河南及邻区中等地震前地震活动性异常

采用“八五”地震预报攻关研究成果－－地震算法复杂性Ｃ（ｎ）值及地震强度因子Ｍｆ值时间扫描，对河南及邻区１２次中等以上地震进行单项和综合分析研究。结果表明，对应率达８０％，显示出这些方法具有一定的预报意义。     

乌鲁木齐地区地震活动的模糊信息检索

本文应用模糊信息检索方法,选取频度变化率、平均震级、震级变化率、上一时段最大震级四项指标,对乌鲁木齐地区1972年以来地震活动进行了初步分析与研究,并探讨了预报指标对应地震的灵敏度和预报原则。     

地震频次的Hurst指数在地震预报中的应用

引用Hurst指数，对华北地区18次中强地震前地震活动频次的Hurst指数进行了分析和研究，总结出了中强地震前地震活动频次的Hurst指数H值的异常主化特征，同时，把该方法应用到了河南及邻区中等地震预报研究中，制定了预报规则，进行了预测内符检验和预报评分，结果表明，该方法是一种有效的中短期地震预报方法，具有推广应用价值。     

辽宁盘一井氢气浓度异常特征及预报效能分析

为了对2020年辽宁盘锦台盘一井氢气出现的异常及预报效能进行分析,在排除外界干扰的基础上,结合地震活动性特征及应用临界慢化方法对氢气进行异常识别。结果表明：盘一井氢气浓度出现的高值变化主要与辽南地区2019年10月以来的构造活动增强以及盖州震群地震活动增强有一定成因关联,此次异常对应了2020年唐山古冶5.1级地震。应用R值评分方法对氢气预报效能进行检验,计算得到盘一井氢气R值为0.48,通过效能检验,表明盘一井氢气对于辽宁地区地震预测具有较好指示意义。     

首都圈地震监测预报的某些模糊数学方法

本文把模糊数学中的模糊模式识别的直接方法、模糊聚类分析方法、模糊信息检索方法和新近提出的模糊分维方法应用到以地震活动性和地震前兆为基础的首都圈地震监测预报当中,分析研究了发生在这一地区的两次大地震,即1976年唐山7.8级和1989年大同6.1级地震前用模糊数学方法识别出的地震学前兆和非地震学前兆,并对这一地区大地震的长期和中短期模糊预报方法与途径进行了简要的讨论。     

固体潮高时段地震活动特征与小震调制比Rm异常的判定

本文用固体潮高时段地震（调制小震）活动性图象演化过程,综合判定小震调制比Rm的异常值,提高Rm异常在中短预报地震的效能,并对小震调制比地震预报方法资料区域选择问题进行了一些探讨,提出区域过小或不适当的区域选择都可能产生不合理的Rm异常。在对辽宁及邻近地区5级左右地震短临预报指标的研究中,显示出用因体潮高时段地震形成的强震前空区活动图象比用全部地震形成空区图象特征更加明显的特征。     

模糊信息检索法在以震报震研究中的应用

模糊信息检索方法是通过统计数据来确定从属函数,然后根据从属函数矩阵对样本进行分类,从而判断出地震活动的不同类型,并对未来的地震活动性作出预报。 文中以我国南北地震带银川——松潘段的中强震活动为例,对这种方法进行了初步检验,发现效果较好。     

营口—海城地区地震活动性浅析

通过研究１９８１年以来,辽宁地区特别是营海地区的地震活动,从中发现了地震的活动特征;（１）辽宁地区地震活动的发震地区是营口－海城地区,并制约了周围地区的地震活动;（２）营口地区自１９８１年以来。形成了自盖州归州经熊岳西海域,盖州西海域,营口市至大石桥高坎,旗口地区的４级地震活动条带,４级地震有从西南向东北迁移的规律,利用营口－海城地区地震活动规律,曾对１９９５年４月１５日高坎５．０级做了短期预报。     

营口-海域地区地震活动性浅析

通过研究１９８１年以来,辽宁地区特别是营海地区的地震活动,从中发现了地震的活动特征,（１）辽宁地区地震活动的主要发震地区是营口－海城地区,并制约了周围地区的地震活动;（２）营口地区自１９８１年以来,形成了自盖州归州经熊岳西海域、盖州西海域、营口市至大石桥高坎、旗口地区的４级地震活动条带、４级地震有从西南向东北迁移的规律．利用营口－海城地区地震活动规律,曾对１９９５年４月１５日高坎５．０级做了短期预报．     

青海东部地区中强震前活动因子A的图像研究

利用地震活动因子A值，对青海省东部地区(监测能力强)小震活动进行空间扫描，分析了该地区中强以上地震前，地震活动因子A值的空间分布、演化特征及其与目标地震的对应关系，并对该方法的预报效能作出评价。     

A theoretical drought classification method for the multivariate drought index based on distribution properties of standardized drought indices

Drought indices have been commonly used to characterize different properties of drought and the need to combine multiple drought indices for accurate drought monitoring has been well recognized. Based on linear combinations of multiple drought indices, a variety of multivariate drought indices have recently been developed for comprehensive drought monitoring to integrate drought information from various sources. For operational drought management, it is generally required to determine thresholds of drought severity for drought classification to trigger a mitigation response during a drought event to aid stakeholders and policy makers in decision making. Though the classification of drought categories based on the univariate drought indices has been well studied, drought classification method for the multivariate drought index has been less explored mainly due to the lack of information about its distribution property. In this study, a theoretical drought classification method is proposed for the multivariate drought index, based on a linear combination of multiple indices. Based on the distribution property of the standardized drought index, a theoretical distribution of the linear combined index (LDI) is derived, which can be used for classifying drought with the percentile approach. Application of the proposed method for drought classification of LDI, based on standardized precipitation index (SPI), standardized soil moisture index (SSI), and standardized runoff index (SRI) is illustrated with climate division data from California, United States. Results from comparison with the empirical methods show a satisfactory performance of the proposed method for drought classification.     

Forecasting annual precipitation to improve the operation of dams in the Comahue region,Argentina

ABSTRACT

This paper attempts to design statistical models to forecast annual precipitation in the Neuquen and Limay river basins in the Comahue region of Argentina. These forecasts are especially useful as they are used to better organize the operation of hydro-electric dams, the agriculture in irrigated valleys and the safety of the population. In this work, multiple linear regression statistical models are built to forecast mean annual rainfall over the two river basins. Since the maximum precipitation occurs in the winter (June–August), forecasting models have been developed for the beginning of March and for the beginning of June, just before the rainy season starts. The results show that the sea-surface temperatures of the Indian and Pacific oceans are good predictors for March models and explain 42.8% of the precipitation index variance. The efficiency of the models increases in June, adding more predictors related to the autumn circulation.     

Skill of 6HR and 12HR thunderstorm forecasts based on assessment of larger-scale information

Summary The relationship between information, contained in aerological data from the European area, and a thunderstorm occurrence in the area of the Czech Republic was investigated with input data from the period of May–September 1989–1991. SYNOP reports from Czech ground stations were utilized to assess event occurrence. TEMP 00UTC and TEMP 12UTC reports from European stations were used to determine potential diagnostic predictors, and the TEMP00 data served as the input data set for the 12hr mesoscale model forecast to gain prognostic predictors. Each of the two diagnostic data sets from 00UTC and 12UTC and of the prognostic data set comprised about 400 predictors/predictand elements. The categorical forecast of thunderstorm occurrence, based on the application of linear regression and a simple version of pattern recognition, is discussed. The critical success index was determined for every type of forecast and used to assess forecast skill.     

Environmental determinants of leech assemblage patterns in lotic and lenitic habitats

Leeches (Clitellata: Hirudinida) are abundant predators or ecto-parasites inhabiting various freshwater habitats; however many biotic and abiotic drivers of their assemblage patterns have been deduced rather than directly tested. To study species richness and composition changes in leech assemblages, 109 sites of running and stagnant water bodies were sampled in three regions of the Czech Republic in 2007–2010, together with several explanatory variables that are known or expected to be important predictors of leech distribution. In total, 17 species of leeches were recorded, varying between 0–7 and 0–9 species in lotic and lenitic sites, respectively. These differences in species richness of lotic and lenitic sites were highly significant, contrary to the abundances, which varied between 0–283 and 0–295 individuals. The main change in species composition was controlled by water temperature and morphological characteristics (e.g. substrate and cover of macrophytes), mostly reflecting the differences between lotic and lenitic habitats. We found the density of benthos (i.e. prey availability) to be the best predictor of species composition in both lotic and lenitic sites, together with the percentage of canopy cover. However, the other significant predictors (i.e. the substrate and water conductivity found to be significant in lotic sites, and the mean annual temperature and PO₄^3? in lenitic sites), differed between these habitats. Other than mean annual temperature and water temperature, which had different effects on species richness in lotic and lenitic sites, there were no other differences between lotic and lenitic sites in terms of how species richness and abundance responded to all other analyzed predictors. Our results stress the importance of prey availability and canopy for leech distribution patterns. Differences in the significant predictors of leech assemblage patterns between lotic and lenitic sites raise fundamental questions about the underlying mechanisms and ecological constraints to leech distribution in these main types of aquatic systems.     

2014年2月12日新疆于田M_S7.3级地震的回顾性预测讨论

静中动判据、异年倍九律、三性法、高山峰指标是郭增建研究组提出的中期与短期的预测指标。用这几个指标回顾性地讨论了2014年2月12日新疆于田南MS7.3级地震的中期和短临预测。     

基于AHP的新疆南天山西段中强地震预测指标体系及效能评估

本文从新疆南天山西段地区映震效果较好的预测指标出发, 采用AHP(Analytic Hierarchy Process, 层次分析法)建立了新疆南天山西段地区预测指标体系。 在此基础上, 应用1～9比率标度法构建相应的判断矩阵, 进而计算各指标的权重。 结果显示, 几项映震效果较好的指标中, 权重值相对较大的是短期预测指标中的形变异常和部分测震学参数。 同时AHP模型也给出了新疆南天山西段有无中强地震的综合判断指标Y, Y值可较好地反映新疆南天山西段地区中强地震的震兆强弱程度和异常可靠性, 当Y≥2.191时, 新疆南天山西段地区短期内存在发生中强地震的危险。     

Comparative study of statistical methods to identify a predictor for discharge at Orsova in the Lower Danube Basin

ABSTRACT

The aims of this study are to investigate the influence of large-scale atmospheric circulation quantified by indices such as the North Atlantic Oscillation index (NAOI), the Greenland-Balkan Oscillation index (GBOI) and blocking-type indices on the Lower Danube discharge. We separately analysed each season for the 1948–2000 period. In addition to the statistical linear procedure, we applied methods to quantify nonlinear connections between variables, as mutual information between predictors and predictand, using Shannon’s information entropy theory. The nonlinear correlation information between climate indices and discharge is higher than that obtained from the linear measure, providing more insight into real connections. Also, the non-stationarity of the link between variables is highlighted by spectral coherence based on wavelet analysis. For the physical interpretation, we analyse composite maps over the Atlantic-European region. The most significant influence on the discharge of the Lower Danube Basin is given by the GBOI and blocking-type atmospheric circulation over Europe.     

极端天气下三峡库区土地利用对河流水质的多时空尺度影响

为探究极端天气下流域内水质对土地利用的响应关系,本研究基于不同空间尺度(1000 m河段缓冲区、500 m河岸带缓冲区及子流域)的土地利用指数以及旱季(2019年11月)、雨季-洪水期(2020年7月)和雨季-干旱期(2022年8月)的水质数据,探究流域内土地利用对水质的多时空尺度影响,从而得到保护流域水质和规划流域内土地利用格局的最佳时空尺度和对水质影响最显著的预测因子。研究表明:(1)流域水质受极端天气影响,降雨会增强水体的稀释能力,高温会加快水中微生物反应速率,具体表现为雨季-洪水期的水质较好,雨季-干旱期次之,旱季较差。(2)土地利用对水质指标的影响存在时空尺度效应,土地利用在子流域和旱季尺度下对河流水质影响最显著。(3)不同土地利用指数对流域水质影响存在差异,耕地、林地、斑块密度、最大斑块指数和边缘密度是影响水质指标最显著的解释变量。其中林地与多数水质指标具有负相关关系,建设用地、耕地、斑块密度与较多水质指标存在正相关关系。本研究结果为合理规划土地利用格局以及保护河流水质提供科学依据,对三峡库区环境可持续发展及生态保护具有一定意义。     

Evaluation of groundwater quality using pollution index of groundwater(PIG) and non-carcinogenic health risk assessment in part of the Gangetic Basin

The aim of this study was to evaluate groundwater fitness for domestic and irrigational use in Unnao district of Uttar Pradesh,India.For this study,block wise(n=16) groundwater samples were collected,and measured parameters were analyzed using the pollution index of groundwater(PIG),various irrigational indices,and graphical techniques.The results of this study suggested that most of the parameters were within the prescribed limits of WHO and BIS,excluding F~-(0.4 to 2.6 mg L~(-1))and Fe~(2+)(0.1 to 1.7 mg L~(-1)).Concentrations of total dissolved solids(TDS) were exceeded the desirable limit( 500 mg L~(-1)) in 43.75% of samples at some sites.The Gibbs plot revealed that groundwater chemistry was governed by rock-water interaction in the region,especially silicate weathering.The Piper plot suggested that Ca~(2+)-HCO_3~-is dominant hydrochemical facies in the area followed by mixed Ca~(2+)-Na~+-HCO_3~-type,Na~+-Cl~-type,and Na~+-HCO_3~-type.PIG evaluation revealed that the contribution of F~-and Fe~(2+) in groundwater degradation is high in comparison to other elements in the region,about 18.75% samples showed low pollution,while about 6.25% samples shows moderate pollution,and 6.25%samples reflected high pollution.The human health risk(HHR) assessment finding suggested that children(mean:1.36) are more vulnerable than adults(mean:1.01).Sodium absorption ration(SAR),Residual sodium carbonate(RSC),and Permeability index(PI) indicated that most of the groundwater was suitable for irrigation,whereas,Magnesium hazard ration(MHR) and Potential salinity(PS) indices suggested that only 37.5% and 56.25%of the samples were suitable for irrigational use,respectively.This regional study would help in decision making for stakeholders and relevant authorities in the execution of groundwater management and remediation plans in the area.     

Seasonal drought predictability in Portugal using statistical–dynamical techniques

Atmospheric forecasting and predictability are important to promote adaption and mitigation measures in order to minimize drought impacts. This study estimates hybrid (statistical–dynamical) long-range forecasts of the regional drought index SPI (3-months) over homogeneous regions from mainland Portugal, based on forecasts from the UKMO operational forecasting system, with lead-times up to 6 months. ERA-Interim reanalysis data is used for the purpose of building a set of SPI predictors integrating recent past information prior to the forecast launching. Then, the advantage of combining predictors with both dynamical and statistical background in the prediction of drought conditions at different lags is evaluated. A two-step hybridization procedure is performed, in which both forecasted and observed 500 hPa geopotential height fields are subjected to a PCA in order to use forecasted PCs and persistent PCs as predictors. A second hybridization step consists on a statistical/hybrid downscaling to the regional SPI, based on regression techniques, after the pre-selection of the statistically significant predictors. The SPI forecasts and the added value of combining dynamical and statistical methods are evaluated in cross-validation mode, using the R² and binary event scores. Results are obtained for the four seasons and it was found that winter is the most predictable season, and that most of the predictive power is on the large-scale fields from past observations. The hybridization improves the downscaling based on the forecasted PCs, since they provide complementary information (though modest) beyond that of persistent PCs. These findings provide clues about the predictability of the SPI, particularly in Portugal, and may contribute to the predictability of crops yields and to some guidance on users (such as farmers) decision making process.