,他引:6
从广西农业生产特点和农业干旱监测业务实际需要出发,建立了综合考虑降雨量和同期作物需水量的基于作物水分盈亏原理的农业干旱监测模型,农业干旱监测等级指标,并用该指标分析了1961~2004年广西双季稻、甘蔗生育期间农业干旱的时空分布特征,用自治区农业厅同期旱情资料对2004年广西春旱发生情况进行监测验证.结果表明:水分盈亏指数模型能很好地反映旱情,是一种实时的干旱监测方法,能运用于广西农业干旱监测业务,在每季干旱发生频率的时空分布特征分析上得到的结果也与实际情况相符合. 相似文献
4.
干旱是威胁我国及世界农业发展的自然灾害之一,遥感技术已经成为监测土壤水分与旱情的重要手段.本文总结分析了近年来利用MODIS资料监测土壤水分和干旱的优势,回顾总结了目前利用MODIS资料监测土壤水分和干旱的方法,着重从光谱法、温度植被干旱指数(TVDI)空间法、植被水分监测法、水分亏缺指数法等几方面进行了重点介绍,并对未来的发展趋势进行了展望. 相似文献
5.
利用多源遥感数据和稻谷产量资料,采用降水条件指数(PCI)、植被状态指数(VCI)、土壤湿度条件指数(SMCI)、温度条件指数(TCI)以及优化植被干旱指数(OVDI)5种干旱指数,对2012、2013年长江中下游五省的干旱状况进行监测。在此基础上,以标准化降水指数(SPI)和作物减产率为参考指标,对上述5种干旱指数在该区域的适用性进行比较分析。结果表明:PCI、SMCI更适用于长江中下游五省的干旱监测,而TCI、VCI不适合单独用于该地区的干旱监测。在2012、2013年,长江中下游五省发生了不同程度的干旱,其中2013年的干旱更严重(波及范围更广、发生频次更高)。2012年的干旱主要集中在1—2月和6—8月两个时段,旱区集中在北部地区,大致呈东北—西南向的条带状分布;而2013年的干旱主要集中在1—2月和6—10月两个时段,旱区覆盖了整个研究区域,且各省旱情不同步。 相似文献
6.
MODIS资料遥感监测土壤水分与干旱研究进展 总被引:5,自引:0,他引:5
干旱是威胁我国及世界农业发展的自然灾害之一,遥感技术已经成为监测土壤水分与旱情的重要手段。本文总结分析了近年来利用MOD IS资料监测土壤水分和干旱的优势,回顾总结了目前利用MOD IS资料监测土壤水分和干旱的方法,着重从光谱法、温度植被干旱指数(TVD I)空间法、植被水分监测法、水分亏缺指数法等几方面进行了重点介绍,并对未来的发展趋势进行了展望。 相似文献
7.
安徽省MODIS干旱监测技术研究 总被引:4,自引:0,他引:4
为了更有效地监测安徽省农业干旱,以安徽省半湿润区、过渡区为研究对象,利用MODIS资料,在对比分析了基于植被供水指数和温度植被干旱指数两种干旱监测方法基础上,结合已业务应用的农业干旱指标累积湿润指数,开展安徽省分时段的晴空遥感干旱监测研究,建立并确定了部分时段半湿润区、过渡区的MODIS植被供水指数和温度植被干旱等级标准,同时结合当地台站监测土壤墒情对干旱监测进行检验。结果表明,作物生长季节VSWI、TVDI与累积湿润指数存在相关性,两种方法建立指标和模型可以用来分时段进行区域干旱监测,得出了初夏旱TVDI更适合于研究区域的干旱监测。 相似文献
8.
SPI与K指数在阿勒泰地区应用的对比分析 总被引:2,自引:0,他引:2
干旱是限制新疆阿勒泰地区农牧业生产可持续发展最主要的自然灾害之一,研究适合该区的干旱指标,是进行有效干旱监测的基础。利用阿勒泰地区7个气象台站1961-2009年的月降水量、气温资料,计算SPI和K指数,比较分析了SPI和在该地区应用较好的K指数。结果表明:SPI计算简单,资料容易获取,而且计算结果与K指数有较好的一致性,能够较好地反映该地区干旱状况,特别是SPI可以计算不同时间尺度的指标值,能够满足不同水资源状况分析的要求。对比分析了5种时间尺度的SPI值,发现SPI能较准确地反映该地区的旱涝趋势,尤其是3、6个月时间尺度的SPI值,能较好的反应该地区的干旱发展,12个月尺度对长期连续干旱监测较好。 相似文献
9.
几种干旱指标对西南和华南区域月尺度干旱监测的适用性评价 总被引:2,自引:0,他引:2
《高原气象》2015,(6)
利用西南和华南区域129个气象站逐日和逐月气象数据,计算并对比分析了7种干旱监测指标在该区域的适用性,结果表明:MCI指数和K干旱指数在研究区各季干旱监测中表现均较好,其中,夏、秋季K指数优于MCI指数,冬、春季MCI指数优于K指数;DI指数对冬、春季旱情监测较好;PDSI指数和G EVI指数在夏、秋季监测能力较强;SPI指数夏季监测效果较好;SPIW60指数在各季的监测能力都较弱。K指数对干旱演变过程的刻画能力最强,其次是DI指数;MCI指数在干旱缓解阶段存在监测偏重的情况;SPI、SPIW60以及GEVI指数对干旱的累积效应考虑不够,存在监测偏轻、缓解或解除过快情况;PDSI指数对干旱波动发展过程反映能力较差。综合来看,MCI指数和K指数优于其他指数,K指数更适用于研究区的月尺度干旱监测。 相似文献
10.
内蒙古中部地区春玉米水分亏缺时空特征 总被引:2,自引:0,他引:2
水分亏缺指数是判识作物干旱程度的重要指标之一,分析作物水分亏缺指数时空变化规律可为作物合理布局和科学有效灌溉提供理论依据。基于内蒙古中部地区24个气象站1971—2015年降水、气温、风速等逐日气象观测数据和春玉米生育期观测与调查资料,以Penman-Monteith模型为基础计算春玉米需水量,并结合作物有效降水量,定量分析春玉米水分亏缺指数的时空分布特征。结果表明,内蒙古中部地区春玉米生长季内水分亏缺指数呈"高、低、高"的波动变化;近45 a来,春玉米水分亏缺指数在生育前期和后期呈下降趋势,而在生育中期则呈上升趋势,且抽雄-乳熟期的水分亏缺指数上升趋势明显,1990年代以后上升趋势达到显著性水平,说明水分亏缺指数表现出向春玉米需水关键期增加的趋势,并在1991年发生显著突变;空间上,春玉米水分亏缺指数呈带状分布,且呈北高南低的分布格局,这与该区降水量南多北少的分布特征有关。 相似文献
11.
Using the International Comprehensive Ocean-Atmosphere Data Set(ICOADS) and ERA-Interim data, spatial distributions of air-sea temperature difference(ASTD) in the South China Sea(SCS) for the past 35 years are compared,and variations of spatial and temporal distributions of ASTD in this region are addressed using empirical orthogonal function decomposition and wavelet analysis methods. The results indicate that both ICOADS and ERA-Interim data can reflect actual distribution characteristics of ASTD in the SCS, but values of ASTD from the ERA-Interim data are smaller than those of the ICOADS data in the same region. In addition, the ASTD characteristics from the ERA-Interim data are not obvious inshore. A seesaw-type, north-south distribution of ASTD is dominant in the SCS; i.e., a positive peak in the south is associated with a negative peak in the north in November, and a negative peak in the south is accompanied by a positive peak in the north during April and May. Interannual ASTD variations in summer or autumn are decreasing. There is a seesaw-type distribution of ASTD between Beibu Bay and most of the SCS in summer, and the center of large values is in the Nansha Islands area in autumn. The ASTD in the SCS has a strong quasi-3a oscillation period in all seasons, and a quasi-11 a period in winter and spring. The ASTD is positively correlated with the Nio3.4 index in summer and autumn but negatively correlated in spring and winter. 相似文献
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Fei LIU Chen XING Jinbao LI Bin WANG Jing CHAI Chaochao GAO Gang HUANG Jian LIU Deliang CHEN 《大气科学进展》2020,37(7):663-670
<正>The Taal Volcano in Luzon is one of the most active and dangerous volcanoes of the Philippines. A recent eruption occurred on 12 January 2020(Fig. 1a), and this volcano is still active with the occurrence of volcanic earthquakes. The eruption has become a deep concern worldwide, not only for its damage on local society, but also for potential hazardous consequences on the Earth’s climate and environment. 相似文献
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The moving-window correlation analysis was applied to investigate the relationship between autumn Indian Ocean Dipole (IOD) events and the synchronous autumn precipitation in Huaxi region, based on the daily precipitation, sea surface temperature (SST) and atmospheric circulation data from 1960 to 2012. The correlation curves of IOD and the early modulation of Huaxi region’s autumn precipitation indicated a mutational site appeared in the 1970s. During 1960 to 1979, when the IOD was in positive phase in autumn, the circulations changed from a “W” shape to an ”M” shape at 500 hPa in Asia middle-high latitude region. Cold flux got into the Sichuan province with Northwest flow, the positive anomaly of the water vapor flux transported from Western Pacific to Huaxi region strengthened, caused precipitation increase in east Huaxi region. During 1980 to 1999, when the IOD in autumn was positive phase, the atmospheric circulation presented a “W” shape at 500 hPa, the positive anomaly of the water vapor flux transported from Bay of Bengal to Huaxi region strengthened, caused precipitation ascend in west Huaxi region. In summary, the Indian Ocean changed from cold phase to warm phase since the 1970s, caused the instability of the inter-annual relationship between the IOD and the autumn rainfall in Huaxi region. 相似文献
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Various features of the atmospheric environment affect the number of migratory insects, besides their initial population. However, little is known about the impact of atmospheric low-frequency oscillation(10 to 90 days) on insect migration. A case study was conducted to ascertain the influence of low-frequency atmospheric oscillation on the immigration of brown planthopper, Nilaparvata lugens(Stl), in Hunan and Jiangxi provinces. The results showed the following:(1) The number of immigrating N. lugens from April to June of 2007 through 2016 mainly exhibited a periodic oscillation of 10 to 20 days.(2) The 10-20 d low-frequency number of immigrating N. lugens was significantly correlated with a low-frequency wind field and a geopotential height field at 850 h Pa.(3) During the peak phase of immigration, southwest or south winds served as a driving force and carried N. lugens populations northward, and when in the back of the trough and the front of the ridge, the downward airflow created a favorable condition for N. lugens to land in the study area. In conclusion, the northward migration of N. lugens was influenced by a low-frequency atmospheric circulation based on the analysis of dynamics. This study was the first research connecting atmospheric low-frequency oscillation to insect migration. 相似文献
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The atmospheric and oceanic conditions before the onset of EP El Ni?o and CP El Ni?o in nearly 30 years are compared and analyzed by using 850 hPa wind, 20℃ isotherm depth, sea surface temperature and the Wheeler and Hendon index. The results are as follows: In the western equatorial Pacific, the occurrence of the anomalously strong westerly winds of the EP El Ni?o is earlier than that of the CP El Ni?o. Its intensity is far stronger than that of the CP El Ni?o. Two months before the El Ni?o, the anomaly westerly winds of the EP El Ni?o have extended to the eastern Pacific region, while the westerly wind anomaly of the CP El Ni?o can only extend to the west of the dateline three months before the El Ni?o and later stay there. Unlike the EP El Ni?o, the CP El Ni?o is always associated with easterly wind anomaly in the eastern equatorial Pacific before its onset. The thermocline depth anomaly of the EP El Ni?o can significantly move eastward and deepen. In addition, we also find that the evolution of thermocline is ahead of the development of the sea surface temperature for the EP El Ni?o. The strong MJO activity of the EP El Ni?o in the western and central Pacific is earlier than that of the CP El Ni?o. Measured by the standard deviation of the zonal wind square, the intensity of MJO activity of the EP El Ni?o is significantly greater than that of the CP El Ni?o before the onset of El Ni?o. 相似文献
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基于最新的GTAP8 (Global Trade Analysis Project)数据库,使用投入产出法,分析了2004年到2007年全球贸易变化下南北集团贸易隐含碳变化及对全球碳排放的影响。结果显示,随着发展中国家进出口规模扩张,全球贸易隐含碳流向的重心逐渐向发展中国家转移。2004年到2007年,发达国家高端设备制造业和服务业出口以及发展中国家资源、能源密集型行业及中低端制造业出口的趋势加强,该过程的生产转移导致全球碳排放增长4.15亿t,占研究时段全球贸易隐含碳增量的63%。未来发展中国家的出口隐含碳比重还将进一步提高。贸易变化带来的南北集团隐含碳流动变化对全球应对气候变化行动的影响日益突出,发达国家对此负有重要责任。 相似文献
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The spatial and temporal variations of daily maximum temperature(Tmax), daily minimum temperature(Tmin), daily maximum precipitation(Pmax) and daily maximum wind speed(WSmax) were examined in China using Mann-Kendall test and linear regression method. The results indicated that for China as a whole, Tmax, Tmin and Pmax had significant increasing trends at rates of 0.15℃ per decade, 0.45℃ per decade and 0.58 mm per decade,respectively, while WSmax had decreased significantly at 1.18 m·s~(-1) per decade during 1959—2014. In all regions of China, Tmin increased and WSmax decreased significantly. Spatially, Tmax increased significantly at most of the stations in South China(SC), northwestern North China(NC), northeastern Northeast China(NEC), eastern Northwest China(NWC) and eastern Southwest China(SWC), and the increasing trends were significant in NC, SC, NWC and SWC on the regional average. Tmin increased significantly at most of the stations in China, with notable increase in NEC, northern and southeastern NC and northwestern and eastern NWC. Pmax showed no significant trend at most of the stations in China, and on the regional average it decreased significantly in NC but increased in SC, NWC and the mid-lower Yangtze River valley(YR). WSmax decreased significantly at the vast majority of stations in China, with remarkable decrease in northern NC, northern and central YR, central and southern SC and in parts of central NEC and western NWC. With global climate change and rapidly economic development, China has become more vulnerable to climatic extremes and meteorological disasters, so more strategies of mitigation and/or adaptation of climatic extremes,such as environmentally-friendly and low-cost energy production systems and the enhancement of engineering defense measures are necessary for government and social publics. 相似文献
18.
Analysis of Atmospheric Boundary Layer Height Characteristics over the Arctic Ocean Using the Aircraft and GPS Soundings
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正ERRATUM to: Atmospheric and Oceanic Science Letters, 4(2011), 124-130 On page 126 of the printed edition (Issue 2, Volume 4), Fig. 2 was a wrong figure because the contact author made mistake giving the wrong one. The corrected edition has been updated on our website. The editorial office is sincerely sorry for any 相似文献
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《大气科学进展》2014,(6)
正AN Junling;see LI Ying et al.;(5),1221—1232AN Junling;see QU Yu et al.;(4),787-800AN Junling;see WANG Feng et al.;(6),1331-1342Ania POLOMSKA-HARLICK;see Jieshun ZHU et al.;(4),743-754Baek-Min KIM;see Seong-Joong KIM et al.;(4),863-878BAI Tao;see LI Gang et al.;(1),66-84BAO Qing;see YANG Jing et al.;(5),1147—1156BEI Naifang; 相似文献