Applications of Explicitly Incorporated/Post‐Processing Measurement Uncertainty in Watershed Modeling

In the field of watershed modeling, the impact of measurement uncertainty (MU) on calibration results indicates the potential issue of inaccurate model predictions. It is important to note that MU refers to the uncertainty in measured data such as flow and nutrient values that are used to evaluate model outputs. The calculation of error statistics assuming measured data are deterministic may not be appropriate as has been frequently stated in literature. Although MU can affect model calibration results, it is rarely incorporated in modeling practice. MU can be incorporated in two schemes: explicitly incorporated (MU‐EI) during model calibration and post‐processed (MU‐PP) after calibration is completed. In this study, both schemes are implemented in a case study of the Arroyo Colorado Watershed, Texas. Unexpectedly, no substantial differences were observed between each scheme for flow predictions. Although MU did not cause dramatic differences in most sediment and NH₄‐N predictions, error statistics were affected in cases with MU greater than 50%, especially for sediment and NH₄‐N. Therefore, it is concluded that MU may not exert a significant impact on model predictions until certain threshold is reached. This study demonstrates that high levels of uncertainty in measured calibration/validation data significantly affect parameter estimation, especially in the auto‐calibration process.     

Evaluation of various global solar radiation models for Nigeria

This study assessed the performance of six solar radiation models. The objective was to determine the most accurate model for estimating global solar radiation on a horizontal surface in Nigeria. Twenty-two years meteorological data sets collected from the Nigerian Meteorological agency and the National Aeronautics and Space Administration for the three regions, covering the entire climatic zones in Nigeria were utilized for calibrating and validating the selected models for Nigeria. The accuracy and applicability of various models were determined for three locations (Abuja, Benin City, and Sokoto), which spread across Nigeria using seven viable statistical indices. This study found that the estimation results of considered models are statistically significant at the 95% confidence level, but their accuracy varies from one location to another. However, the multivariable regression relationship deduced in terms of sunshine ratio, air temperature ratio, maximum air temperature, and cloudiness performs better than other relationships. The multivariable relationship has the least root mean square error and mean absolute bias error, not exceeding 1.0854 and 0.8160 MJ m^?2 day^?1, respectively, and monthly relative percentage error in the range of ± 12% for the study areas.     

多管发酵法与其改进法检测水中粪大肠菌群的比较

粪大肠菌群是水体中粪便污染的重要指示菌,对于评价水质污染程度具有重要的卫生学意义。多管发酵法是传统的粪大肠菌群检测方法,但其步骤繁杂、周期长。目前,已有部分监测及科研人员对其进行了改进,笔者采用改进法与国标多管发酵法进行比较验证,认为Ec培养法可完全替代国标多管发酵法,纸片法亦可用于除酸性水样及菌量大的原污水外的其他环境水样中粪大肠菌群的检测。     

建筑废弃物的回收原理与方法研究

由于社会和经济因素的要求,当今的建筑生命周期要最大程度地完成圆形的循环过程,即重新使用拆建物料制造新产品。循环经济的大背景下,承建商通过坚实的理论基础,采用分层次的方法来进行建筑废弃物的管理。文中总结了两种方法的优点和缺点,进行了混合回收与源头分离两种方法的比较研究。最值得注意的是两个不同的意向方案:绿色星球计划和ICC-ESSAVETM方案。突出的可持续认证和设计方案及绿色星球计划侧重的能源效率模型时常被繁重调试系统的LEED所青睐。一旦被国际编码理事会所批准,这些文件可以在LEED的评分系统下或绿色星球计划中,帮助其寻找资格评分。文章最后分析了建筑废弃物的回收流程,并得出结论:高达95%的建筑和拆迁建筑废弃物都可以回收;回收相比垃圾填埋更节省资金;源头分离较混合回收需要更多的管理和劳动力,但能获得更大的经济利益;承建商应该组织和管理现场的回收工作。     

仪器线路中噪声来源与控制方法

本文主要阐述了电子仪器线路的有源器件、无源器件的噪声和低噪声电路设计过程中元器件的选取原则,以及无噪声偏置放大器放大级方面的问题做了初步分析,并探讨出解决和控制噪声的方法。     

Review on Quantitative Research Methods of Regional Sustainable Development in China

Since the 1990s, with the in-depth study and understanding of sustainable development, quantitative study of regional sustainable development (RSD) was increasingly hot in China. Based on quantitative study of RSD from Core Periodical in China National Knowledge Infrastructure (1992–2008), statistical analysis on the annual distribution, periodicals distribution and changes of the distribution were made. And from perspectives of social–economic, ecological, systematic and emerging methods, the course, probl...     

Reliability and availability modeling of Subsea Xmas tree system using Dynamic Bayesian network with different maintenance methods

Subsea Xmas tree is a vital equipment for offshore oil and gas development. Aiming at the fault mode of subsea Christmas tree system under production conditions, the fault tree of subsea tree system was established, which was transformed into Dynamic Bayesian network, and the reliability and availability of subsea tree system with different repair states are quantitatively analyzed. In this paper, the DBNs are partially verified by the method based on three axes. The results show that the reliability of subsea vertical tree system is slightly higher than that of subsea horizontal tree system. After repair and maintenance, the performance of subsea tree system has been significantly improved, and the improvement of the system performance by preventive maintenance is more obvious. Compared with the perfect repair, the performance of the system with imperfect repair is not significantly reduced. Compared with perfect repair & preventive maintenance, the performance of the system with imperfect repair & preventive maintenance is slightly reduced. In addition, the influence of failure rates and degradation probability on reliability and availability is analyzed. By comparing the influence of failure rates on the system performance of non-maintenance and maintenance, it is found that the change of failure rates has the greatest influence on the reliability and the least influence on the availability of perfect repair & preventive maintenance. By comparing the performance of each component in the subsea tree system, it is found that the failure rates has the most obvious influence on the chock module, and gate valve and tree cap have the most significant influence on the reliability of the system. In order to improve the reliability of subsea tree system, it is necessary to improve the reliability of chock module, gate valve and tree cap.     

A Streamflow Forecasting Framework using Multiple Climate and Hydrological Models1

Abstract: Water resources planning and management efficacy is subject to capturing inherent uncertainties stemming from climatic and hydrological inputs and models. Streamflow forecasts, critical in reservoir operation and water allocation decision making, fundamentally contain uncertainties arising from assumed initial conditions, model structure, and modeled processes. Accounting for these propagating uncertainties remains a formidable challenge. Recent enhancements in climate forecasting skill and hydrological modeling serve as an impetus for further pursuing models and model combinations capable of delivering improved streamflow forecasts. However, little consideration has been given to methodologies that include coupling both multiple climate and multiple hydrological models, increasing the pool of streamflow forecast ensemble members and accounting for cumulative sources of uncertainty. The framework presented here proposes integration and offline coupling of global climate models (GCMs), multiple regional climate models, and numerous water balance models to improve streamflow forecasting through generation of ensemble forecasts. For demonstration purposes, the framework is imposed on the Jaguaribe basin in northeastern Brazil for a hindcast of 1974‐1996 monthly streamflow. The ECHAM 4.5 and the NCEP/MRF9 GCMs and regional models, including dynamical and statistical models, are integrated with the ABCD and Soil Moisture Accounting Procedure water balance models. Precipitation hindcasts from the GCMs are downscaled via the regional models and fed into the water balance models, producing streamflow hindcasts. Multi‐model ensemble combination techniques include pooling, linear regression weighting, and a kernel density estimator to evaluate streamflow hindcasts; the latter technique exhibits superior skill compared with any single coupled model ensemble hindcast.     

Exploration of relationships between phytoplankton biomass and related environmental variables using multivariate statistic analysis in a eutrophic shallow lake： A S-year study

Understanding the process of the changing phytoplankton patterns can be particularly useful in water quality improvement and management decisions. However, it is generally not easy to illustrate the interactions between phytoplankton biomass and related environmental variables given their high spatial and temporal heterogeneity. To elucidate relationships between them, in a eutrophic shallow lake, Taihu Lake, relative long-term data set of biotic and abiotic parameters of water quality in the lake were conducted using multivariate statistical analysis within seasonal periodicity. The results indicate that water temperature and total phosphorus （TP） played governing roles in phytoplankton dynamics in most seasons （i.e. temperature in winter, spring and summer; TP in spring, summer and autumn）; COD （chemical oxygen demand） and BOD （biological oxygen demand） presented significant positive relationships with phytoplankton biomass in spring, summer and autumn. However, a complex interplay was found between phytoplankton biomass and nitrogen considering significant positive relationships occurring between them in spring and autumn, and conversely negative ones in summer. As the predatory factor, zooplankton presented significant grazing-pressure on phytoplankton biomass during summer in view of negative relationship between them in the season. Significant feedback effects of phytoplankton development were identified in summer and autumn in view that significant relationships were obser,qed between phytoplankton biomass and pH, Trans （transparency of water） and DO. The results indicate that interactions between phyto：plankton biomass and related environmental variables are highly sensitive to seasonal periodicity, which improves understanding of different roles of biotic and abiotic variables upon phytoplankton variability, and hence, advances management methods for eutrophic lakes.     

高氯酸盐分析方法研究进展

高氯酸盐是一种甲状腺毒素,它会干扰碘的吸收,阻碍甲状腺的产生,影响胎儿、儿童脑部的发育,甚至造成脑部的损伤。随着检测技术的发展,其在世界范围内的水体中陆续检出,牛奶、瓶装饮料、桶装水、土壤、植物中检出高氯酸盐也随后出现。文章参考大量文献,对高氯酸盐监测分析方法进行了详细论述。