Singularity and Nonnormality in the Classification of Compositional Data

Geologists may want to classify compositional data and express the classification as a map. Regionalized classification is a tool that can be used for this purpose, but it incorporates discriminant analysis, which requires the computation and inversion of a covariance matrix. Covariance matrices of compositional data always will be singular (noninvertible) because of the unit-sum constraint. Fortunately, discriminant analyses can be calculated using a pseudo-inverse of the singular covariance matrix; this is done automatically by some statistical packages such as SAS. Granulometric data from the Darss Sill region of the Baltic Sea is used to explore how the pseudo-inversion procedure influences discriminant analysis results, comparing the algorithm used by SAS to the more conventional Moore–Penrose algorithm. Logratio transforms have been recommended to overcome problems associated with analysis of compositional data, including singularity. A regionalized classification of the Darss Sill data after logratio transformation is different only slightly from one based on raw granulometric data, suggesting that closure problems do not influence severely regionalized classification of compositional data.     

柴达木盆地东缘晚更新世气候变化的（古）土壤发生记录

柴达木盆地东缘典型剖面（古）土壤及其黄土线质的宏观特征和理化分析表明,本区晚更新世以来气候曾有几次较大的波动,反映了全球冰量影响的气候波动以及东亚季风气候的强弱变化特征。揭示出气候变化过程中温度和降水并不同步,温度主要受地球轨道变化引起的太阳辐射的影响;降水则主要取决于冬、夏季风的浊弱对比关系,夏季风盛行时,降水丰沛。间冰段古土壤Ｓｍ的土壤发生特征和强度,揭示了除低温引起的蒸发减少外,降水丰沛也是     

Indigenous knowledge of landscape–ecological zones among traditional herbalists: a case study in Keiyo District, Kenya

Object of study is the insight of traditional herbalists in the landscape-ecological factors wich control the growth of the plants they use for healing their patients. The extent of this insight determines their capacity to adapt to environmental changes such as deforestation and soil degradation. This paper deals with the landscape-ecological perception of herbalists who live in an area with sharp landscape contrasts and drastic changes in landuse: the Keiyo Escarpment in the Rift which links the cool and humid Uasin Gishu Plateau at a level of more than 2600 m above sea level, with the warm and semi-arid Kerio Valley at 1250 m. The landuse changes of the last 50 years are caused by growing population density, loss of traditional attitudes towards the value of the land, and changes in forest cover. Data on local knowledge are acquired through interviews and field visits. The corresponding western knowledge is derived from aerial photographs and existing reports and maps. Six landscape-ecological zones are recognized. They are named after topography (wareng, mosop, soin), vegetation (teguming, korget) or landuse (tumdo). These zones coincide with the units of the agro-climatic map of Kenya. Apart from geology, all the factors of the hierarchical model used in western-based landscape ecology (climate, geology, relief, water, soil, vegetation and fauna) are included in the indigenous perception of the landscape, but the hierarchical order is not necessarily the same: e.g. the herbalists assume that rock grows in the soil instead of the other way around, and that forests attract rain. From the herbalists' point of view, deforestation and the establishment of small-holder agriculture is less serious for their trade than the replacement of indigenous trees by plantations with exotic species. They adapt to the loss of the forest by travelling to areas with comparable landscape-ecological conditions or, especially in the case of women and older male herbalists, by planting the required species in the garden. According to the herbalists, good climate and fertile soil stimulate species diversity, but best medicinal performance give plants on soils which are periodically dry.     

华北北部黑风暴的气候学特征

使用内蒙古中西部72个地面测站1957～1996年历史资料,分析研究了该地区黑风暴的气候学特征,包括黑风暴的地理分布、年代际变化、年际变化、年变化、旬变化、日变化等时间变化特征和强度变化;讨论了形成上述变化特征的气候成因。得到如下结论：内蒙古中西部是黑风暴的易发区和多发区,最大中心位于内蒙古中部的朱日和;黑风暴的时间变化特征显著;强度有较大差异。     

Derivation of a curve number and kinematic-wave based flood frequency distribution

Abstract

The physically-based flood frequency models use readily available rainfall data and catchment characteristics to derive the flood frequency distribution. In the present study, a new physically-based flood frequency distribution has been developed. This model uses bivariate exponential distribution for rainfall intensity and duration, and the Soil Conservation Service-Curve Number (SCS-CN) method for deriving the probability density function (pdf) of effective rainfall. The effective rainfall-runoff model is based on kinematic-wave theory. The results of application of this derived model to three Indian basins indicate that the model is a useful alternative for estimating flood flow quantiles at ungauged sites.     

Pleistocene climatic change in Southern Australia and its effect on speleothem deposition in some Nullarbor caves

Activity ratios of ²³⁴U/²³⁸U, ²³⁰Th/²³⁴U, and ²³⁰Th/²³²Th have been determined for calcite, gypsum and halite speleothems from caves of the Nullarbor Plain, mostly in the area N and NW of Mundrabilla Station, for the purpose of U-series dating. All calcite speleothems contain adequate amounts of uranium for dating, but some show an excess of ²³⁰Th. Stratigraphic relationships indicate that there were at least three phases of calcium carbonate deposition in the Nullarbor caves. The calcite samples, with one possible exception, have ages in excess of ca. 400000 yrs BP. This suggests that no significant amounts of calcium carbonate deposition have taken place during the last 400ka. At present, active deposition of speleothems is restricted almost entirely to gypsum and halite. The only gypsum speleothem dated was found to have a finite age of ca. 185 ka. Six dates on a small halite speleothem containing insect and arachnid remains indicate that it formed rapidly during Holocene time.     

A comparative study of extensive machine learning models for predicting long-term monthly rainfall with an ensemble of climatic and meteorological predictors

Rainfall prediction is of vital importance in water resources management. Accurate long-term rainfall prediction remains an open and challenging problem. Machine learning techniques, as an increasingly popular approach, provide an attractive alternative to traditional methods. The main objective of this study was to improve the prediction accuracy of machine learning-based methods for monthly rainfall, and to improve the understanding of the role of large-scale climatic variables and local meteorological variables in rainfall prediction. One regression model autoregressive integrated moving average model (ARIMA) and five state-of-the-art machine learning algorithms, including artificial neural networks, support vector machine, random forest (RF), gradient boosting regression, and dual-stage attention-based recurrent neural network, were implemented for monthly rainfall prediction over 25 stations in the East China region. The results showed that the ML models outperformed ARIMA model, and RF relatively outperformed other models. Local meteorological variables, humidity, and sunshine duration, were the most important predictors in improving prediction accuracy. 4-month lagged Western North Pacific Monsoon had higher importance than other large-scale climatic variables. The overall output of rainfall prediction was scalable and could be readily generalized to other regions.     

Temporal trends of hydro‐climatic variables and runoff response to climatic variability and vegetation changes in the Yiluo River basin,China

The Yiluo River is the largest tributary for the middle and lower reaches of the Yellow River below Sanmenxia Dam. Changes of the hydrological processes in the Yiluo River basin, influenced by the climatic variability and human activities, can directly affect ecological integrity in the lower reach of the Yellow River. Understanding the impact of the climatic variability and human activities on the hydrological processes in the Yiluo River basin is especially important to maintain the ecosystem integrity and sustain the society development in the lower reach of the Yellow River basin. In this study, the temporal trends of annual precipitation, air temperature, reference evapotranspiration (ET₀) and runoff during 1961–2000 in the Yiluo River basin were explored by the Mann‐Kendall method (M‐K method), Yamamoto method and linear fitted model. The impacts of the climatic variability and vegetation changes on the annual runoff were discussed by the empirical model and simple water balance model and their contribution to change of annual runoff have been estimated. Results indicated that (i) significant upwards trend for air temperature and significant downwards trend both for precipitation and ET₀ were detected by the M‐K method at 95% confidence level. And the consistent trends were obtained by the linear fitted model; (ii) the abrupt change started from 1987 detected by the M‐K method and Yamamoto method, and so the annual runoff during 1961–2000 was divided into two periods: baseline period (1961–1986) and changeable period (1987–2000); and (iii) the vegetation changes were the main cause for change of annual runoff from baseline period to changeable period, and climatic variability contributed a little to the change of annual runoff of the Yiluo River. Copyright © 2009 John Wiley & Sons, Ltd.     

Correspondence analysis of water quality data: Implications for fauna deaths at Stillwater Lakes, Nevada

Over the last 2 years, more than seven million fish and 16,000 birds have died from drought-induced disease and suspected poisoning at Stillwater Lakes, Nevada. Correspondence analysis of water samples taken in June and September 1986, from lakes and reservoirs near Stillwater Lakes and four background lakes/reservoirs (not associated with fauna disease and death), is attempted to investigate the correlation among sites associated with fauna deaths. This analysis identifies high correlation between some Stillwater Lakes sites and boron. A correlation between boron and arsenic is also identified. Leaching and transport of toxins to the Stillwater Lakes system is one hypothesis to explain the wildlife deaths. Other hypotheses include the direct introduction of toxins by groundwater associated with geothermal activity. A natural cause for the wildlife deaths is also suggested, a cause not associated with the introduction of toxins.     

Long‐term trend of precipitation and runoff in Korean river basins

The spatial and temporal variations of precipitation and runoff for 139 basins in South Korea were investigated for 34 years (1968–2001). The Precipitation‐Runoff Modelling System (PRMS) was selected for the assessment of basin hydrologic response to varying climates and physiology. A non‐parametric Mann–Kendall's test and regression analysis are used to detect trends in annual, seasonal, and monthly precipitation and runoff, while Moran's I is adapted to determine the degree of spatial dependence in runoff trend among the basins. The results indicated that the long‐term trends in annual precipitation and runoff were increased in northern regions and decreased in south‐western regions of the study area during the study period. The non‐parametric Mann–Kendall test showed that spring streamflow was decreasing, while summer streamflow was increasing. April precipitation decreased between 15% and 74% for basins located in south‐western part of the Korean peninsula. June precipitation increased between 18% and 180% for the majority of the basins. Trends in seasonal and monthly streamflow show similar patterns compared to trends in precipitation. Decreases in spring runoff are associated with decreases in spring precipitation which, accompanied by rising temperatures, are responsible for reducing soil moisture. The regional patterns of precipitation and runoff changes show a strong to moderate positive spatial autocorrelation, suggesting that there is a high potential for severe spring drought and summer flooding in some parts of Korea if these trends continue in the future. Copyright © 2008 John Wiley & Sons, Ltd.