雅鲁藏布江流域TRMM降水数据降尺度研究

为了获取我国西南典型缺资料地区雅鲁藏布江流域较高时空分辨率的降水空间分布数据,开展了复杂地形条件下的卫星降水资料降尺度方法研究.构建了基于TRMM降水数据的雅鲁藏布江流域降水时空降尺度模型,并利用搜集的降水实测数据对模型进行了率定和精度验证,结果表明:1)研究提出的水汽运移距离因子在雅江流域与降水具有很强的相关性,在构建的空间降尺度模型中其显著性优于传统地形指示因子DEM;2)TRMM降尺度结果较原始降水数据空间分布更为合理性且细节刻画能力更强,但统计精度月际差异明显,月R2值主要集中在0.5~0.9之间,RMSE值在3.7~40mm之间波动,且整体呈现随月降雨量增加而增大的趋势,个别月份由于降水观测极值出现而导致R2值不足0.1.日尺度上,降尺度结果受原始TRMM降水数据精度影响,平均精度R2不足0.3.     

基于局部非线性地理加权回归模型的地表温度降尺度算法研究

地表温度(land surface temperature, LST)是反映地表状况的一个重要参数,能对地表-大气相互作用过程进行描述。由于受到卫星热红外传感器成像条件的制约,获取的卫星热红外遥感图像存在时间分辨率、空间分辨率难以兼顾的问题,导致反演的LST数据难以得到深入应用。采用LST降尺度算法可以解决此矛盾,获得高时空分辨率的地表温度数据。目前LST降尺度模型逐步由全局模型转向局部模型,但局部降尺度模型忽略了非线性关系。针对此问题,提出基于局部非线性地理加权回归(non-linear geographically weighted regression, NL-GWR)的地表温度降尺度算法。选择合适的研究区域,并分别选取归一化差异植被指数(normalized difference vegetation index, NDVI)、归一化差异建筑指数(normalized difference build-up index, NDBI)以及数字高程模型(digital elevation model, DEM)作为辅助参数进行LST降尺度,将中分辨率成像光谱仪(moderate resolution imaging spectroradiometer, MODIS)地表温度空间分辨率从1 000 m提升到100 m,并将基于地理加权回归(geographically weighted regression, GWR)与NL-GWR模型的降尺度结果进行对比分析。实验结果表明,考虑非线性关系的NL-GWR模型要优于GWR线性模型,能够获得较低的均方根误差(1.96 ℃)和平均绝对误差(1.63 ℃)。     

基于统计降尺度和SPI 的黄河流域干旱预测

基于黄河流域101个气象站点的实测气象数据和国际耦合模式比较计划第5阶段(coupled model intercomparison project phase 5,CMIP5)3种排放情景下的6个模型1961—2099年的降水和气温数据,采用等距离累积分布函数法(equidistant cumulative distribution function matching method,EDCDFm)进行统计降尺度;通过历史阶段(1961—2005年)实测站点数据对降尺度后的降水和气温进行精度评估;在此基础上,通过标准化降水指数(standardprecipitation index,SPI)对黄河流域气象干旱进行预估。结果表明,EDCDFm的降尺度方法能够明显提高气候模式所模拟的气温和降水精度,尤其对极值的模拟精度;黄河流域气象干旱的预估显示,3种气候情景下21世纪初的干旱情况相对于基准期均变得比较严重,但是世纪末的干旱程度均明显减轻,近期黄河流域的防旱工作形势仍然严峻。     

祁连山区DISPATCH、多元回归降尺度方法及SMAP产品的应用对比

土壤水分对山区水文过程具有重要意义,遥感土壤水分产品能够长时间序列地提供山区流域的土壤水分空间分布数据,但分辨率较粗,无法直接应用,因此需要在山区进行降尺度研究.本文采用DISPATCH(disaggregation base on physical and theoretical scale change)方法和多元回归方法对SMAP(soil moisture active passive)36 km×36 km遥感土壤水分产品进行降尺度,进而选取SMAP (9 km×9 km)的高精度遥感土壤水分产品和实测土壤水分数据,利用R(相关系数)、E_(RMS)(均方根误差)和E_(bias)(偏差)指标评估降尺度结果.评估结果表明:由于2种降尺度方法的函数关系和反演过程存在差异,DISPATCH方法降尺度结果的数据趋势拟合效果较好,而多元回归方法降尺度结果的数据精度较好;在季节尺度对比中,不同季节山区温度和土壤水分的时空变化,导致多元回归方法降尺度效果春季最好,秋季次之,而夏季最差;DISPATCH方法降尺度效果秋季最好,夏季次之,而春季最差;亮温数据和SMAP表层土壤温度数据在山区的质量,导致2种方法降尺度结果的精度均比SMAP (9 km×9 km)产品好,但趋势拟合效果较差.     

多模式集合在统计降尺度应用上的研究进展

气候模式(GCMs)由于分辨率过低,进行区域气候的模拟和预测的能力很差;而统计降尺度可以通过建立气候模式的输出信息和区域气候要素之间的统计关系来弥补GCMs的不足。在对比动力降尺度和统计降尺度之后首先对统计降尺度的基本原理和方法进行了介绍。强调了产生模拟预测结果的不确定来源,据此讨论集合思想在统计降尺度的应用的必要性。并重点追踪了其在国内外的研究进展,提出了未来多模式集合统计降尺度的发展方向。     

基于多源降水融合驱动的WRF-Hydro模型在中小河流洪水预报中的适用性

从提高驱动数据(降水)的质量和时空分辨率出发,评估了基于混合地理加权回归截尾函数(MGWR-BI)多源降水融合算法的有效性,以及融合降水对WRF-Hydro模型计算结果的影响。将融合降水数据用于WRF-Hydro模型中进行子午河流域的洪水预报,并与站点实测降水数据进行比较,结果表明,融合降水的精度高于原始CMORPH卫星降尺度降水,融合降水数据驱动WRF-Hydro模型比CMORPH卫星降尺度降水数据能更好地预报与模拟洪水事件,WRF-Hydro模型具有中小河流洪水预报的潜在优势。     

基于卫星遥感降水反演产品在黄河源区的水文应用研究

多卫星遥感降水产品具有高时空分辨率、覆盖范围广等特点,弥补了传统地面观测时空上的局限性,并为无或缺资料地区提供新的数据来源。为评价TRMM 3B42-V7降水产品在地形复杂的高原山区的精度及适用性,以黄河源区为例,利用地面观测降雨站点对卫星数据的精度及适用性进行评估,并结合大尺度陆面分布式水文模型VIC进行水文模拟验证。研究结果表明:3B42-V7与地面观测站点的流域平均月降水之间的相关系数达到0.99;空间尺度上,流域尺度相关性优于网格尺度;时间尺度上,月尺度相关性较日尺度好。该产品可在一定程度上代替黄河源区的地面观测,但使用时需要根据实际情况对模型的参数重新率定。     

未来气候变化情景下中国北方农牧交错带脆弱性评估

基于中国地面降水和气温0.5°×0.5°格点数据集以及区域协同降尺度试验东亚地区项目组RCP4.5和RCP8.5情景下未来气候预估数据,对中国北方农牧交错带1980—2100年的气候脆弱性进行模拟和分析。结果表明,北方农牧交错带气候脆弱性在未来的100年中整体上呈现增大的趋势,特别是2050年后,增大速度进一步加剧,到21世纪末,几乎整个北方农牧交错带都表现出明显脆弱性。从宁夏中卫到呼和浩特北部一带,脆弱性形势较为严峻,而大兴安岭北部地区脆弱性程度较低。     

基于地理加权回归模型和不同植被特征参数的TRMM 3B43降尺度研究——以云南省为例

为了将空间分辨率约为27 km×27 km的热带降水测量计划卫星(TRMM) 3B43数据降尺度为1 km×1 km,并对比不同植被参数下TRMM 3B43降尺度效果,以云南省为研究区, TRMM 3B43卫星降水数据、MOD13 A3归一化植被指数(NDVI)和增强型植被指数（EVI)数据、MOD17A2H GPP数据、气象站点月降水数据等为数据源,基于地理加权回归模型,开展不同植被特征参数的TRMM3B43降水数据降尺度研究,采用线性相关系数、偏离率和均方根误差验证云南省整体、不同气候区及单气象站点TRMM数据的降尺度精度.结果表明,不同植被特征参数中,以植被总初级生产力、NDVI数据为基础的降尺度结果为佳,利用EVI进行降尺度的结果较差;各时间尺度下,以月尺度的降水数据降尺度结果最佳,其中降雨量较多的月份相关性更高,其次为季节尺度,其中以秋季降尺度最佳,年尺度下的降尺度综合精度评价结果较差;不同气候带下,边缘热带区域内TRMM降尺度效果最佳,其次为南亚热带和中亚热带,高原气候区TRMM降尺度结果稍差;单气象站点中,江城、丽江等站点处的TRMM降尺度效果最佳,贡山站点降尺度效果最差...     

喀斯特山区TRMM降水数据降尺度研究

获取高精度的卫星降水数据,为喀斯特区域旱涝灾害评估、水文预报等各研究领域提供数据基础。以热带降雨卫星(tropical rainfall measuring mission, TRMM)为数据源,采用多元线性回归法(ordinary least square, OLS)和地理加权回归法(geographically weighted regression, GWR),综合考虑高程、坡度、坡向、经纬度和归一化植被指数(normalized difference vegetation index, NDVI)等6个因子构建OLS和GWR降尺度模型进行年降尺度研究,并比较OLS和GWR两种降尺度模型在喀斯特山区的适用性。结果表明：1)TRMM数据与站点观测数据之间精度较好;2)降尺度后数据空间分辨率提升到1 km, GWR降尺度年降水量在多数年份比原始TRMM数据更接近实测值,高估现象得到改善;与OLS降尺度数据相比,其三项指标表现更好;3)单站点中,OLS降尺度数据在高程和NDVI突变区域易出现假性更优相关性。综合多指标评价,GWR降尺度数据在喀斯特山区总体精度更高。后续可通过划分植被区、...     

Application of the Bottom-up Method on Risk Evaluation of Climate Change in Water Resources System

Traditional approach to evaluate the impacts of climate change on the water resources systems always begins with downscaling general circulation models( GCMs) and proceeding back to the hydrological model. This approach has some distinct disadvantages: 1) GCM must be downscaled; 2) different GCMs are difficult to be reconciled for a given climate change scenario;3) the uncertainty of GCMs is far from the requirement of the evaluation of climate change impacts. To overcome these limits of the traditional method,a new method termed as "bottom-up"was used for climate risk assessment that linked vulnerability assessment with climate information to assess the risk of climate change impacts on the Quabbin Reservoir,and United States under A2 scenario.The result shows that the risks are around 20% in 2006-2035 and 2036-2055,50% in 2066-2095.     

气候变化影响下的流域水循环：回顾与展望?

随着全球气候变化的影响日益加剧,气候变化对流域水循环影响的研究成为普遍关注的焦点.数理统计和水文模拟两大手段的运用贯穿于过去30余年的研究当中,本文就其中所涉及的水文气象要素趋势分析、大气环流模式(GCMs)评估、降尺度技术及其选择、水文模型及其选择、不确定性分析5大内容的研究成果进行回顾与展望,以拉萨河流域为例综合运用前述技术分析区域气候变化特征及其对流域径流的影响.结果表明,在未来经济社会发展与气候变化情景下,流域径流时空分布不均匀性更加显著,具体表现为集中性增强,这给区域防洪抗旱与水资源配置等都带来了巨大挑战.通过系统阐述如何开展气候变化对流域水循环的影响研究及所包含的关键问题,可为后续相关研究和区域水资源管理提供科学依据与决策支撑.     

Variety distribution pattern and climatic potential productivity of spring maize in Northeast China under climate change

This study was based on the daily meteorological data of 101 meteorological stations from 1971 to 2000 and the 0.25°×0.25° grid data from 1951 to 2100 simulated by RegCM3 under the future A1B climatic scenario published by National Climate Center,in combination with the demand of climatic condition for maize growth in Northeast China.The trajectory of agricultural climatic resources and the effects of climate change on variety distribution and climatic potential productivity of spring maize in Northeast China under future climate change were analyzed.The main agro-climatic resource factors include:the initial date daily average temperature stably passing 10℃(≥10℃),the first frost date,the days of growing period,the ≥10℃ accumulated temperature,and the total radiation and precipitation in the growing period.The results showed that:(1) in the coming 100 years,the first date of ≥10℃ would be significantly advanced,and the first frost date would be delayed.The days of growing period would be extended,the ≥10℃ accumulated temperature and the total radiation would be significantly increased.However,no significant change was found in precipitation.(2) Due to the climate change,the early-maturing varieties will be gradually replaced by late-maturing varieties in Northeast China,and the planting boundaries of several maize varieties would be extended northward and eastward.(3) There would be a significant change in the climatic potential productivity of maize in Northeast China with the high-value gradually moving towards northeast.(4) It was an effective way to increase the climatic potential productivity of maize by appropriate adjustment of sowing date.     

Coupling methods of global climate models and regional climate models

The future climate dynamical downscaling method is that output of general circulation models (GCMs) is employed to provide initial conditions,lateral boundary conditions,sea surface temperatures,and initial land surface conditions to regional climate models (RCMs).There are two methods of downscaling:offline coupling and online coupling.The two kinds of coupling methods are described in detail by coupling the Weather Research and Forecasting model (WRF) with the Institute of Atmospheric Physics of Chinese Academy of Sciences Atmospheric General Circulation Model Version 4.0 (IAP AGCM4.0) in the study.And the extreme precipitation event over Beijing on July 21 2012 is simulated by using the two coupling methods.Results show that online coupling method is of great value in improving the model simulation.Furthermore,the data exchange frequency of online coupling has some effect on simulation result.     

水文水资源管理经济中气候变化影响评估的不确定性问题

大多数关于气候变化对水文过程影响评估的文献中,对大气模型中存在的显著的不确定性往往考虑不足.在很多重要的领域,GCM生成的长期预测很少能用于未来水资源规划与管理,因为其结果无法对变化趋势进行准确的描述,也就是对水文变量增加或减少趋势的预测缺乏可信度.一项借助于14个GCM模型以及不同发展情景(包括基准情景)开展的径流预报集合分析研究验证了这一点.GCM的输出情景作为水平衡模型的输入这种方法用于多瑙河上游未来水文变化的预测.如果将这种预测情景用于预测由气候变化引起的水文变化经济成本,更大的不确定性将会在经济数学模型中产生.著名的Stern"气候变化经济学"评论在水资源管理领域被广泛的应用.在经济学文献中对Stern评论的观点存在很多争议,很多专家对方法的使用和结论提出了一些质疑,本文的实例也证明了这一点.因此,进行气候变化对水文和水资源影响评估的学者们应该意识到不仅在GCM中存在很大的不确定性,大气-水文-经济模型的耦合过程可能会导致更大的不确定性.     

Impact of land surface degradation in northern China and southern Mongolia on regional climate

Clear evidence provided by the singular value decomposition (SVD) analysis to the normalized difference vegetation index (NDVI) and precipitation data identifies that there exists a sensitive region of vegetation-climate interaction located in the transitional zone over northern China and its surrounding areas, where the vegetation cover change has the most significant influence on summer precipitation over China.““ Comparison of reanalysis data with station data provides a good method to assess the impacts of land use change on surface temperature, and the most obvious contribution of land use change may be to lead to notable warming over northern China in the interdecadal time scale. Based on the new statistical results, a high-resolution regional integrated environmental model system (RIEMS) is employed to investigate the effects of land surface degradation over the transitional zone and its surrounding areas (northern China and southern Mongolia) on the regional climate. Land degradation results in the decreases in precipitation over northern and southern China, and the increase in between, and increased and decreased temperature over vegetation change areas and the adjacent area to the south, respectively. Not only would it change the surface climate, but also bring the significant influence on the atmospheric circulation. Both the surface climate and circulation changes generally agree to the observed interdecadal anomalies over the last five decades. These integrated statistical and simulated results imply that land surface degradation over the transitional zone in northern China and its surrounding areas could be one of the main causes responsible for the climate anomalies over China, especially the drought over northern China.     

High-resolution climate evolution derived from the sediment records of Erlongwan Maar Lake since 14 ka BP

Varve counts with AMS 14 C,137 Cs and 210 Pb dating of sediments(0-900 cm) from Erlongwan Maar Lake,NE China were used to establish a high-resolution chronology series for the late Quaternary.Dry density,total organic carbon(TOC) content,total nitrogen(TN) content,TOC/TN ratios and stable organic carbon isotope(13 C org) ratios were continuously analyzed on this sediment profile.On the basis of lithological characters,sporo-pollen assemblages and geochemical analyses,we identified 6 climate stages within the last 14 ka BP.The time before the Holocene(14-11.4 ka BP) represents a higher-order oscillation climatic transitional period(I).The entire Holocene climate development(from 11.4 ka BP to present) exhibited an increasing temperature trend,although there were cold and warm alternations(II-VI).The periods included were:II(11.4-9.05 ka BP) warm-wet stage,III(9.05-7.4 ka BP) cold and warm fluctuation stage,IV(7.4-4.2 ka BP) smoothly warming climate stage,V(4.2-1.67 ka BP) climate optimum stage,and VI(from 1.67 ka BP to present) cool and drier stage.Each climate stage began with a warming event and ended with an abrupt cooling event.This climate change cycle had unequal time spaces that were progressively shorter over time.Several abrupt climate shifts occurred at about 9.4-9.05,8.5-8.2,7.8-7.4,4.6-4.2,3.7-3.25,2-1.67 and 0.3-0.03 ka BP.Thus,it can be seen that the climate has been warming since 1920 AD,which indicates a new climate stage.     

Global vegetation change analysis based on MODIS data in recent twelve years①

Vegetation cover change is critical for understanding impacts and responses of vegetation to climate change. A study found that vegetation in the regions between 45°N-70°N was increasing using normalized difference vegetation index( NDVI) from 1981 to 1991 ten years ago. The global vegetation growth has changed because of climate change in recent twelve years( 2001- 2012). After thorough analysis based on satellite data,it is found that it is evident that the global vegetation changed( NDVI) little,and it is increasing slightly in Northern hemisphere while it is decreasing slightly in Southern Hemisphere. For different latitudes,vegetation is increasing 0.17% every year from 60°N to 70 °N( R~2= 0.47,P 0.013),while the vegetation is decreasing 0.11% every year from 10°N to 10° S( R~2= 0.54,P 0.004). For different continents,the vegetation in South America is decreasing 0.16% every year( R~2= 0.78,P 0.0001) and it is increasing 0.05% every year in Asia( R~2= 0.28,P 0.072) and 0.25% every year in Oceania( R~2= 0.24,P 0.1). The analysis of global vegetation in different seasons indicates that spatial distribution of global temperature and water vapor will affect the spatial distribution of vegetation,in turn,the spatial distribution of vegetation will also regulate the global temperature and water vapor spatial distribution at large scale. The growth and distribution of vegetation are mainly caused by the orbit of the celestial bodies,and a big data model based on gravitational-magmatic change with the solar or the galactic system as its center is proposed to be built for analyzing how the earth's orbit position in the solar and galaxy system affects spatial-temporal variations of global vegetation and temperature at large scale. These findings promise a holistic understanding of the global climate change and potential underlying mechanisms.     

基于CMIP6集合优化数据集的全球陆地极端气候变化预估

预估极端气候事件趋势能够降低其引起的灾害风险.该文基于CMIP6集合优化数据集EPTGODD-WHU,选取5个极端气候指数,即最高气温极大值(TXx)、最高气温极小值(TXn)、最低气温极大值(TNx)、最低气温极小值(TNn)和最大月降水量(PXx),并结合GIS分析手段,对2021—2100年SSP1-2.6、SSP2-4.5和SSP5-8.5情景下的全球陆地极端气温及降水进行预估.结果表明：1)相较于CMIP单一模式,EPTGODD-WHU数据集模拟性能显著提升,气温及降水的空间相关系数分别达到0.99和0.81.2) SSP5-8.5情景下,年最低气温和最高气温均上升明显,且这种上升趋势年内波动不大,地球陆地极寒地区将面临升温的风险,而赤道等极热地区将处于年内长时间酷热状态.3)六大洲在SSP5-8.5情景下的极端降水整体上升趋势最剧烈,但北美洲密西西比平原和滨海平原的地区在SSP5-8.5情景下在未来面临较高的旱灾风险.4)中国西南部地区的极端降水在三个情景下均呈稳定的增幅,且增幅高达60%,预示面临较高的洪灾风险.     

基于Hadgem2_ES的气候变化对金华江流域枯水变化的影响评估

气候变化引发降雨径流等水文要素的变化已经成为学界的共识,浙江省内近年来频发的气候极端事件更是引起众多学者的重视。枯水水文作为水文学的一部分,分析其变化有其积极的实际意义。选取了金华江流域1975-2004年的日降水、温度和径流资料,使用多站统计降尺度方法,基于最新CMIP5中的Hadgem2_ES模型,利用GR4J水文模型模拟了未来(2071-2100年)的径流,重点分析了几个重要枯水指数7Q10和30Q10的变化。结果表明,未来这两个指数都有明显的上升趋势,即气候变化可能缓和金华江流域未来的枯水情况。