三江平原典型下垫面FAO Penman-Monteith模型适用性分析

提高蒸散量估算精度对于研究地表能量和水分平衡具有重要意义.基于涡度相关系统测量值和小气候观测资料,比较分析了FAO Penman-Monteith模型对三江平原典型下垫面沼泽湿地、水稻和大豆田蒸散量的模拟效果,以探讨模型在该区的适用性.结果表明:作物系数采用FAO推荐值时,FAO Penman-Monteith模型对沼泽湿地蒸散量的模拟值明显高于测量值,平均高估81.8%,模拟效率(ME)为负值,说明该模型不适用于沼泽湿地;该模型能够模拟水稻和大豆田蒸散量季节变化,且对稻田的模拟效果明显优于大豆田.沼泽湿地、水稻和大豆田3种类型下垫面的作物系数(Kc)值与叶面积指数(LAI)均呈极显著正相关关系,大豆田的Kc值还与饱和水汽压差(VPD)呈极显著负相关关系.依据线性回归方程校正Kc值后,FAO Penman-Monteith模型对沼泽湿地、水稻和大豆田估算精度均显著提高,平均偏差(MBE)为-0.1~0.3 mm·d-1,均方根误差(RMSE)为0.50~0.67 mm·d-1,ME为0.69~0.85,对水稻田蒸散量的模拟效果最好.无论是否校正作物系数,FAO Penman-Monteith模型都适用于模拟三江平原稻田蒸散量,如果用于模拟沼泽湿地和大豆田蒸散量,则必须要校正作物系数.     

生态模型在农田蒸散及土壤水分模拟中的适用性评价

为评价生态模型在农田蒸散及土壤水分运动模拟中的适用性,利用2013—2015年南京农业气象测站观测数据,评估了BEPS(Boreal Ecosystem Productivity Simulator)模拟冬小麦农田生态系统逐日蒸散及与土壤水分动态的可靠性,并进一步开展了植被冠层蒸腾和农田土壤蒸发分离。模拟结果表明:BEPS适用于研究冬小麦农田蒸散量及土壤水分运动规律;由于考虑了叶片聚集指数和冬小麦根系垂直分布递减系数随生育期变动的参数化改进,BEPS分别可以解释2013—2014年和2014—2015年两个生长季农田生态系统蒸渗仪实测蒸散量变化的83%和74%,参数化改进前后模型效率ME相当(前:0.8,后0.74),标准差RMSE(前:1.50,后1.05),平均偏差MBE(前:0.5,后0.35),误差减小;两个生长季中,土壤蒸发占冠层上方总蒸散的比例随生育进程而变化,全生育期发散比平均值分别为34%和29%;BEPS模拟的0~40 cm土层深度土壤水分随时间变化趋势与实测值基本一致,可以解释78%以上的土壤水分实测值变化,并能快速地响应降水变化。本研究表明,生态模型可以用于模拟冬小麦农田蒸散和土壤水分变化,并有助于厘定农田冠层中难以区分的植被蒸腾和土壤蒸发的比例关系,可为进一步开展气候变化背景下的区域蒸散发评估及与之相联系的农田节水管理奠定基础。     

黄土高原春小麦农田蒸散及其影响因素

蒸散与水循环、能量平衡密切相关,是黄土高原雨养农田生态系统最重要的水通量之一。准确测定半干旱区农田生态系统蒸散,对增强陆气相互作用的理解以及科学应对气候变化有重要意义。采用涡度相关技术对黄土高原春小麦农田生态系统蒸散进行了观测,利用气象梯度系统进行环境因子观测;分析了春小麦农田生态系统蒸散日、季动态及其环境影响因子。结果表明,黄土高原半干旱区春小麦农田生态系统蒸散呈早晚低、中午高的"单峰型"日变化特征;最大日峰值出现在8月(0.22mm/h)。生长季蒸散日峰值高于非生长季。春小麦农田最大日蒸散率值相对较低,这可能与该地区干旱少雨的气候特征有关。农田蒸散且具有明显的季节动态,与降水季节分布密切相关。7、8月份降水较多,月蒸散量较高。全年蒸散量(318.0 mm)略低于年降水量(332.3 mm);蒸散量与降水量比值为95.7%。非生长季蒸散量显著低于生长季(4—9月);二者之比为0.26。农田蒸散随土壤含水量和空气温度(低于26℃)增大呈指数增长趋势;随空气相对湿度、太阳辐射、风速增大呈先增大后降低的二次曲线变化趋势。净辐射是黄土高原半干旱区农田生态系统蒸散主要环境控制因子,土壤含水量次之。     

作物农田蒸散计算模型的研究

农田蒸散是指田间条件下,作物棵间蒸发和蒸腾之和,它涉及土壤作物大气系统,受气象、作物和土壤等多种因素的制约。本文从田间试验出发,综合考虑影响农田蒸散的各种因素,建立了不同作物（棉花、玉米和冬小麦）农田蒸散的计算模型,为今后农业生产中的合理灌溉、节...     

森林、草地和农田典型植被蒸散量的差异

利用中国生态系统研究网络(CERN)12个试验站1990—2003年的气象资料,基于大气-土壤-植被传输(SVAT)模型模拟植被的蒸散量,分析了不同气候条件下森林、草地、农田3种植被类型蒸散的变化规律.结果表明:多年平均蒸散量从高到低依次为热带森林、亚热带农田、温带森林、温带农田和草地;对农田生态系统而言,水稻的蒸散量最高,其次为小麦,玉米最低;3种生态系统月蒸散量峰值的年际变化差异显著,森林、草地的月蒸散量峰值年际变化较稳定,农田则较明显.     

基于Landsat8 遥感影像和SEBS 模型的呼图壁县蒸散量时空格局分析

利用Landsat8 影像, 采用SEBS 模型, 结合呼图壁县气象站观测的温度风速、日照时数等气象数据, 对新疆昌吉回族自治州呼图壁县2013 年4 月22 日、6 月9 日、8 月28 日 10 月15 日的蒸散发量进行了估算。从时间上看, 估算结果存在明显的季节变化规律: 夏季最大, 春季次之, 秋季最小, 以耕地为例四天蒸散发量分别为: 1.938, 3.136, 2.641 和1.314 mm·day–1。从空间上看, 县域蒸散发量整体变化趋势为: 北部荒漠区<中部平原区<南部山区。四天当中最大值出现在南部山区6 月9 日达到了4.128mm·day–1。对SEBS 的估算结果与呼图壁县气象站的观测结果和利用彭曼公式计算的结果进行比较,表明SBES 模型的结果是合理的, 可以在实践中用来反映天山北坡典型县域蒸散量的时空变化特征。     

河南省冬小麦农田蒸散和作物系数

农田蒸散是农田水分消耗的主要方式,是农田管理和规划必须考虑的重要因素之一。本试验在郑州农业气象试验站开展,用Penman-Monteith公式计算了2017和2018年两年冬小麦越冬期-成熟期的参考作物蒸散量,利用大型称重式蒸渗仪观测了充分灌水(T2)和自然降水(T1)两种状况下冬小麦农田的实际蒸散量,进而计算充分灌溉下冬小麦的作物系数和自然降水条件下的冬小麦实际作物系数,并分析它们的变化规律及其与气象要素的相关关系。结果表明:不同水分条件下冬小麦农田蒸散量均呈现先升高后降低的单峰变化趋势,其中T2处理的蒸散量和波动幅度明显高于T1处理;冬小麦试验观测时期内,T2、T1处理两年总蒸散量均值分别为535.8和256.4 mm,日均蒸散量分别为3.7和1.7 mm;不同发育期日均蒸散量均是孕穗、抽穗期最高,越冬期最低;冬小麦作物系数明显高于自然降水条件下的实际作物系数,总体上均呈现降低-升高-降低的变化趋势; T1处理实际作物系数与空气湿度相关性最好,与平均气温相关性最差; T2处理作物系数与平均气温、总辐射和风速均有较好相关性,而与空气湿度相关性较差。     

涡度相关观测的能量闭合状况及其对农田蒸散测定的影响

涡度相关法被认为是测定农田蒸散量的标准方法。然而,能量不闭合现象在涡度相关测量中普遍存在。分析能量不闭合对涡度相关观测的影响,对于提高涡度相关观测精度具有重要意义。以蒸渗仪法为参照,探讨涡度相关观测的能量闭合状况对农田蒸散测定的影响,在导致涡度相关观测能量不闭合的诸多因素中,寻找对蒸散测定有影响的因素。结果表明:涡度相关观测的白天能量平衡比率(EBR)呈秋冬高、春夏低的变化特征,麦季日均EBR范围在0.26—2.84之间,平均1.15;玉米季日均EBR范围在0.19—2.59之间,平均0.78。无论麦季或玉米季,涡度相关法测定的平均蒸散量(ETec)均明显低于蒸渗仪法观测值(ETL),但两者显著相关(P<0.01),并有相似的季节变化。平均蒸散比(ETec/ETL)麦季约为0.61,玉米季约为0.50。在冬小麦田和夏玉米田,ETec/ETL均与EBR显著相关(P<0.01)。麦田种植密度大,下垫面较均匀,蒸散比与EBR成正比(P<0.01),且不受叶面积指数(LAI)大小影响;反之,玉米田种植密度小,只有当LAI>1,下垫面变得较均匀后,蒸散比与EBR的关系才变得显著(P<0.01)。风速小时ETec/ETL与EBR显著相关,风速增加时二者相关性减弱。尤其在玉米田,当摩擦风速(u*)大于0.3 m/s时,ETec/ETL与EBR的相关性不再显著。风速小时,大气湍流微弱,湍流的涡旋较大。在有限的观测时段(0.5h)内,涡度相关仪的传感器难以捕捉足够的湍涡能量,所测湍流能量偏低,导致能量不闭合。以上结果为应用能量平衡比率校正农田蒸散提供了可能途径。     

雨养玉米农田生态系统的蒸散特征及其作物系数

基于雨养玉米农田生态系统2007年整个生长季的涡度相关通量资料,对蒸散的日、季动态进行分析.结果表明：玉米农田生态系统蒸散的日、季动态均呈单峰型变化,最大值分别出现在12:00左右和7月.结合修正的Penman-Monteith公式与相应的生态、气象观测要素,对作物系数(K指数)影响因子的分析结果表明,K指数主要受叶面积指数(LAI)、气温(T_a)、净辐射(R_n)以及表层土壤含水量的影响.在此基础上,初步建立了半小时尺度的作物系数(K指数)模型.     

西北黑河中游荒漠绿洲农田作物蒸腾与土壤蒸发区分及作物耗水规律

利用中国生态系统研究网络临泽内陆河流域研究站绿洲农田2009年小气候、湍流交换、土壤蒸发和叶片气孔导度等综合观测试验数据,应用Shuttleworth-Wallace(S-W)双源模型以半小时为步长估算了绿洲农田玉米生长季实际蒸散量,并利用涡动相关与微型蒸渗仪实测数据对田间蒸散发量和棵间土壤蒸发量计算结果进行了检验。结果表明:S-W模型较好地估算研究区的蒸散量,并能有效区分农田作物蒸腾和土壤蒸发;全生育期玉米共耗水640 mm,其中作物蒸腾累积量为467 mm,土壤蒸发累积量为173 mm,分别占总量的72.9%和27.1%;日时间尺度上,作物蒸腾和土壤蒸发分别在0—6.3 mm/d和0—4.3 mm/d之间变化,其日平均分别为2.9和1.0 mm/d;田间供水充足,作物蒸腾与土壤蒸发比值明显受作物生长过程影响,播种—出苗期、出苗—拔节期、拔节—抽雄期、抽雄—灌浆期、灌浆—成熟期,其比值分别为0.04、0.8、7.0、5.2和1.4,不同阶段的比值差异主要受叶面积指数影响。     

三江平原典型沼泽湿地蒸散发估测

2006年5—9月,利用涡度相关技术对三江平原典型沼泽湿地蒸散发进行了连续观测,在分析生长季内沼泽湿地蒸散发时间动态的基础上,采用Penman-Monteith（PM）和Priestley-Taylor（PT）模型分别模拟了沼泽湿地的日蒸散发,并利用实测值对两种模型的模拟精度进行了验证.结果表明：生长季内（5—9月）,研究区沼泽湿地蒸散发具有明显的季节变化,月均日蒸散量在5月最低、7月最高;生长季内平均蒸散发为1.94 mm·d^-1,总蒸散量293 mm.生长季前期和后期,与蒸散发实测值相比,PM模型的模拟值存在明显低估现象;PT模型模拟值与实测值在整个生长季内的一致性较好,且PT模型的形式简单、所需参数少,更适于沼泽湿地的蒸散发模拟.     

Assessing the Role of Parameter and Input Uncertainty in Ecohydrologic Modeling: Implications for a Semi-arid and Urbanizing Coastal California Catchment

Ecohydrologic models are a key tool in understanding plant–water interactions and their vulnerability to environmental change. Although implications of uncertainty in these models are often assessed within a strictly hydrologic context (for example, runoff modeling), the implications of uncertainty for estimation of vegetation water use are less frequently considered. We assess the influence of commonly used model parameters and inputs on predictions of catchment-scale evapotranspiration (ET) and runoff. By clarifying the implications of uncertainty, we identify strategies for insuring that the quality of data used to drive models is considered in interpretation of model predictions. Our assessment also provides insight into unique features of semi-arid, urbanizing watersheds that shape ET patterns. We consider four sources of uncertainty: soil parameters, irrigation inputs, and spatial extrapolation of both point precipitation and air temperature for an urbanizing, semi-arid coastal catchment in Santa Barbara, CA. Our results highlight a seasonal transition from soil parameters to irrigation inputs as key controls on ET. Both ET and runoff show substantial sensitivity to uncertainty in soil parameters, even after parameters have been calibrated against observed streamflow. Sensitivity to uncertainty in precipitation manifested primarily in winter runoff predictions, whereas sensitivity to irrigation manifested exclusively in modeled summer ET. Neither ET nor runoff was highly sensitive to uncertainty in spatial interpolation of temperature. Results argue that efforts to improve ecohydrologic modeling of vegetation water use and associated water-limited ecological processes in these semi-arid regions should focus on improving estimates of anthropogenic outdoor water use and explicit accounting of soil parameter uncertainty.     

The regulation of activity in populations of the terrestrial slugLimax maximus (Gastropoda; Limacidae)

The activity of the slug Limax maximus was studied in relation to weather. Three hundred-and-fifty-eight hourly observations of activity and weather were made on 21 nights from May until October, 1976. Factors causally important to molluscan activity were included in a step-down correlation-regression analysis of daily and seasonal behavior. The analysis was also performed using weather data from the previous hourly observation. Models using lag-weather did not explain as much variability as did concurrent weather. The regression models explained about 73% to 87% of the observed variation in activity. The most important factors included in the regression models were time of day (circadian rhythm), light intensity, changes in light intensity and surface temperature. Shelter temperature, temperature gradients, length of the night, and time of sunset were also included in some models. Age and hydration were shown to be key factors in other experiments. A model incorporating weather thresholds estimated from field data explained 83.06% of the variability in the activity of L. maximus over the season. The values predicted from the model did not differ significantly from those actually observed in the field (Kolmogorov-Smirnov test, p>0.50).     

Study of Saharan dust influence on PM10 measures in Sicily from 2013 to 2015

Nowadays, particulate matter, especially that with small dimension as PM₁₀, PM_2.5 and PM_1, is the air quality indicator most commonly associated with a number of adverse health effects. In this paper it is analyzed the impact that a natural event, such as the transport of Saharan dust, can have on increasing the particulate matter concentration in Sicily.Consulting the data of daily PM₁₀ concentration, acquired by air quality monitoring network belonging to “Agenzia Regionale Protezionedell’ Ambiente” (Environmental Protection Regional Agency), it was possible to analyze the trend from 2013 to 2015. The days, in which the limit value was exceeded, were subjected to combined analysis. It was based on three models: interpretations of the air masses back-trajectories, using the atmospheric model HYSPLIT (HYbrid Single-Particle Lagrangian Integrated trajectory); on the calculation of the concentration on the ground and at high altitude particulate applying DREAM model (Dust REgional atmospheric model) and on the calculation of the concentration of mineral aerosols according to the atmospheric optical thickness (AOT) applying NAAPS model (Navy Aerosol Analysis and Prediction System).The daily limit value exceedances were attributed to the transport of Saharan dust events exclusively when the three models were in agreement with each other. Identifying the natural events, it was possible to quantify the contribution of the Saharan dust and consequently the reduction of the exceedances number. To quantify the contribution of Saharan dust on daily PM₁₀ concentration, it was calculated the regional background in according to precautionary approach recommended by “Guidance on the quantification of the contribution of natural sources under the EU Air Quality Directive 2008/50/EC”, when the application of the method cannot be validated with chemical analysis, as in this case. In this study is obtained, as the most important quantitative goal, the convergence of the three models to the same result. So, is evident that exceedances of the daily limit value that occurred from 2013 to 2015 in Sicily can be attributed, in most cases, to the Saharan dust intrusion.     

Day-to-day variation in nitrogenase activity of Alnus incana explained by weather variables: a multivariate time series analysis

A modelling system is described that indicates the extent to which day-to-day variations in nitrogenase activity in young Alnus incana (L.) Moench, grown in defined conditions in the field, may be affected by weather conditions both during and prior to the day of measurement. Nitrogenase activity (acetylene reduction activity, ARA) was measured weekly on intact field-grown grey alder (A. incana) plants, 0.15–0.42 m tall at planting, nodulated with Frankia. The measurements were done at noon on two groups of plants in 1987 and on two other groups in 1988. Each group was made up of five or six plants. Seven weather variables: daily sunshine hours, daily mean, maximum and minimum air temperature, daily mean and 1300 h relative humidity, and daily rainfall were used. The relation between log(ARA/leaf area) and the weather variables were analysed using a PLS model (partial least squares projection to latent structures). The advantage of PLS is that it can handle x-variables that are correlated. Data from 1987 were chosen as a training set. Multivariate PLS time series analysis was made by adding, in a stepwise manner, the weather data up to 5 d before the day of measurement. This procedure gave six models with n * 7 x-variables (n= 1–6). With the models from the time series analysis of 1987 data, true predictions of ARA per leaf area were made from weather data 1988 (test set 1) and from ‘early-season’ weather data from 1987 and 1988 (test set 2). The variation in ARA/leaf area could be predicted from the weather conditions. The predictions of the two test sets improved when the weather conditions one and two days before the day of measurements were added to the model. The further addition of weather data from 3 to 5 d before the day of measurement did not improve the model. The good predictions of ARA/leaf area show that the alders responded to the variable weather conditions in the same way in 1988 as in 1987, despite the ten-fold difference in size (leaf area) at the end of the growing season. Among the weather variables, air temperature and the daily sunshine hours were positively correlated to ARA, while relative air humidity and rainfall were negatively correlated to ARA. The daily minimum temperature and rainfall appeared to have least impact on ARA. By use of PLS, we could extract information out of a data set containing highly correlated x-variables, information that is non-accessible with conventional statistical tools such as multiple regression. When making measurements of nitrogenase activities under field conditions, we propose that attention should be paid to the weather conditions on the days preceding the day of measurement. The day-to-day variation in nitrogenase activity is discussed with reference to known effects of stress factors under controlled conditions.     

极值分布理论在广东寒害重现期预测中的应用

寒害是广东省继洪涝、台风之后的第三大灾害性天气,预测寒害重现期对科学防寒减灾具有实际意义.本研究基于广东省86个县(市)气象站1961-2015年冬季(12月-翌年2月)逐日气象资料,以积寒指数为寒害指标,采用Gumble分布、Weibull分布、对数正态分布和Pearson-Ⅲ型分布4个模型对各站寒害极值进行概率分布拟合,并检验筛选最优模型,计算不同重现期的寒害极值.结果表明: 广东省86个县(市)气象站中,有77个站适用Pearson-Ⅲ型分布,8个站适用对数正态分布,1个站用Gumble分布拟合最佳,Weibull分布函数不适用于广东寒害极值分布的拟合.根据各站最优拟合分布函数,预测广东86个站点10、25、50和100年寒害重现期,其相对误差均较小(≤6%);其多年一遇的积寒极值呈明显的纬向分布特征,表现为北多南少,与寒害发生过程中最低气温、平均气温、降温幅度等分布特征一致.研究成果可为广东相关行业科学防寒提供依据.     

Added-value of mosquito vector breeding sites from street view images in the risk mapping of dengue incidence in Thailand

Dengue is an emerging vector-borne viral disease across the world. The primary dengue mosquito vectors breed in containers with sufficient water and nutrition. Outdoor containers can be detected from geotagged images using state-of-the-art deep learning methods. In this study, we utilize such container information from street view images in developing a risk mapping model and determine the added value of including container information in predicting dengue risk. We developed seasonal-spatial models in which the target variable dengue incidence was explained using weather and container variable predictors. Linear mixed models with fixed and random effects are employed in our models to account for different characteristics of containers and weather variables. Using data from three provinces of Thailand between 2015 and 2018, the models are developed at the sub-district level resolution to facilitate the development of effective targeted intervention strategies. The performance of the models is evaluated with two baseline models: a classic linear model and a linear mixed model without container information. The performance evaluated with the correlation coefficients, R-squared, and AIC shows the proposed model with the container information outperforms both baseline models in all three provinces. Through sensitivity analysis, we investigate the containers that have a high impact on dengue risk. Our findings indicate that outdoor containers identified from street view images can be a useful data source in building effective dengue risk models and that the resulting models have potential in helping to target container elimination interventions.     

Poaceae pollen in the atmosphere of Tetouan (NW Morocco): effect of meteorological parameters and forecast of daily pollen concentration

The Poaceae pollen season has been characterized in Tetouan during a 7-year period, and the effect of weather conditions on daily concentrations was examined. The forecast models were produced using a stepwise multiple regression analyses. Firstly, three models were constructed to predict daily Poaceae pollen concentrations during the main pollen season, as well as the pre-peak and post-peak periods with data from 2008 to 2012 and tested on data from 2013 and 2014. Secondly, the regression models using leave-one-out cross-validation were produced with data obtained during 2008–2014 taking into account meteorological parameters and mean pollen concentrations of the same day in other years. The duration of the season ranged from 70 days in 2009 to 158 days in 2012. The highest amount of Poaceae pollen was detected in spring and the first fortnight of July. The annual sum of airborne Poaceae pollen concentrations varied between 2100 and 6251. The peak of anthesis was recorded in May in six of the other years studied. The regression models accounted for 36.3–85.7% of variance in daily Poaceae pollen concentrations. The models fitted best when the mean pollen concentration of the same day in other years was added to meteorological variables, and explained 78.4–85.7% of variance of the daily pollen changes. When the year 2014 was used for validating the models, the lowest root-mean-square errors values were found between the observed and estimated data (around 13). The reasonable predictor variables were the mean pollen concentration of the same day in other years, mean temperature, precipitations, and maximum relative humidity.