利用人工神经网络方法对牙形石鉴定中若干“模糊特征”的数字化研究

牙形石是一类在地层学研究中具有重要意义的古生物化石。但由于牙形石鉴定中存在诸多重要、却难以定量分析的“模糊特征”,使得牙形石的鉴定和分类工作长期依赖于古生物工作者的经验。利用数学中的非线性方程将这些“模糊特征”进行数学描述,并利用人工神经网络中的反向传播算法设计出一套多隐含层的前馈网络分类器,对这些“模糊特征”进行分类尝试,取得了良好的效果,为牙形石的进一步数值化研究奠定了基础。     

A Regional Scale Investigation of Climatological Tropical Convection and Precipitation in the Amazon Basin

This study constructs a regional scale climatology of tropical convection and precipitation from more than 15 years of monthly outgoing longwave radiation (OLR) and precipitation data on 2.5°× 2.5° latitude-longitude grid to examine the spatial and temporal patterns and variability of convection and precipitation in the Amazon Basin. A linear regression analysis also detects if any trends exist in the two datasets. The region of study extends from 15°N to 25°S and 30° to 80°W that encompass the Amazon Basin and surrounding fringe areas for the period from January 1979 through December 1995 for the OLR data and up to 1996 for the precipitation dataset. The basin-average mean monthly and seasonal climatology serve as a ‘baseline’ reference for comparison with the full time series of basin-average monthly OLR and precipitation to illustrate the interannual variability and identify anomalous periods of wet and dry conditions. A linear trend analysis of OLR data found small negative values across the Amazon Basin indicating a slight increase in convective activity over the period of study. The analysis of the precipitation time series, however, shows no coincidental increase in precipitation as would be expected with an increase in convective activity. Portions of Rondônia and Mato Grosso, areas that have undergone extensive deforestation, illustrate no trend in precipitation as suggested by GCM simulation results. The only area featuring any large change in precipitation occurs in a small area in the northwestern region of South America where a large positive trend in precipitation exists.     

准噶尔盆地南缘荒漠区梭梭维持水源初步研究

为了了解沙漠植物梭梭维持水源的利用情况,探明其对水源的利用策略,利用稳定性同位素技术定位取样,对准噶尔盆地南缘荒漠区梭梭进行了研究.结果表明,冬季,梭梭基本没有直接利用降雪;随着融雪后浅层土壤含水率的上升,梭梭明显利用浅层土壤水;梭梭利用水源中,地下水占有很大比例,这种比例在冬季和夏季最高,最高幅度可达到80％,平均占到30％左右;降雨也是梭梭利用的水源之一,在降雨后的3～5 d内,梭梭木质部δ18O值有明显趋近降雨δ18O值的趋势.因此,本研究区的梭梭维持水源有多个途径,地下水、融雪形成的浅层土壤水是其主要水源,中、大量降雨也是其利用的水源之一.     

Soil erosion and its response to the changes of precipitation and vegetation cover on the Loess Plateau

Soil erosion is a major threat to our terrestrial ecosystems and an important global environmental problem. The Loess Plateau in China is one of the regions that suffered more severe soil erosion and undergoing climate warming and drying in the past decades. The vegetation restoration named Grain-to-Green Program has now been operating for more than 10 years. It is necessary to assess the variation of soil erosion and the response of precipita- tion and vegetation restoration to soil erosion on the Loess Plateau. In the study, the Revised Universal Soil Loss Equation （RUSLE） was applied to evaluate annual soil loss caused by water erosion. The results showed as follows. The soil erosion on the Loess Plateau between 2000 and 2010 averaged for 15.2 t hm-2 a 1 and was characterized as light for the value less than 25 t hm-2 a-1. The severe soil erosion higher than 25 t hm-2 a-~ was mainly distributed in the gully and hilly regions in the central, southwestern, and some scattered areas of earth-rocky mountainous areas on the Loess Plateau. The soil erosion on the Loess Plateau showed a deceasing trend in recent decade and reduced more at rates more than 1 t hm 2 a 1 in the areas suffering severe soil loss. Benefited from the improved vegetation cover and ecological construction, the soil erosion on the Loess Plateau was significantly declined, es- pecially in the east of Yulin, most parts of Yah＇an prefectures in Shaanxi Province, and the west of Luliang and Linfen prefectures in Shanxi Province in the hilly and gully regions. The variation of vegetation cover responding to soil erosion in these areas showed the relatively higher contribution than the precipitation. However, most areas in Qingyang and Dingxi pre- fectures in Gansu Province and Guyuan in Ningxia Hui Autonomous Region were predomi- nantly related to precipitation.     

Discovering environmental data: metadatabases,network information resource tools and the GENIE system

Abstract

The existence of global computer networks in conjunction with various computer based tools offers the GIS community the possibility of identifying existing spatial data in a faster and more complete way. Using such tools could help the GIS community reduce the high costs of data collection. This paper focuses on the discovery of environmental data using metadatabases and network information resource tools and includes comments on some of the limitations of these computer based tools. The article will conclude by describing how the system developed by the GENIE project attempts to overcome some of these limitations.     

An approach for the determination of precipitation input for worst-case flood modelling

ABSTRACT

There is a lack of suitable methods for creating precipitation scenarios that can be used to realistically estimate peak discharges with very low probabilities. On the one hand, existing methods are methodically questionable when it comes to physical system boundaries. On the other hand, the spatio-temporal representativeness of precipitation patterns as system input is limited. In response, this paper proposes a method of deriving spatio-temporal precipitation patterns and presents a step towards making methodically correct estimations of infrequent floods by using a worst-case approach. A Monte Carlo approach allows for the generation of a wide range of different spatio-temporal distributions of an extreme precipitation event that can be tested with a rainfall–runoff model that generates a hydrograph for each of these distributions. Out of these numerous hydrographs and their corresponding peak discharges, the physically plausible spatio-temporal distributions that lead to the highest peak discharges are identified and can eventually be used for further investigations.

Editor A. Castellarin; Associate editor E. Volpi     

Mapping of climatic parameters under climate change impacts in Iran

ABSTRACT

This research aims to provide a comprehensive evaluation of climate change effects on temperature, precipitation and potential evapotranspiration over the country of Iran for the time periods 2010–2039, 2040–2069 and 2070–2099, and under scenarios A2 and B2. After preparation of measured temperature and precipitation data and calculation of potential evapotranspiration for the base time period of 1960–1990 for 46 meteorological stations (with a nationwide distribution), initial zoning of these three parameters over the country was attempted. Maximum and minimum temperatures and values of precipitation were obtained from the HadCM3 model under scenarios A2 and B2 for the three time periods, and these data were downscaled. Corresponding maps were prepared for the three parameters in the three time periods, and spatial and temporal variations of these climatic parameters under scenarios A2 and B2 were extracted and interpreted. Results showed that the highest increase in temperature would occur in western parts of the country, but the highest increase of potential evapotranspiration would occur in the central region of Iran. However, precipitation would vary temporally and spatially in different parts of the country depending on the scenario used and the time period selected.

Editor Z. W. Kundzewicz; Associate editor not assigned     

Spatio-temporal evaluation of global gridded precipitation datasets across Iran

ABSTRACT

In this work, the accuracy of four gridded precipitation datasets – Climatic Research Unit (CRU), Global Precipitation Climatology Centre (GPCC), PERSIANN-Climate Data Record (PCDR) and University of Delaware (UDEL) – is evaluated across Iran to find an alternative source of precipitation data. Monthly, seasonal and annual precipitation data from 85 synoptic stations for the period 1984–2013 were used as the basis for the evaluations. Our results indicate that all datasets underestimate and overestimate precipitation in stations with annual precipitation greater than 600 and less than 100 mm, respectively. However, all datasets correctly recognize regimes of precipitation, but with a bias in amount of precipitation. Our spatio-temporal assessments show that GPCC is the most suitable dataset to be used over Iran. Both UDEL and CRU can be considered as the second and third most suitable datasets, while PCDR showed the weakest performance among the studied datasets.     

Intensity–duration–frequency curves exploiting neighbouring extreme precipitation data

ABSTRACT

Records of precipitation extremes are essential for hydrological design. In urban hydrology, intensity–duration–frequency curves are typically estimated from observation records. However, conventional approaches seldom consider the areal extent of events. If they do, duration-dependent area reduction factors are used, but precipitation is measured at only a few locations. Due to the high spatial variability of precipitation, it is relatively unlikely that a gauged observation network will capture the extremes that occur during a precipitation event. Therefore, the area reduction approach cannot be regarded as the reduction of an observed maximum. To investigate precipitation extremes, spatial aspects need to be considered using different approaches. Here, we both address the conventional practice of area reduction and consider a within-area chance of increased precipitation, defined as the maximum precipitation intensity observed in a cluster within a selected domain. The results show that (1) the risk of urban flooding is routinely underestimated in current design practice, and (2) traditional calculations underestimate extremes by as much as 30–50%. We show how they can be revised sensibly.     

Historical trends in Florida temperature and precipitation

Because of its low topographic relief, unique hydrology, and the large interannual variability of precipitation, Florida is especially vulnerable to climate change. In this paper, we investigate a comprehensive collection of climate metrics to study historical trends in both averages and extremes of precipitation and temperature in the state. The data investigated consist of long‐term records (1892–2008) of precipitation and raw (unadjusted) temperature at 32 stations distributed throughout the state. To evaluate trends in climate metrics, we use an iterative pre‐whitening method, which aims to separate positive autocorrelation from trend present in time series. Results show a general decrease in wet season precipitation, most evident for the month of May and possibly tied to a delayed onset of the wet season. In contrast, there seems to be an increase in the number of wet days during the dry season, especially during November through January. We found that the number of dog days (above 26.7 °C) during the year and during the wet season has increased at many locations. For the post‐1950 period, a widespread decrease in the daily temperature range (DTR) is observed mainly because of increased daily minimum temperature (Tmin). Although we did not attempt to formally attribute these trends to natural versus anthropogenic causes, we find that the urban heat island effect is at least partially responsible for the increase in Tmin and its corresponding decrease in DTR at urbanized stations compared with nearby rural stations. In the future, a formal trend attribution study should be conducted for the region. Copyright © 2012 John Wiley & Sons, Ltd.