Experimental technique of calibration of symmetrical air pollution models

Based on the inherent property of symmetry of air pollution models, a Symmetrical Air Pollution Model Index (SAPMI) has been developed to calibrate the accuracy of predictions made by such models, where the initial quantity of release at the source is not known. For exact prediction the value of SAPMI should be equal to 1. If the predicted values are overestimating then SAPMI is <1 and if it is underestimating then SAPMI is >1. Specific design for the layout of receptors has been suggested as a requirement for the calibration experiments. SAPMI is applicable for all variations of symmetrical air pollution dispersion models.     

On Modelling Discrete Geological Structures as Markov Random Fields

The purpose of this paper is to extend the locally based prediction methodology of BayMar to a global one by modelling discrete spatial structures as Markov random fields. BayMar uses one-dimensional Markov-properties for estimating spatial correlation and Bayesian updating for locally integrating prior and additional information. The methodology of this paper introduces a new estimator of the field parameters based on the maximum likelihood technique for one-dimensional Markov chains. This makes the estimator straightforward to calculate also when there is a large amount of missing observations, which often is the case in geological applications. We make simulations (both unconditional and conditional on the observed data) and maximum a posteriori predictions (restorations) of the non-observed data using Markov chain Monte Carlo methods, in the restoration case by employing simulated annealing. The described method gives satisfactory predictions, while more work is needed in order to simulate, since it appears to have a tendency to overestimate strong spatial dependence. It provides an important development compared to the BayMar-methodology by facilitating global predictions and improved use of sparse data.     

Event and model dependent rainfall adjustments to improve discharge predictions

Most conceptual rainfall–runoff models use as input spatially averaged rainfall fields which are typically associated with significant errors that affect the model outcome. In this study, it was hypothesised that a simple spatially and temporally averaged event-dependent rainfall multiplier can account for errors in the rainfall input. The potentials and limitations of this lumped multiplier approach were explored by evaluating the effects of multipliers on the accuracy and precision of the predictive distributions. Parameter sets found to be behavioural across a range of different flood events were assumed to be a good representation of the catchment dynamics and were used to identify rainfall multipliers for each of the individual events. An effect of the parameter sets on identified multipliers was found; however, it was small compared to the differences between events. Accounting for event-dependent multipliers improved the reliability of the predictions. At the cost of a small decrease in precision, the distribution of identified multipliers for past events can be used to account for possible rainfall errors when predicting future events. By using behavioural parameter sets to identify rainfall multipliers, the method offers a simple and computationally efficient way to address rainfall errors in hydrological modelling.     

EXPERIMENTS OF SHORT-TERM CLIMATE PREDICTION BY THE OSU/NCC GCM FOR SUMMER SEASON IN CHINA

By using of an ensemble method,the tests of rainfall for the predictions of the seasonal,interseasonal and annual scales in China during 1982—1995 have been made by the atmosphericGCM/mixed layer ocean and ice model(OSU/NCC).Contrasts between forecasts by the OSU/NCC and the observations show that the model has a certain ability in the prediction ofprecipitation for summer over China in all of the three different time scales.And it indicates thatthe interseasonal prediction is the best among the forecasts of three scales.It is also indicated thatthe prediction is especially acceptable in certain areas.     

斜坡变形综合评价系统研究

应用系统科学的基本思想,结合研究斜坡变形的各种方法,建立了针对斜坡变形和破坏的工程地质评价系统。该系统由基于人工神经网络法的稳定性计算子系统、可靠性评价子系统和以灰色系统理论为基础的预测子系统3部分组成,可完成对斜坡的稳定性计算、可靠性评价和变形及破坏的预测等功能,通过实例计算表明,该系统具有较好的适用性,可为斜坡灾害的防治提供重要的依据。      

蔚县矿区断层构造定量研究

采用数理统计方法,分析了断层要素与断层性质的相关关系,建立了不同性质断层的落差与走向长度之间的经验公式,揭示了断层发育的平面展布规律。本文强调小型缓倾斜逆冲断层在蔚县矿区构造格局中的重要性。      

云南省勐野井式钾盐矿找矿模型及预测

勐野井钾盐矿产于新生代环扬子地块西缘兰坪-思茅双向弧后-前陆盆地勐野井组棕红色、杂色盐溶泥砾岩建造中。成矿具有明显的蒸发沉积韵律,钾盐矿层的产出受地层、岩相、构造等因素的控制。将其厘定为新生代（湖相）沉积型钾盐矿的典型矿床-勐野井式钾盐矿。并将成矿条件与之类似的兰坪-云龙、镇源-景谷、江城-勐腊3个地区作为预测工作区,...     

Sensitivity of Kelani streamflow in Sri Lanka to ENSO

As part of an effort to demonstrate the use of climate predictions for water resources management, the El Niño/Southern Oscillation (ENSO) influences on stream flow in the Kelani River in Sri Lanka were investigated using correlation analysis, composite analysis and contingency tables. El Niño (warm phase of ENSO) was associated with decreased annual stream flow and La Niña (cold phase of ENSO) with increased annual flows. The annual stream flow had a negative correlation with the simultaneous ENSO index of NINO3·4 that was significant at the 95% level. This negative correlation is enhanced to a 99% level if the aggregate January to September or the April to September stream flow alone were considered. Although, there is little correlation between ENSO indices and stream flow during the October to December period, there is a high correlation between rainfall and NINO3·4 (r = 0·51, significant at the 99% level). Therefore ENSO based rainfall predictions can be used along with a hydrological model to predict the October to December stream flow. This study demonstrates the viability of using ENSO based predictors for January to September or April to September stream flow predictions in the Kelani River. The October to December stream flow may be predicted by exploiting the strong relationship between ENSO and rainfall during that period. Copyright © 2003 John Wiley & Sons, Ltd.     

沈阳市建筑物震害预测

通过对沈阳范围内的89290栋建筑物基础数据的计算分析,综合评价建筑物状况,并预测沈阳市内的建筑物在地震来临时,可能遭遇的震损情况,并由此为城市防震减灾提供参考依据。     

气候变化情景下北江飞来峡水库极端入库洪水预估

以北江飞来峡水库上游为研究对象,构建了网格分辨率为0.25°×0.25°的VIC(Variable Infiltration Capacity)水文模型,应用CMIP5多模式输出的降尺度结果与VIC模型耦合,对RCP2.6、RCP4.5和RCP8.5情景下未来时期(2020-2050年)飞来峡水库的入库洪水进行预估,并根据IPCC第5次评估报告处理和表达不确定性的方法来描述预估结论的可信度。结果表明,2020-2050年飞来峡水库年最大洪峰流量和年最大7日、15日洪量在RCP2.6情景下"大约可能"呈增加趋势,在RCP4.5和RCP8.5情景下"较为可能"呈增加趋势,水库防洪安全风险增大。与历史时期(1970-2000年)相比,未来水库极端入库洪水增加的可能性从大到小依次为RCP4.5、RCP2.6和RCP8.5情景,其中设计洪水100年、50年和20年一遇的洪峰流量在3种排放情景下均呈上升趋势,100年、50年和20年一遇的最大7日、15日洪量在RCP4.5情景下以上升为主,而在RCP2.6和RCP8.5情景下则主要呈减少态势。