Some meteorological conditions associated with forest fires in Galicia (Spain)

The present paper offers data on the effect of certain meteorological parameters on the outbreak of forest fires in Galicia (Spain). In a day-to-day analysis, the values of the stability of the air column and the saturation deficit at the lower levels are related to the number of fires occurring in the following 24 h.     

Instrumental test to estimate a model of forecast yield: a non-parametric application to the pollen index in South Italy

A statistical test is described to verify the characteristics of the biological information contained in the dynamics of the flowering process. The test focuses on interactions between the pollen index and climatic variables to investigate if the biological indicator can synthesise the information of the pre-flowering phases. The multiple-regression model is built upon two pre-flowering climate macro-indicators extracted by Principal Component Analysis (PCA) and the optimised pollen index is obtained by non-parametric estimation. The empirical analysis is applied to 15 stations located in southern Italy in regions that have a longstanding tradition of olive production. Using the variance explained, we find that an optimised pollen index is fairly well predicted by the pre-flowering climatic data. We conclude that the optimised pollen index makes more parsimonious the modelling for predicting olive production.     

The role of air temperature in determining dormancy release and flowering ofCorylus avellana L.

Summary 7 years of airborne pollen monitoring in Perugia (central Italy) were used to determine the temperature requirements to break dormancy and to resume growth and bloom ofCorylus avellana L.,Corylus needs 1000 chill-units to complete its dormancy and this value, in the Perugian area, is met by the end of December or the first days of January. MoreoverCorylus trees require 220 growth degree hours before they are able to flower. If air temperature is high, this value can be achieved in only 10 days, but if the temperature remains too low, the heat accumulation can require up to 35 days. With these parameters it is possible to build a model to predict the date of the beginning ofCorylus avellana pollen season.     

Statistical analysis for the spatial validity of a model to forecast the daily number of forest fires

In earlier papers a qualitative and quantitative model was developed for predicting the number of forest fires occurring per day. This model permits the forecast at 00.00 hours Universal Time Convention (UTC) of any day (d), the number of forest fires per day for a range of several days (d tod+5) over a particular region. Input data are the number of forest fires in the region during two preceding days (d–2 andd–1) and the type of day (real and evaluated from radiosonde ford–2,d–1,d and predicted from meteorological medium-range forecasts, i.e. of European Centre, ford+1,d+2,d+3,d+4 andd+5. As this model requires data obtained by radiosonde, particularly temperatures and geopotentials at 850 and 700 hPa and dew points (or specific humidity) at 850 hPa, this study investigates the spatial validity of the model in relation to the distance from the radiosonde station (RS). The highest quality forecast is obtained for the region immediately surrounding the RS, and diminishes with increasing distance from it, this being due to the data obtained from the RS not being representative of the atmospheric column over the region. Hence, the derivation of the critical distance for a particular quality level of measurement. Conversely, fixed quality level implies a specific separation between RS and the region for the prediction, with a higher predictive quality implying a shorter distance.     

基于多源遥感数据的景观格局及预测研究

以TM、中巴资源卫星和环境与灾害监测预报小卫星等遥感影像为数据源,利用ENVI 4.7、ARCGIS 9.2、IDRISI 15等软件,研究了西安市辖区的景观特征与空间格局,预测了未来的景观变化,提出了景观格局预测的数据转化和多距离空间分析的精简步骤.结果表明:研究区的景观本底是一个由林地和耕地构成的复合景观基质,建设用地在研究时段内呈现持续增加趋势,且2004-2011年间的增加量高于2000-2004年间的增加量;林地面积略有降低,林地和草地总面积略呈增长趋势,水域和未利用地面积变化较小.研究时段内的景观破碎化程度在降低,林地景观的连通性增强了,耕地的降低了.各景观类型在所设定的最大预期研究尺度下均呈现显著的聚集空间格局;各年和各景观类型之间的聚集、随机和离散的临界阈值差别相对比较大;水域和未利用地的空间聚集强度明显高于耕地、林地、草地和城乡建设用地;耕地和草地空间分布存在一个异质性最大的特征尺度,且均出现了聚集分布、随机分布和离散分布3种分布格局,以2011年最为明显.利用景观指数法和多距离空间聚类分析方法研究景观格局特征的效果要比单一的景观指数法较理想.CA-Markov模型模拟的结果基本能够反映未来的景观格局状况.     

Teleconnection between the early immigration of brown planthopper (Nilaparvata lugens Stål) and ENSO indices: implication for its medium- and long-term forecast

Teleconnection between the early immigration of brown planthopper (BPH) and El Nino/Southern Oscillation (ENSO) indices from January of two years previously to the current June was investigated to make long-term forecast. The teleconnection results were as follows: ENSO indices which were significantly correlated with the early immigration of BPH were primarily sea surface temperature anomalies (SSTA) in N3, N4 and N3.4 regions, accounting for 71.8% of the total. Significant ENSO indices from two years and one year before the immigration events had a proportion of about 84%, while those in the current year only accounted for 16.7%. There was significantly negative correlation between the early immigration of BPH and SSTA in each Nino region from two years before to the previous spring, whereas there was significantly positive correlation between these two factors during the period from the previous winter to the current spring. The significant correlation between the early immigration of BPH and SSTA in each Nino region in the last summer and autumn did not show any obvious tendencies. The relationship between the southern oscillation index (SOI) and the early immigration of BPH was opposite to that between the immigration and SSTA in each Nino region. The above mentioned significant ENSO indices were used as key factors to build forecasting models for the early immigration of BPH by step-wise multiple linear regression analysis. Finally, 12 integrated forecasting models were obtained, which could make predictions 3–27 months ahead and had a predictive accuracy of 88.9%.     

小麦穗期麦长管蚜发生程度的预测模型研究

应用逐步判别分析方法,对山东省金乡县1982—1989年共8年小麦穗期麦长管蚜发生程度的历史观测资料进行了数量分析,建立了判别方程（预测模型）。将历史资料进行回检,符合率为100％,把1990年的观察资料作为独立样本进行试报,结果与实际相吻合。为害虫种群动态的预测预报提供了一种新的研究方法。     

Predicting walnut (Juglans spp.) crop yield using meteorological and airborne pollen data

Crop yield determines economy by influencing prices on the trade market, and so accurate forecasts of the yield are important for planning various aspects of agricultural production. The main aim of this study is to construct a model for predicting walnut yield in an important walnut production area (the region of Novi Sad in Northern Serbia). Relationships between the amount of walnuts produced annually (2000–2011) and abiotic (e.g. meteorological) and biotic (e.g. airborne pollen data) factors were examined using Pearson correlation analysis. Walnut yield data were then entered into linear regression models with variables that had the highest correlations. The models were constructed using 10 years of data, and tested using 2 years of data not included in constructing the model. This paper has shown that walnut yield is greatly dependent on weather conditions, particularly during fertilisation and seed growth, but the amount of available airborne pollen also plays an important role. The introduction of the seasonal pollen index, as a proxy for the amount of pollen available for fertilisation, improved the performance of models predicting walnut yield.     

模糊综合评判法在斜纹夜蛾预测预报上的应用

通过分析多年斜纹夜蛾灯下虫量、田间虫量以及气象资料,采用模糊综合评判的原理和方法,组建了斜纹夜蛾发生量模糊综合评判模型。应用该模型对2006年和2007年斜纹夜蛾田间发生量进行预测,预测结果与田间发生实况基本相符。     

Teleconnection between the early immigration of brown planthopper (Nilaparvata lugens St?l) and ENSO indices: implication for its medium- and long-term forecast

Teleconnection between the early immigration of brown planthopper (BPH) and El Nino/Southern Oscillation (ENSO) indices from January of two years previously to the current June was investigated to make long-term forecast. The teleconnection results were as follows: ENSO indices which were significantly correlated with the early immigration of BPH were primarily sea surface temperature anomalies (SSTA) in N3, N4 and N3.4 regions, accounting for 71.8% of the total. Significant ENSO indices from two years and one year before the immigration events had a proportion of about 84%, while those in the current year only accounted for 16.7%. There was significantly negative correlation between the early immigration of BPH and SSTA in each Nino region from two years before to the previous spring, whereas there was significantly positive correlation between these two factors during the period from the previous winter to the current spring. The significant correlation between the early immigration of BPH and SSTA in each Nino region in the last summer and autumn did not show any obvious tendencies. The relationship between the southern oscillation index (SOI) and the early immigration of BPH was opposite to that between the immigration and SSTA in each Nino region. The above mentioned significant ENSO indices were used as key factors to build forecasting models for the early immigration of BPH by step-wise multiple linear regression analysis. Finally, 12 integrated forecasting models were obtained, which could make predictions 3–27 months ahead and had a predictive accuracy of 88.9%.