1961~2020年中国两大林区森林火险天气的多尺度特征北大核心CSCD

近年来,世界各地的极端林火事件已呈现出多发态势。林火作为一种复合型极端事件,它的发生和蔓延与气象条件有着密切联系,在全球变暖的背景下,研究林区森林火险天气的变化特征可以为“碳中和”背景下的森林防火工作提供科学的信息。本文以逐日森林火险气象指数(FFDI)作为火险天气的度量指标,分析了该指数的适用性及空间分布特征,进而分析了1961~2020年东北和西南两大林区FFDI及相关气象因子的线性变化趋势;最后利用集合经验模分解(EEMD)的方法揭示了两大林区防火期FFDI的多时间尺度演变特征。研究发现:在季节以及年时间尺度上,FFDI的分布具有明显的区域特征,东北地区在春季、秋季处于高值期,而西南地区则集中在春季和冬季,这与两大林区的森林防火期有很好的对应关系。各个季节FFDI呈现显著增长的站点数在10%~20%左右,春季最多(21%)。东北林区FFDI的变化趋势在四季都不显著;但相关气象因子中的日最高气温在四季都呈现显著的变暖趋势,平均风速在四季都呈现显著减弱的趋势。西南林区四季的FFDI都呈现显著(至少是0.1水平下的)增长态势,其中春、冬季防火期的趋势分别为0.09/10 a(P<0.1)和0.05/10 a(P<0.1);夏、秋、冬三个季节显著变暖又显著变干(P<0.05),朝着“暖干化”的气候特征演变。年际变率在两大林区FFDI的演变中贡献超70%;东北春季和秋季防火期FFDI的非线性趋势分别呈先快速上升后减缓和先快速上升后转为下降的趋势;西南春季防火期FFDI的非线性趋势从上个世纪的稳定少变转为21世纪开始呈现快速上升的趋势,冬季防火期FFDI则总体呈稳步上升趋势。因此,西南林区的防火形势正变得愈发地严峻。     

Study on forest fire danger over Northern China during the recent 50?years

Daily meteorological data at 263 stations in northern China from 1956 to 2005 were used to calculate various forest fire danger weather (FFDW) indices, such as Nesterov Index (NI), Modified Nesterov Index (MNI), Keetch-Byram Drought Index (KBDI), and Forest Fire Danger Index (FFDI), at different time scales. The relationship between each index and forest fire was analyzed. MNI and FFDI were then selected to study the impact on forest fire danger due to climatic change in northern China in the recent 50?years. Results show that forest fire danger has significantly increased in Northeast China where there is the richest forest resource in China, and also increased in North China. However, it has not changed much in eastern part of Northwest China, and the forest fire danger has even significantly decreased in northern Xinjiang. Significant rise of forest fire danger in Northeast China mainly results from the co-effect of increase of temperature, and decreases of humidity and precipitation. Relative humidity change dominants forest fire danger trends in the four forest regions in northern China.     

Short-term droughts forecast using Markov chain model in Victoria,Australia

A comprehensive risk management strategy for dealing with drought should include both short-term and long-term planning. The objective of this paper is to present an early warning method to forecast drought using the Standardised Precipitation Index (SPI) and a non-homogeneous Markov chain model. A model such as this is useful for short-term planning. The developed method has been used to forecast droughts at a number of meteorological monitoring stations that have been regionalised into six (6) homogenous clusters with similar drought characteristics based on SPI. The non-homogeneous Markov chain model was used to estimate drought probabilities and drought predictions up to 3 months ahead. The drought severity classes defined using the SPI were computed at a 12-month time scale. The drought probabilities and the predictions were computed for six clusters that depict similar drought characteristics in Victoria, Australia. Overall, the drought severity class predicted was quite similar for all the clusters, with the non-drought class probabilities ranging from 49 to 57 %. For all clusters, the near normal class had a probability of occurrence varying from 27 to 38 %. For the more moderate and severe classes, the probabilities ranged from 2 to 13 % and 3 to 1 %, respectively. The developed model predicted drought situations 1 month ahead reasonably well. However, 2 and 3 months ahead predictions should be used with caution until the models are developed further.

     

Rainfall uncertainty in the Mediterranean: Intraseasonal rainfall distribution

The rainfall distribution within the rainy season has crucial implications on a variety of disciplines. According to one approach of analyzing the intraseasonal rainfall distribution, it is essential to examine the date of different accumulated percentage (DAP hereafter), as presented in Paz and Kutiel (Isr J Earth Sci 52:47–63, 2003). The present study identifies various intraseasonal temporal distributions of rainfall, in 41 stations within the Mediterranean basin. Furthermore, classifications of these distributions according to their time, yield, and length are presented. The accumulated percentage was calculated for each Julian day for every available year in all stations. A correlation matrix between every possible pair of years, in each station, was calculated, and a cluster analysis (average linkage method) was performed. Finally, the averages of the entire dataset and the average of every cluster were compared in order to classify the clusters by using three parameters: timing represented by DAP_{(25%, 50%, 75%)} annual rainfall total and the rainy season length (RSL). Between 2 and 5 different types of clusters, with various probabilities, were defined for every station. Out of 132 overall clusters, which were found in 41 stations, the most frequent type (cluster 1) was the median in all three parameters. There were 16 clusters identified as short in their RSL, and 18 were identified as having a long classification. There were 19 dry clusters, and only eight were identified as wet. As for the parameter of timing, 39 clusters were classified as early and 38 as late. One conclusion of this study was that the probability of a dry year is higher than a wet one, and likewise, the probability of a long year is higher than of a short one.     

Development of air mass climatology analysis for the determination of characteristic marine atmospheres. Part I: North Atlantic

Summary A method of determining characteristic marine atmospheres was developed based on analysis of air mass types and occurrences. A 10-year set of radiosonde and surface observations collected over the North Atlantic Ocean from a combination of land-based stations, oceanographic buoys, and weather ships, was obtained from the archives of meteorological agencies from 6 different countries. Through a mixture of man-machine objective analysis of upper air measurements of air temperature at the 85, 70, and 50 kPa levels, a total of 16 typical air mass categories could be identified. They are cA, cA/cmA, cA/cmA/mA, cA:cmA, cmA, cmA/mA, cmA/mA/mP, cmA:mA, mA, mA/mP, mA/mP/mT, mP, mP/mT, mT(f), mT, and mT(d). Analysis of the data showed that regions of near air mass homogeneity can be identified. The frequency of occurrence of single air mass categories coincided with their source regions, and the categories with combinations of air masses coincided with well known storm tracks. Vertical profiles of characteristic categories were then developed and applied to the modelling of evaporative duct heights and calculations of precipitable water. These test results showed strong dependence of derived parameters on the defining air mass category and compared well with data from other independent studies. The value of this type of analysis has been demonstrated and results have been produced which can be used in downscaling studies whereby changes in the occurrence of regional weather elements may be found that are associated with variations in large-scale air mass features.With 6 Figures     

A comprehensive analysis of physiologically equivalent temperature changes of Iranian selected stations for the last half century

As a preliminary and major step for land use planning of the coming years, the study of variability of the past decades’ climatic conditions with comprehensive indicators is of high importance. Given the fact that one of the affected areas by climatic change includes variability of thermal comfort, this study uses the physiologically equivalent temperature (PET) to identify and evaluate bioclimatic conditions of 40 meteorological stations in Iran. In this study, PET changes for the period of 1960 to 2010 are analyzed, with the use of Mann-Kendall non-parametric test and Pearson parametric method. The study focuses particularly on the diversity in spatio-temporal distribution of Iran’s bioclimatic conditions. The findings show that the mean frequency percentage of days with comfort is 12.9 % according to the total number of selected stations. The maximum and minimum frequency percentage with values of 17.4 and 10.3 belong to Kerman and Chabahar stations, respectively. The findings of long-term trend analysis for the period of 1960–2010 show that 55 % of the stations have significant increasing trend in terms of thermal comfort class based on the Pearson method, while it is 40 % based on Mann-Kendall test. The results indicate that the highest frequency of days with thermal comfort in the southern coasts of Iran relates to the end of autumn and winter, nevertheless, such ideal conditions for the coastal cities of Caspian Sea and even central stations of Iran relate to mid-spring and mid-autumn. Late summer and early autumn along with late spring can be identified as the most ideal times in the west and northwest part of Iran. In addition, the most important inhibiting factors of thermal comfort prove to be different across the regions of Iran. For instance, in the southern coasts, warm to very hot bioclimatic events and in the west and northwest regions, cold to very cold conditions turn out to be the most important inhibiting factors. When considering the variations across the studied period, an increase in the frequency of thermal comfort condition is observed in almost half of the stations. Moreover, based on Pearson and Mann-Kendall methods, the trend of changes in monthly averages of PET has decreased in most stations and months, which can lead to different consequences in each month and station. Thus, it is expected that due to PET changes in recent decades and to the intensified global warming conditions, Iran’s bioclimatic conditions change in a way that transfers the days with comfort to early spring and late autumn.     

An investigation of near surface wind speed trends in arid and semiarid regions of Iran

Wind plays an important role on the ecosystems and hydrological cycles besides other meteorological parameters such as temperature, precipitation, sunshine, and relative humidity. It strongly affects evapotranspiration, especially in arid and semiarid regions where there are serious problems in regard to water resource management. Evaluating the wind speed trend can provide good information for future agricultural planning. This study was conducted in order to investigate the wind speed trends over 24 synoptic meteorological stations located in arid and semiarid regions of Iran from 1975 to 2005. Near-surface wind speed was trended by nonparametric Mann–Kendall test spatially and temporally in three time scales (annual, seasonal, and monthly). Then, Sen’s slope estimator was used to determine the amount of the changes; furthermore, 10-year moving average low-pass filter was applied to show general trends. Finally, the smoothed time series derived from the mentioned filter were classified in three clusters for each time series and then mapped to show their spatial distribution pattern. Results showed insignificant and significant, increasing and decreasing trends during the surveyed time. Wind speeds in less than 50 % of stations changed statistically in all time scales, and in most cases, the frequency of the upward trends was more than that of downward ones. The spatial distribution of significant wind speed showed that the increase mostly occurred in eastern part. Clustering gave us the turning point around 1990. Clearly, when clusters were mapped, they indicated the same pattern as the Z value maps derived from Mann–Kendall test which meant that the outputs of the mentioned method confirmed the other one. As the wind speed trends in different stations likely to follow the previous evapotranspiration (ET₀) trend results in Iran, it confirms that wind speed was an effective parameter on ET₀, even though other parameters should be considered too.     

Fire activity in Borneo driven by industrial land conversion and drought during El Niño periods, 1982–2010

Tropical rainforests, naturally resistant to fire when intact, are increasingly vulnerable to burning due to ongoing forest perturbation and, possibly, climatic changes. Industrial-scale forest degradation and conversion are increasing fire occurrence, and interactions with climate anomalies such as El Niño induced droughts can magnify the extent and severity of fire activity. The influences of these factors on fire frequency in tropical forests has not been widely studied at large spatio-temporal scales at which feedbacks between fire reoccurrence and forest degradation may develop. Linkages between fire activity, industrial land use, and El Niño rainfall deficits are acute in Borneo, where the greatest tropical fire events in recorded history have apparently occurred in recent decades. Here we investigate how fire frequency in Borneo has been influenced by industrial-scale agricultural development and logging during El Niño periods by integrating long-term satellite observations between 1982 and 2010 – a period encompassing the onset, development, and consolidation of its Borneo’s industrial forestry and agricultural operations as well as the full diversity of El Niño events. We record changes in fire frequency over this period by deriving the longest and most comprehensive spatio-temporal record of fire activity across Borneo using AVHRR Global Area Coverage (GAC) satellite data. Monthly fire frequency was derived from these data and modelled at 0.04° resolution via a random-forest model, which explained 56% of the monthly variation as a function of oil palm and timber plantation extent and proximity, logging intensity and proximity, human settlement, climate, forest and peatland condition, and time, observed using Landsat and similar satellite data. Oil-palm extent increased fire frequency until covering 20% of a grid cell, signalling the significant influence of early stages of plantation establishment. Heighted fire frequency was particularly acute within 10 km of oil palm, where both expanding plantation and smallholder agriculture are believed to be contributing factors. Fire frequency increased abruptly and dramatically when rainfall fell below 200 mm month⁻¹, especially as landscape perturbation increased (indicated by vegetation index data). Logging intensity had a negligible influence on fire frequency, including on peatlands, suggesting a more complex response of logged forest to burning than appreciated. Over time, the epicentres of high-frequency fires expanded from East Kalimantan (1980’s) to Central and West Kalimantan (1990’s), coincidentally but apparently slightly preceding oil-palm expansion, and high-frequency fires then waned in East Kalimantan and occurred only in Central and West Kalimantan (2000’s). After accounting for land-cover changes and climate, our model under-estimates observed fire frequency during ca. 1990–2002 and over-estimates it thereafter, suggesting that a multi-decadal shift to industrial forest conversion and forest landscapes may have diminished the propensity for high-frequency fires in much of this globally significant tropical region since ca. 2000.     

中国降水区划模糊聚类软划分法

利用软划分模糊聚类分析法,对中国160个观测站的近50年的年均降水数据进行研究,按降水量的多少和年际变化规律,把中国降水区域划分n类降水带,并研究了聚类参数对于划分结果的影响.进一步研究了经典的5类降水带划分法,发现了一些被分入某一降水带的地区可能具有较大的与其他类别的相似性(如通辽,大连等),研究结论提供了更丰富的隶...     

Fitting optimum order of Markov chain models for daily rainfall occurrences in Peninsular Malaysia

The analysis of the daily rainfall occurrence behavior is becoming more important, particularly in water-related sectors. Many studies have identified a more comprehensive pattern of the daily rainfall behavior based on the Markov chain models. One of the aims in fitting the Markov chain models of various orders to the daily rainfall occurrence is to determine the optimum order. In this study, the optimum order of the Markov chain models for a 5-day sequence will be examined in each of the 18 rainfall stations in Peninsular Malaysia, which have been selected based on the availability of the data, using the Akaike’s (AIC) and Bayesian information criteria (BIC). The identification of the most appropriate order in describing the distribution of the wet (dry) spells for each of the rainfall stations is obtained using the Kolmogorov-Smirnov goodness-of-fit test. It is found that the optimum order varies according to the levels of threshold used (e.g., either 0.1 or 10.0 mm), the locations of the region and the types of monsoon seasons. At most stations, the Markov chain models of a higher order are found to be optimum for rainfall occurrence during the northeast monsoon season for both levels of threshold. However, it is generally found that regardless of the monsoon seasons, the first-order model is optimum for the northwestern and eastern regions of the peninsula when the level of thresholds of 10.0 mm is considered. The analysis indicates that the first order of the Markov chain model is found to be most appropriate for describing the distribution of wet spells, whereas the higher-order models are found to be adequate for the dry spells in most of the rainfall stations for both threshold levels and monsoon seasons.     

Assessment of mesoscale convective systems using IR brightness temperature in the southwest of Iran

In this research, the spatial and temporal distribution of Mesoscale Convective Systems was assessed in the southwest of Iran using Global merged satellite IR brightness temperature (acquired from Meteosat, GOES, and GMS geostationary satellites) and synoptic station data. Event days were selected using a set of storm reports and precipitation criteria. The following criteria are used to determine the days with occurrence of convective systems: (1) at least one station reported 6-h precipitation exceeding 10 mm and (2) at least three stations reported phenomena related to convection (thunderstorm, lightning, and shower). MCSs were detected based on brightness temperature, maximum areal extent, and duration thresholds (228 K, 10,000 km², and 3 h, respectively). An MCS occurrence classification system is developed based on mean sea level, 850 and 500 hPa pressure patterns.

The results indicated that the highest frequency of MCSs occurred in December and April. Assessment of MCSs spatial frequency showed that MCS occurrence is strongly correlated with topography in April and May unlike the cold months. In other words, the role of Zagros Mountains in developing MCSs varies based on the season so that its impact increases with enhancement of mean monthly temperature. In addition, the occurrence of MCSs depends closely on the configuration of the Sudan Low in the southwest of Iran.

     

Streamflow drought severity analysis by percent of normal index (PNI) in northwest Iran

The streamflow drought is the most important type of drought due to the high dependence of many activities on surface water resources. The streamflow drought severity was identified by the percent of normal index (PNI) in the western basins of the Lake Urmia located in northwest Iran. The streamflow records were obtained from 14 hydrometric stations for the period October 1975–September 2009. The temporal trends of the streamflow drought severity were detected by the parametric Student’s t test and the nonparametric Mann–Kendall and Sen’s tests. The worst streamflow droughts at almost all the stations occurred in 1999–2000 and 2000–2001. The streamflow drought severity based on the PNI increased during the last 34 years. The results also indicated that the temporal dependency of time series had a dominant role in detecting trend by the parametric Student’s t test.     

A survey of temporal and spatial reference crop evapotranspiration trends in Iran from 1960 to 2005

Reference crop evapotranspiration (ET₀) is one of the most important climatic parameters which plays a key role in estimating crop water demand and scheduling irrigation. Under global warming and climate change conditions, it is needed to survey the trend of ET₀ in Iran. In this study, ET₀ values were determined based on FAO-56 Penman-Monteith equation over 32 synoptic meteorological stations during 1960–2005; and analyzed spatially and temporally in monthly, seasonal and annual time scales. After removing the significant lag-1 serial correlation effect by pre-whitening, non-parametric statistical Mann–Kendall (MK) test was used to detect the trends. The slope of the changes was determined by Sen’s slope estimator. In order to facilitate in trend analysis, the 10 moving average low pass filter were also applied on the normalized annual ET₀ time series. Annual ET₀ time series and filtered ones were then classified by hierarchical clustering in three clusters and then mapped in order to show the patterns of different clusters. Results showed that the significant decreasing trends were more considerable than increasing ones. Among surveyed stations, and on an annual time scale, the highest and lowest annual values of Sen’s slope estimator were observed in Tabas with (+) 72.14 mm per decade and Shahrud with (?) 62.22 mm per decade, respectively. Results also indicated that the clustered map based on normalized and filtered annual ET₀ time series is in accordance with another map which showed spatial distribution of increasing, decreasing and non-significant trends of ET₀ on annually time scale. Exploratory and visual analysis of smoothed time series showed increasing trend in recent years especially after 1980 and 1995. In brief, the upward trend of ET₀ in recent years is a crucial issue with regard to the high cost of dam construction for agricultural aims in arid and semi-arid regions e.g. Iran.     

Falling Lake Victoria water levels: Is climate a contributing factor?

Recently, and perhaps most threatening, Lake Victoria water level has been receding at an alarming rate. A recent study suggested the possibility of the expanded hydroelectric power station in Uganda. However, since the lake receives 80% of its refill through direct rainfall and only 20% from the basin discharge, climatic contributions cannot be ignored, since the 80% water is directly dependant on it. It is therefore necessary to investigate climatic contribution to the declining Lake Victoria water level observed over a long period, i.e., 30 years. This contribution uses 30 years period anomalies for rainfall, river discharge and lake level changes of stations within Lake Victoria basin to analyse linear and cyclic trends of climate indicators in relation to Lake levels. Linear trend analysis using the Student’s t test indicate a decreasing pattern in rainfall anomalies, with the slope being statistically similar to those of water levels at both Kisumu, Maziba and Jinja stations for the same period of time (1976–1999), thus showing a strong correlation. On the other hand, cyclic trend analysis using Discrete Fourier Transform (DFT) shows cyclic period of water level to coincide with those of droughts and rainfall. The strong relationship between climatic indicators of drought and rainfall on one-hand and lake levels on the other hand signifies the need to incorporate climate information in predicting, monitoring and managing lake level changes.     

Exceptionally hot summers in Central and Eastern Europe (1951–2010)

The paper focuses on exceptionally hot summers (EHS) as a manifestation of contemporary climate warming. The study identifies EHS occurrences in Central and Eastern Europe and describes the characteristic features of the region’s thermal conditions. Average air temperatures in June, July and August were considered, as well as the number of days with maximum temperatures exceeding 25, 30 and 35 °C, and with a minimum temperature greater than >20 °C, as recorded at 59 weather stations in 1951–2010. Extremely hot summers are defined as having an average temperature equal to or greater than the long-term average plus 2 SD. A calendar of EHSs was compiled and their spatial extent identified. The region experienced 12 EHSs, which occurred in a given year at 5 % or more stations (1972, 1981, 1988, 1992, 1997, 1998, 1999, 2002, 2003, 2006, 2007 and 2010). The EHS frequency of occurrence was found to be clearly on an increase. Indeed, only one EHS occurred during the first 30 years, but these occurred five times during the last 10 years of the study period. Their geographical extent varied both in terms of location and size. EHSs were observed at 57 out of the total of 59 weather stations in the study (the exceptions were Pecora and Cluj). The average air temperature of EHSs tended to exceed the relevant long-term average by 2–4 °C. The summer of 2010 was among the hottest (temperature anomaly 5.5–6 °C) and spatially largest.     

Selecting the best performing fire weather indices for Austrian ecoregions

The interpretation and communication of fire danger warning levels based on fire weather index values are critical for fire management activities. A number of different indices have been developed for various environmental conditions, and many of them are currently applied in operational warning systems. To select an appropriate combination of such indices to work in different ecoregions in mountainous, hilly and flat terrain is challenging. This study analyses the performance of a total of 22 fire weather indices and two raw meteorological variables to predict wildfire occurrence for different ecological regions of Austria with respect to the different characteristics in climate and fire regimes. A median-based linear model was built based on percentile results on fire days and non-fire days to get quantifiable measures of index performance using slope and intercept of an index on fire days. We highlight the finding that one single index is not optimal for all Austrian regions in both summer and winter fire seasons. The summer season (May–November) shows that the Canadian build-up index, the Keetch Byram Drought Index and the mean daily temperature have the best performance; in the winter season (December–April), the M68dwd is the best performing index. It is shown that the index performance on fire days where larger fires appeared is better and that the uncertainties related to the location of the meteorological station can influence the overall results. A proposal for the selection of the best performing fire weather indices for each Austrian ecoregion is made.     

Ranking of CMIP5-based global climate models for India using compromise programming

In this study, regional patterns of precipitation in Marmara are described for the first time by means of Ward’s hierarchical cluster analysis. Daily values of winter precipitation data based on 19 meteorological stations were used for the period from 1960 to 2012. Five clusters of coherent zones were determined, namely Black Sea-Marmara, Black Sea, Marmara, Thrace, and Aegean sub-regions. To investigate the prevailing atmospheric circulation types (CTs) that cause precipitation occurrence and intensity in these five different rainfall sub-basins, objective Lamb weather type (LWT) methodology was applied to National Centers of Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) reanalysis of daily mean sea level pressure (MSLP) data. Precipitation occurrence suggested that wet CTs (i.e. N, NE, NW, and C) offer a high chance of precipitation in all sub-regions. For the eastern (western) part of the region, the high probability of rainfall occurrence is shown under the influence of E (SE, S, SW) atmospheric CTs. In terms of precipitation intensity, N and C CTs had the highest positive gradients in all the sub-basins of the Marmara. In addition, although Marmara and Black Sea sub-regions have the highest daily rainfall potential during NE types, high daily rainfall totals are recorded in all sub-regions except the Black Sea during NW types.     

Cloud Classification and Distribution of Cloud Types in Beijing Using Ka-Band Radar Data

A cloud clustering and classification algorithm is developed for a ground-based Ka-band radar system in the vertically pointing mode. Cloud profiles are grouped based on the combination of a time–height clustering method and the k-means clustering method. The cloud classification algorithm, developed using a fuzzy logic method, uses nine physical parameters to classify clouds into nine types: cirrostratus, cirrocumulus, altocumulus, altostratus, stratus, stratocumulus, nimbostratus,cumulus or cumulonimbus. The performance of the clustering and classification algorithm is presented by comparison with all-sky images taken from January to June 2014. Overall, 92% of the cloud profiles are clustered successfully and the agreement in classification between the radar system and the all-sky imager is 87%. The distribution of cloud types in Beijing from January 2014 to December 2017 is studied based on the clustering and classification algorithm. The statistics show that cirrostratus clouds have the highest occurrence frequency(24%) among the nine cloud types. High-level clouds have the maximum occurrence frequency and low-level clouds the minimum occurrence frequency.     

湍流高阶矩模式中的参数研究

该文为高阶矩湍流方程组的二阶矩闭合方案提供了参数组选择。此参数组是用近地层实测资料结合湍流方程二阶矩闭合方案推算出的。然后，将其与Mellor等人在实验室所得的参数组分别代入湍流二阶矩闭合方程组拟合，得动量(ΦM(ξ))、热量(ΦH(ξ))与稳定度(ξ)的二组关系曲线，可见此二组曲线几乎重合，且与Businger野外近地层实测结果符合较好。拟合的垂直速度方差((W*2)1/2(ξ))、温度方差((θ*2)1/2(ξ))与ξ的二组关系曲线相对于UTAH资料，所得曲线偏差比Mellor曲线偏差小得多，而相对于KANSAS资料，所得曲线偏差稍大于Mellor曲线。     

Analysis of mid-twentieth century rainfall trends and variability over southwestern Uganda

A methodology has been applied to investigate the spatial variability and trends existent in a mid-twentieth century climatic time series (for the period 1943–1977) recorded by 58 climatic stations in the Albert–Victoria water management area in Uganda. Data were subjected to quality checks before further processing. In the present work, temporal trends were analyzed using Mann–Kendall and linear regression methods. Heterogeneity of monthly rainfall was investigated using the precipitation concentration index (PCI). Results revealed that 53 % of stations have positive trends where 25 % are statistically significant and 45 % of stations have negative trends with 23 % being statistically significant. Very strong trends at 99 % significance level were revealed at 12 stations. Positive trends in January, February, and November at 40 stations were observed. The highest rainfall was recorded in April, while January, June, and July had the lowest rainfall. Spatial analysis results showed that stations close to Lake Victoria recorded high amounts of rainfall. Average annual coefficient of variability was 19 %, signifying low variability. Rainfall distribution is bimodal with maximums experienced in March–April–May and September–October–November seasons of the year. Analysis also revealed that PCI values showed a moderate to seasonal rainfall distribution. Spectral analysis of the time components reveals the existence of a major period around 3, 6, and 10 years. The 6- and 10-year period is a characteristic of September–October–November, March–April–May, and annual time series.