玛纳斯河流域气候变化对参考蒸散量的影响

蒸散量是内陆水循环的重要环节,探索西北干旱半干旱区气候因素对蒸散量的影响,有助于深入研究内陆水循环对气候变化的响应。本文利用玛纳斯河流域1964—2010年6个气象台站的日气温、风速、相对湿度等气候资料,通过Penman-Monteith公式估算玛纳斯河流域的参考作物蒸散量(RET),利用回归分析、Mann-Kendall等方法分析研究参考作物蒸散量的时空变化特征。结果表明:(1)玛纳斯河流域参考作物蒸散量空间差异明显,除石河子外南部绿洲区参考作物蒸散量均大于北部绿洲边缘区,季节变化趋势也较北部明显。从季节上来看,玛纳斯河流域参考作物蒸散量季节变化差异显著,夏季是参考作物蒸散量变化的主要贡献者,其次是秋季大于春季,冬季的变化最小。(2)南部绿洲区平均风速的减小是参考作物蒸散量减少的主要原因,北部绿洲边缘区相对湿度的增加是参考作物蒸散量减少的主要原因。     

吉林省参考作物蒸散量的时空变化特征及影响因素

基于吉林省50个气象站1960—2014年逐日最高气温、最低气温、日照时数、风速数据,采用Penman-Monteith算法,计算各站逐日参考作物蒸散量,进而计算各站及全省四季和年平均参考作物蒸散量,利用数理统计方法,结合地理信息系统软件,分析参考作物蒸散量的时空变化特征及主要气候影响因子。结果表明:近55 a,吉林省年平均参考作物蒸散量为876 mm,年参考作物蒸散量呈显著下降趋势(p <0. 01);空间分布差异显著,由东南向西北逐级递增,56%的站点呈显著下降趋势(p <0. 05)。参考作物蒸散量夏季最大、春季次之、冬季最小,且均呈下降趋势,但只有春季的下降趋势显著(p <0. 01);春、夏、秋、冬季与年平均参考作物蒸散量在空间分布上基本一致,但气候倾向率为负值以及通过显著性检验的站点数依次减少。全省四季和年参考作物蒸散量均与降水呈显著负相关,与日照时数、风速、最高气温呈显著正相关;其中年、春、夏、秋季与气温日较差以及春、夏、秋季与平均气温也呈显著正相关;冬季与最低气温、平均气温呈显著正相关;而典型站点参考作物蒸散量各季节影响因素及影响大小略有差异,各气象因子的共同作用导致了参考作物蒸散量的变化。     

我国参考作物蒸散的空间分布和时间趋势

根据我国616个地面气象台站1975-2004年的观测资料,利用联合国粮农组织推荐的Penman-Monteith公式计算各年逐日、逐月参考作物蒸散值(ET0)和年总量.结果表明,我国参考作物蒸散多年平均值大多界于800～1 100 mm之间,西北地区高,东北地区低.1978年出现最大值,1993年出现最低值,青藏高原以东地区波动小,西北地区波动大.参考作物蒸散变化率在-30～30 mm·(10 a)-1之间,西部和长江流域地区显著下降,东部沿海、黄河中上游和东北显著上升.造成我国参考作物蒸散出现先降后增趋势的主要因素是日照时数(净辐射)和饱和差.     

黄淮海平原冬小麦最大可能蒸散的估算

作物最大可能蒸散考虑了作物及当地地表状况,为当地地表实际覆盖情况下实际蒸散的理论上限值,能客观分析作物对水分的需求程度和农业干旱状况。基于遥感（叶面积指数和地表反照率）数据和逐日气象数据,利用Penman-Monteith公式,计算黄淮海平原小麦种植区27个气象站冬小麦生育期2000-2015年逐日蒸散,提取得到冬小麦生育期逐日最大可能蒸散数据集,并分析其时空变化特征及成因。结果表明:与联合国粮农组织（FAO）单作物系数法计算的最大可能蒸散E_k对比,区域平均最大可能蒸散E_c的时间变化趋势与E_k一致,空间分布上E_c符合客观实际。黄淮海平原冬小麦全生育期、越冬期和返青-拔节期E_c均呈北低南高的分布特征,日平均值分别为1.99 mm,0.44 mm和2.75 mm;其余3个生育期（越冬前、抽穗期、乳熟-成熟期）在空间分布上差异不大,日平均值分别为1.23 mm,4.71 mm和3.74 mm。冬小麦不同生育期（含全生育期）E_c的空间分布主要受叶面积指数分布特征的影响,二者呈显著正相关关系。     

黄河源区陆面蒸散量的时空分布特征研究

基于修正的Penman-Monteith（P-M）模型,利用1980~2020年黄河源区的气象台站观测数据和陆-气间水热交换观测试验数据,计算出该区域的陆面参考蒸散量,分析了黄河源区蒸散量的时空演变特征,探讨了影响黄河源区蒸散量变化的原因。结果表明:（1）修正的P-M模型能较准确地估算黄河源区的参考蒸散量,与实际观测的相关系数在0.85以上。（2）黄河源区的蒸散量总体呈上升趋势,但在20世纪80年代中期和90年代中期均呈显著减少趋势;近年来,中部和西部地区的蒸散量呈减少趋势,而东部地区的蒸散量呈增加趋势。（3）黄河源区年蒸散量呈自东向西减小的分布特征,东、中、西部地区分别为473.5~516.0mm、437.6~473.5mm和386.3~437.6mm;四季蒸散量差异明显,夏季最大,春季和秋季次之,冬季最小。（4）黄河源区蒸散量随温度、风速和日照时数的增加而增大,随相对湿度和降水量的增大而减小。      

北疆地区参考作物蒸散量时空变化特征

为明确北疆地区在全球气候变暖背景下合理的灌溉制度,利用北疆地区22个气象站49 a(1962~2010年)的逐日气象资料,运用Penman-Monteith公式计算北疆地区1962~2010年的参考作物蒸散量ET0(reference crop evapotranspiration),并用Mann-Kendall方法对其进行突变检验,基于Arc GIS9.3空间分析功能模块对北疆参考作物蒸散量进行了空间变化分析。结果表明:研究区域的ET0在1983年发生向下突变,ET0在时间分布上整体呈下降趋势,主要受该地区相对湿度和风速的影响;ET0从北疆的东北部和西南部向中间逐渐升高,东南部和西部表现略高,具有明显的区域差异;4~10月ET0对全年ET0的分布具有显著影响。     

基于自动观测站的鲁中地区土壤水分变化规律及精细化预报模型的研究

为了建立鲁中地区土壤水分精细化预报模型,利用2010—2013年农田土壤水分自动站逐日资料进行土壤水分年、月变化特征研究,并结合附近自动气象站资料,以土壤水分平衡方程、农田蒸散模型为基础,采用逐步回归和曲线估计等方法建立4—6月无降水条件下平原水浇田与山旱田土壤水分1 d、7 d降幅的经验预报模型。结果表明:鲁中地区0~100 cm土壤水分贮存量年变化趋势和0~50 cm基本一致,年最高出现在8月,最低出现在6月,年降幅最大出现在3—6月,易出现干旱。对预报模型进行回代和预报检验结果显示,回代平均相对误差为0.07%,7 d模型和1 d模型滚动预报第7天0~50 cm土壤水分贮存量,绝对误差分别为-0.15和-2.17 mm,平均相对误差分别为-0.07%和-1.56%,模型具有较强的理论基础和实用性,预报精度较高,为鲁中地区土壤墒情监测和精细化预报提供支持。     

基于Penman-Monteith模型的蒸散模拟评估分析

根据南京地区粳稻、籼稻两个品种水稻分别在干旱、水层条件下的逐时、逐日蒸散量观测资料,采用Penman-Monteith模型(以下简称PM模型)对水稻蒸散量进行模拟,并对比模拟蒸散值与观测蒸散值。通过计算,对PM模型的可靠性进行验证。结果表明:(1)水层条件下PM模型的精度比干旱条件下高。(2)模拟值乘以作物系数后,与蒸散实际测量值更加接近。(3)通过敏感性分析可知,使用PM模型进行蒸散量模拟时,方程中各个因子取值的准确性对模拟结果的精确度有较大影响,计算时要合理确定各个因子值。(4)水层条件下稻田的蒸散量明显大于干旱条件下的蒸散量。     

乌鲁木齐河流域参考作物蒸散量时空变化特征

根据乌鲁木齐河流域5个气象站近30a的地面气象观测资料.应用1998年FAO最新推荐的Penman-Monteith公式计算了各月参考作物蒸散量ETo,在此基础上,分析了ETo的月际和年际变化特征,并探讨了各气候要素和海拔高度与ETo的相关关系。结果表明,乌鲁木齐河流域ETo空间变化较大。从山前冲洪积平原的人工绿洲区到高寒地带的乌鲁木齐河源头ETo多年平均值呈明显递减趋势,平均垂直递减率为17.3mm.（100m）-1;30a来,流域各站的年参考作物蒸散量ETo均呈递减趋势,递减速率为-0.05mm.a-1～-5.21mm.a-1;ETo与平均气温、平均最高气温、平均最低气温、空气相对湿度、风速、日照时数、降水量和小型蒸发皿蒸发量均具有较好的相关性;造成近30a乌鲁木齐河流域参考作物蒸散量呈递减趋势的气候原因是：气温、空气相对湿度升高和降水增多以及风速、日照时数减小等气候变化综合作用的结果。     

农业旱涝指标及在江淮地区监测预警中的应用

该文提出了一个可业务应用的农业旱涝监测预警气象指标———累积湿润指数。该指标以相对湿润度指数为基础, 用作物需水量取代参考作物蒸散量, 并考虑前期旱涝程度对当前旱涝状况的累积影响, 具有农业意义。为方便农业气象业务应用, 采用FAO Penman-Monteith模型的简化方法计算参考作物蒸散量, 用气温资料对简化式进行校准, 将误差减小到可满足应用要求; 通过求算不同区域农田作物系数的加权平均值, 得到宏观农田作物需水量, 并确定了该指标分区域的旬旱涝等级标准。该指标用于旱涝监测, 与土壤墒情的定性符合率为80%~90%, 定量符合率为60%~70%, 在旬时间尺度比土壤墒情指标更符合旱涝实况; 用于下一旬旱涝预警, 尽管受到中期降水量预报准确度影响, 但由于含有前期旱涝实况信息, 预警趋势大体正确, 提高了旱涝预警的准确度。     

Comparison of Ten Potential Evapotranspiration Models and Their Attribution Analyses for Ten Chinese Drainage Basins

Potential evapotranspiration(E_(PET)) is usually calculated by empirical methods from surface meteorological variables,such as temperature, radiation and wind speed. The in-situ measured pan evaporation ET_(pan) can also be used as a proxy for E_(PET). In this study, E_(PET) values computed from ten models are compared with observed ET_(pan) data in ten Chinese river basins for the period 1961-2013. The daily observed meteorological variables at 2267 stations are used as the input to those models, and a ranking scheme is applied to rank the statistical quantities(ratio of standard deviations, correlation coefficient, and ratio of trends) between ET_(pan) and modeled E_(PET) in different river basins. There are large deviations between the modeled E_(PET) and the ET_(pan) in both the magnitude and the annual trend at most stations. In eight of the basins(except for Southeast and Southwest China), ET_(pan) shows decreasing trends with magnitudes ranging between-0.01 mm d~(-1) yr~(-1) and-0.03 mm d~(-1) yr~(-1), while the decreasing trends in modeled E_(PET) are less than-0.01 mm d~(-1) yr~(-1). Inter comparisons among different models in different river basins suggest that PET_(Ham1) is the best model in the Pearl River basin, PET_(Ham2) outperforms other models in the Huaihe River, Yangtze River and Yellow River basins, and PET_(FAO) is the best model for the remaining basins. Sensitivity analyses reveal that wind speed and sunshine duration are two important factors for decreasing E_(PET) in most basins except in Southeast and Southwest China. The increasing E_(PET) trend in Southeast China is mainly attributed to the reduced relative humidity.     

地面有效辐射气候学模型评估和参数优化

基于中国19个辐射站1993-2012年的地面辐射平衡资料和气象资料,分析评估了布朗特法、彭曼法、别尔良德法、FAO24法、FAO56-PM法、邓根云法和童宏良法7种参数化方案计算中国地面有效辐射的适用性;并以均方根误差最小为目标函数,利用步长加速法和多元回归法迭代求解最优参数,建立适合于中国的最优参数化逐日有效辐射估算方法。结果表明:参与评估的7种方案都不同程度低估了中国的有效辐射;从全中国总体误差水平看,童宏良法的平均绝对百分比误差和均方根误差小于其他6种方案,分别为27.0%和24.5 W/m2,估算效果较好;其次是彭曼法和邓根云法;FAO56-PM法精度较低,不适用于中国的有效辐射估算。针对单站来说,邓根云法在东部平原地区的精度最高,童宏良法由于考虑了海拔高度的订正,适用于西部高原地区。相关分析表明水汽压是影响有效辐射估算误差的最关键因素,因此根据水汽压的地理分布规律,分东部区和西部区建立分区方案。基于观测资料建立的全中国方案和分区方案的均方根误差分别为20.8和21.4 W/m2,精度均高于已有参与评估的7种方案;而且在绝大多数站点,分区方案的误差小于全中国方案,所以划分东部区和西部区进行有效辐射模型参数化很有必要。同时发现,分区方案在西部区明显优于邓根云法,在东部区明显优于童宏良法,因此推荐其作为中国有效辐射的计算方法。      

不同方法在湖南省早稻产量动态预报中的比较

为了提高产量趋势预报的准确性和定量预报的准确率,利用1962—2002年气象、早稻产量和田间观测资料,建立基于气候适宜度、关键气象因子、作物生长模型的湖南省早稻产量动态预报方法,进行回代检验;并利用2003—2012年资料进行预报检验。分析表明:3种方法的预报准确率比较接近,平均在93.8%以上;基于气候适宜度预报方法的趋势预报准确性最高,较基于关键气象因子的预报方法高4%~6%;基于作物生长模型预报方法的误差5%以内样本百分率最高,较基于气候适宜度的预报方法高2%~20%。研究结果为湖南省早稻产量动态预报筛选出了较优的方法,即产量趋势预报选用基于气候适宜度的方法,定量预报选用基于作物生长模型的方法,同时可供我国其他早稻区的产量动态预报方法研究借鉴。     

Influence of Climate Change on Winter Wheat Growth in North China During 1950-2000

1. IntroductionChinese agriculture has undergone tremendousstructural changes over the last decades. The averagestaple crop productivity has doubled in 25 yr while thepopulation increased by 25 % [China Statistical Year-book (CSY), 2003]. Winter wheat is one of China'smost important staple food crops, with a total farm-ing area of nearly 24 million hectares and a produc-tion exceeding 92 million ton in 2002 (CSY, 2003).Although China has been the world's largest wheatproducer since 1983 (…     

四川省潜在蒸散量估算模型

Penman-Monteith法是FAO-56推荐的计算潜在蒸散量的标准方法, 但由于涉及的气象要素较多, 难于在业务中应用。以综合气象干旱指数的业务化应用为目标, 利用1971-2000年四川省156个气象站的观测资料, 以Penman-Monteith法计算结果作为标准,分析了Thornthwaite法和Hargreaves法对川西高原和四川盆地年、月潜在蒸散量的估算精度, 建立了可供业务应用的E_T0估算模型, 并应用于2006年四川省特大伏旱监测, 结果表明:Thornthwaite法反映不出E_T0的年际变化,在冬季显著偏小, 而Hargreaves法对E_T0的年际变化具有较好的反映能力, 与Thornthwaite法相比,其E_T0年、月估算值更接近于Penman-Monteith法标准值,且Hargreaves法估算值与Penman-Monteith法标准值之间具有较好的线性关系,引入风速和相对湿度两个订正因子后,Hargreaves订正值的误差可控制在10%以内, 基于该文E_T0估算模型计算的综合气象干旱指数对四川干旱具有较强的监测能力。     

Intercomparison and validation of snow albedo parameterization schemes in climate models

Snow albedo is known to be crucial for heat exchange at high latitudes and high altitudes, and is also an important parameter in General Circulation Models (GCMs) because of its strong positive feedback properties. In this study, seven GCM snow albedo schemes and a multiple linear regression model were intercompared and validated against 59 years of in situ data from Svalbard, the French Alps and six stations in the former Soviet Union. For each site, the significant meteorological parameters for modeling the snow albedo were identified by constructing the 95% confidence intervals. The significant parameters were found to be: temperature, snow depth, positive degree day and a dummy of snow depth, and the multiple linear regression model was constructed to include these. Overall, the intercomparison showed that the modeled snow albedo varied more than the observed albedo for all models, and that the albedo was often underestimated. In addition, for several of the models, the snow albedo decreased at a faster rate or by a greater magnitude during the winter snow metamorphosis than the observed albedo. Both the temperature dependent schemes and the prognostic schemes showed shortcomings.     

Retrieving daily global solar radiation from routine climate variables

Solar radiation is an important variable for studies related to solar energy applications, meteorology, climatology, hydrology, and agricultural meteorology. However, solar radiation is not routinely measured at meteorological stations; therefore, it is often required to estimate it using other techniques such as retrieving from satellite data or estimating using other geophysical variables. Over the years, many models have been developed to estimate solar radiation from other geophysical variables such as temperature, rainfall, and sunshine duration. The aim of this study was to evaluate six of these models using data measured at four independent worldwide networks. The dataset included 13 stations from Australia, 25 stations from Germany, 12 stations from Saudi Arabia, and 48 stations from the USA. The models require either sunshine duration hours (Ångstrom) or daily range of air temperature (Bristow and Campbell, Donatelli and Bellocchi, Donatelli and Campbell, Hargreaves, and Hargreaves and Samani) as input. According to the statistical parameters, Ångstrom and Bristow and Campbell indicated a better performance than the other models. The bias and root mean square error for the Ångstrom model were less than 0.25 MJ m² day^?1 and 2.25 MJ m² day^?1, respectively, and the correlation coefficient was always greater than 95 %. Statistical analysis using Student’s t test indicated that the residuals for Ångstrom, Bristow and Campbell, Hargreaves, and Hargreaves and Samani are not statistically significant at the 5 % level. In other words, the estimated values by these models are statistically consistent with the measured data. Overall, given the simplicity and performance, the Ångstrom model is the best choice for estimating solar radiation when sunshine duration measurements are available; otherwise, Bristow and Campbell can be used to estimate solar radiation using daily range of air temperature.     

Comparison of the performance of net radiation calculation models

Daily values of net radiation are used in many applications of crop-growth modeling and agricultural water management. Measurements of net radiation are not part of the routine measurement program at many weather stations and are commonly estimated based on other meteorological parameters. Daily values of net radiation were calculated using three net outgoing long-wave radiation models and compared to measured values. Four meteorological datasets representing two climate regimes, a sub-humid, high-latitude environment and a semi-arid mid-latitude environment, were used to test the models. The long-wave radiation models included a physically based model, an empirical model from the literature, and a new empirical model. Both empirical models used only solar radiation as required for meteorological input. The long-wave radiation models were used with model calibration coefficients from the literature and with locally calibrated ones. A measured, average albedo value of 0.25 was used at the high-latitude sites. A fixed albedo value of 0.25 resulted in less bias and scatter at the mid-latitude sites compared to other albedo values. When used with model coefficients calibrated locally or developed for specific climate regimes, the predictions of the physically based model had slightly lower bias and scatter than the empirical models. When used with their original model coefficients, the physically based model had a higher bias than the measurement error of the net radiation instruments used. The performance of the empirical models was nearly identical at all sites. Since the empirical models were easier to use and simpler to calibrate than the physically based models, the results indicate that the empirical models can be used as a good substitute for the physically based ones when available meteorological input data is limited. Model predictions were found to have a higher bias and scatter when using summed calculated hourly time steps compared to using daily input data.     

An evaluation of air quality modeling over the Pearl River Delta during November 2006

In this paper, we evaluate the performance of several air quality models using the Pearl River Delta (PRD) region, including the Nested Air Quality Prediction Modeling System (NAQPMS), the Community Multiscale Air Quality (CMAQ) model, and the Comprehensive Air Quality Model with extensions (CAMx). All three model runs are based on the same meteorological fields generated by the Fifth-Generation Pennsylvania State University/National Center for Atmospheric Research (PSU/NCAR) Mesoscale Model (MM5) and the same emission inventories. The emission data are processed by the Sparse Matrix Operator Kernel Emissions (SMOKE) model, with the inventories generated from the Transport and Chemical Evolution over the Pacific/Intercontinental Chemical Transport Experiment Phase B (TRACE-P/INTEX-B) and local emission inventory data. The results show that: (1) the meteorological simulation of the MM5 model is reasonable compared with the observations at the regional background and urban stations. (2) The models have different advantages at different stations. The CAMx model has the best performance for SO₂ simulation, with the lowest mean normalized bias (MNB) and mean normalized error (MNE) at most of the Guangzhou stations, while the CMAQ model has the lowest normalized mean square error (NMSE) value for SO₂ simulation at most of the other PRD urban stations. The NAQPMS model has the best performance in the NO₂ simulation at most of the Guangzhou stations. (3) The model performance at the Guangzhou stations is better than that at the other stations, and the emissions may be underestimated in the other PRD cities. (4) The PM₁₀ simulation has the best model measures of FAC2 (fraction of predictions within a factor of two of the observations) (average 53–56%) and NMSE (0.904–1.015), while the SO₂ simulation has the best concentration distribution compared with the observations, according to the quantile–quantile (Q–Q) plots.     

Daily soil temperature modeling using neuro-fuzzy approach

Soil temperature is an important meteorological parameter which influences a number of processes in agriculture, hydrology, and environment. However, soil temperature records are not routinely available from meteorological stations. This work aimed to estimate daily soil temperature using the coactive neuro-fuzzy inference system (CANFIS) in arid and semiarid regions. For this purpose, daily soil temperatures were recorded at six depths of 5, 10, 20, 30, 50, and 100 cm below the surface at two synoptic stations in Iran. According to correlation analysis, mean, maximum, and minimum air temperatures, relative humidity, sunshine hours, and solar radiation were selected as the inputs of the CANFIS models. It was concluded that, in most cases, the best soil temperature estimates with a CANFIS model can be provided with the Takagi–Sugeno–Kang (TSK) fuzzy model and the Gaussian membership function. Comparison of the models’ performances at arid and semiarid locations showed that the CANFIS models’ performances in arid site were slightly better than those in semiarid site. Overall, the obtained results indicated the capabilities of the CANFIS model in estimating soil temperature in arid and semiarid regions.