一种统计技术结合动力释用的沿海海雾预报方法

提出了一种利用历史相似个例查询技术,结合WRF模式输出释用的Gultepe沿海大雾业务预报方法。该方法可以较好的计算近地面大气中的液态水含量(LWC)和云滴粒子数密度(Nd)参数。利用该方法对2011—2013年的3—6月的历史沿海海雾个例进行试报,结果表明,恰当的结合统计方法和动力模式可以一定程度上克服目前数值模式不成熟的缺陷,从而提高WRF模式在大雾业务预报方面的释用水平。     

提高月预报业务水平的动力相似集合方法

针对基于大气环流模式的月预报问题,提出了一种能有效减小预报误差并提高预报技巧的动力相似集合预报新方法。该方法着眼于动力模式与统计经验的内在结合,在模式积分过程中通过提取大气环流历史相似性信息,对模式误差进行参数化处理,形成多个时变的相似强迫量来扰动生成预报的集合成员。将这一集合新方法应用到中国国家气候中心业务大气环流模式(BCC AGCM1.0),一组10 a准业务环境下回报试验结果显示,相比于业务集合预报,动力相似集合预报方法能有效改进模式对于大气环流的纬向平均、超长波和长波预报,从而有效提高了月平均环流预报技巧(几乎达到业务可用标准)和逐日环流预报技巧,并显著降低了预报误差,合理增加集合离散度,使二者配置关系得以改善,有望在业务预报中应用。     

基于互信息量与BP神经网络的中长期径流预报方法研究

针对中长期径流预报因子的选择问题,采用互信息量方法筛选预报模型输入因子,在BP神经网络模型中,分别用均方误差和互信息量作为目标函数,衡量因子复合相关关系,优化选择最终预报因子并应用于碧流河汛期径流预报中。结果表明,基于互信息量筛选的预报因子与BP神经网络模型相结合,可有效识别多个预报因子与预报量间的复合相关性,对中长期径流预报因子的选择有很好参考价值。     

大型基坑工程水平位移监测与预测结果分析

以银川大世界商务广场的基坑为例,介绍了大型基坑工程水平位移监测的实施方案,给出了水平位移监测方法的精度,并对监测成果进行了分析。在此基础上,分别用多项式拟合和时间序列分析模型两种方法进行建模,对基坑水平位移进行预测,结果表明,该基坑水平位移较小,在规范规定的要求之内,说明基坑是稳定的;时间序列分析模型的预测精度要高于多项式拟合模型的精度。     

Spatial interpolation of snow water equivalency using surface observations and remotely sensed images of snow‐covered area

As demand for water continues to escalate in the western Unites States, so does the need for accurate monitoring of the snowpack in mountainous areas. In this study, we describe a simple methodology for generating gridded‐estimates of snow water equivalency (SWE) using both surface observations of SWE and remotely sensed estimates of snow‐covered area (SCA). Multiple regression was used to quantify the relationship between physiographic variables (elevation, slope, aspect, clear‐sky solar radiation, etc.) and SWE as measured at a number of sites in a mountainous basin in south‐central Idaho (Big Wood River Basin). The elevation of the snowline, obtained from the SCA estimates, was used to constrain the predicted SWE values. The results from the analysis are encouraging and compare well to those found in previous studies, which often utilized more sophisticated spatial interpolation techniques. Cross‐validation results indicate that the spatial interpolation method produces accurate SWE estimates [mean R² = 0·82, mean mean absolute error (MAE) = 4·34 cm, mean root mean squared error (RMSE) = 5·29 cm]. The basin examined in this study is typical of many mid‐elevation mountainous basins throughout the western United States, in terms of the distribution of topographic variables, as well as the number and characteristics of sites at which the necessary ground data are available. Thus, there is high potential for this methodology to be successfully applied to other mountainous basins. Copyright © 2010 John Wiley & Sons, Ltd.     

Ensemble rainfall forecasting with numerical weather prediction and radar‐based nowcasting models

The overall objective of this study is to improve the forecasting accuracy of the precipitation in the Singapore region by means of both rainfall forecasting and nowcasting. Numerical Weather Predication (NWP) and radar‐based rainfall nowcasting are two important sources for quantitative precipitation forecast. In this paper, an attempt to combine rainfall prediction from a high‐resolution mesoscale weather model and a radar‐based rainfall model was performed. Two rainfall forecasting methods were selected and examined: (i) the weather research and forecasting model (WRF); and (ii) a translation model (TM). The WRF model, at a high spatial resolution, was run over the domain of interest using the Global Forecast System data as initializing fields. Some heavy rainfall events were selected from data record and used to test the forecast capability of WRF and TM. Results obtained from TM and WRF were then combined together to form an ensemble rainfall forecasting model, by assigning weights of 0.7 and 0.3 weights to TM and WRF, respectively. This paper presented results from WRF and TM, and the resulting ensemble rainfall forecasting; comparisons with station data were conducted as well. It was shown that results from WRF are very useful as advisory of anticipated heavy rainfall events, whereas those from TM, which used information of rain cells already appearing on the radar screen, were more accurate for rainfall nowcasting as expected. The ensemble rainfall forecasting compares reasonably well with the station observation data. Copyright © 2012 John Wiley & Sons, Ltd.     

An ANN‐based model for spatiotemporal groundwater level forecasting

This paper evaluates the feasibility of using an artificial neural network (ANN) methodology for estimating the groundwater levels in some piezometers placed in an aquifer in north‐western Iran. This aquifer is multilayer and has a high groundwater level in urban areas. Spatiotemporal groundwater level simulation in a multilayer aquifer is regarded as difficult in hydrogeology due to the complexity of the different aquifer materials. In the present research the performance of different neural networks for groundwater level forecasting is examined in order to identify an optimal ANN architecture that can simulate the piezometers water levels. Six different types of network architectures and training algorithms are investigated and compared in terms of model prediction efficiency and accuracy. The results of different experiments show that accurate predictions can be achieved with a standard feedforward neural network trained usung the Levenberg–Marquardt algorithm. The structure and spatial regressions of the ANN parameters (weights and biases) are then used for spatiotemporal model presentation. The efficiency of the spatio‐temporal ANN (STANN) model is compared with two hybrid neural‐geostatistics (NG) and multivariate time series‐geostatistics (TSG) models. It is found in this study that the ANNs provide the most accurate predictions in comparison with the other models. Based on the nonlinear intrinsic ANN approach, the developed STANN model gives acceptable results for the Tabriz multilayer aquifer. Copyright © 2008 John Wiley & Sons, Ltd.     

Analysis of flood propagation changes in the Kienstock—Bratislava reach of the Danube River/Analyse des changements de propagation des crues dans le tronçon Kienstock—Bratislava du Fleuve Danube

Abstract

In the first part of this study, a flood wave transformation analysis for the largest historical floods in the Danube River reach Kienstock–Bratislava was carried out. For the simulation of the historical (1899 and 1954) flood propagation, the nonlinear river model NLN-Danube (calibrated on the recent river reach conditions) was used. It was shown that the simulated peak discharges were not changed significantly when compared to their historical counterparts. However, the simulated hydrographs exhibit a significant acceleration of the flood wave movement at discharges of between 5000 and 9000 m³ s^-1. In the second part, the travel time-water level relationships between Kienstock and Bratislava were analysed on a dataset of the flood peak water levels for the period 1991–2002. An empirical regression routing scheme for the Danube short-term water level forecast at Bratislava station was derived. This is based on the measured water level at Kienstock gauging station.     

水文模型参数区域化研究进展

参数区域化方法是解决资料缺乏地区水文模拟和预报的有效手段,主要包括回归法、空间邻近法和属性相似法三类方法,可将有资料流域的水文模型参数移用到资料缺乏流域。首先回顾了区域化方法的基本原理和应用方法,并分析了三类主要区域化方法的适用性。从流域特征因子、水文模型及参数、不确定性探讨三个方面综述了区域化方法的研究进展。分析发现,当前区域化方法缺乏完善的理论基础,流域特征因子选择存在主观性,水文模型及参数的适用性方面研究不足。最后展望了未来的研究重点：（1）多维度适用性比较;（2）水文过程和参数的空间分布规律;（3）参数的尺度问题;（4）参数区域化的不确定性问题。     

Transformation of ammonium nitrogen and response characteristics of nitrifying functional genes in tannery sludge contaminated soil

High concentrations of ammonium nitrogen released from tannery sludge during storage in open air may cause nitrogen pollution to soil and groundwater. To study the transformation mechanism of NH₄⁺-N by nitrifying functional bacteria in tannery sludge contaminated soils, a series of contaminated soil culture experiments were conducted in this study. The contents of ammonium nitrogen (as NH₄⁺-N), nitrite nitrogen (as NO₂^?-N) and nitrate nitrogen (as NO₃^?-N) were analyzed during the culture period under different conditions of pollution load, soil particle and redox environment. Sigmodial equation was used to interpret the change of NO₃^?-N with time in contaminated soils. The abundance variations of nitrifying functional genes (amoA and nxrA) were also detected using the real-time quantitative fluorescence PCR method. The results show that the nitrification of NH₄⁺-N was aggravated in the contaminated silt soil and fine sand under the condition of lower pollution load, finer particle size and more oxidizing environment. The sigmodial equation well fitted the dynamic accumulation curve of the NO₃^?-N content in the tannery sludge contaminated soils. The Cr(III) content increased with increasing pollution load, which inhibited the reproduction and activity of nitrifying bacteria in the soils, especially in coarse-grained soil. The accumulation of NO₂^?-N contents became more obvious with the increase of pollution load in the fine sand, and only 41.5% of the NH₄⁺-N was transformed to NO₃^?-N. The redox environment was the main factor affecting nitrification process in the soil. Compared to the aerobic soil environment, the transformation of NH₄⁺-N was significantly inhibited under anaerobic incubation condition, and the NO₃^?-N contents decreased by 37.2%, 61.9% and 91.9% under low, medium and high pollution loads, respectively. Nitrification was stronger in the silt soil since its copy number of amoA and nxrA genes was two times larger than that of fine sand. Moreover, the copy numbers of amoA and nxrA genes in the silt soil under the aerobic environment were 2.7 times and 2.2 times larger than those in the anaerobic environment. The abundance changes of the amoA and nxrA functional genes have a positive correlation with the nitrification intensity in the tannery sludge-contaminated soil.