A non-linear perturbation model considering catchment wetness and its application in river flow forecasting

A non-linear perturbation model for river flow forecasting is developed, based on consideration of catchment wetness using an antecedent precipitation index (API). Catchment seasonality, of the form accounted for in the linear perturbation model (the LPM), and non-linear behaviour both in the runoff generation mechanism and in the flow routing processes are represented by a constrained non-linear model, the NLPM-API. A total of ten catchments, across a range of climatic conditions and catchment area magnitudes, located in China and in other countries, were selected for testing daily rainfall-runoff forecasting with this model. It was found that the NLPM-API model was significantly more efficient than the original linear perturbation model (the LPM). However, restriction of explicit non-linearity to the runoff generation process, in the simpler LMP-API form of the model, did not produce a significantly lower value of the efficiency in flood forecasting, in terms of the model efficiency index R².     

地下水测年方法及其在地震监测中的应用展望

对地下水动力学特征的观测与研究,是地震前兆监测的重要方法之一。地下水动力学研究中,利用地下水测年方法,可以对地下水的补给、径流特征及更新能力作出定性和定量的分析。本文介绍了近年来国际上常用的地下水测年方法,总结了前人在该领域的主要研究进展,重点分析了地下水更新能力与地震前兆信息的关系、地下水运动规律与地震构造活动的关系以及地下水浅层补给与异常干扰排除的方法等。已有的研究成果表明,地下水年龄的测定与分析方法对于了解监测点映震能力、评价构造活动与地震活动程度以及在观测资料异常变化的现场核实等方面,可以发挥重要作用。     

Sequential assimilation of soil moisture and streamflow data in a conceptual rainfall–runoff model

Soil moisture is a key hydrological variable in flood forecasting: it largely influences the partition of rain between runoff and infiltration and thus controls the flow at the outlet of a catchment. The methodology developed in this paper aims at improving the commonly used hydrological tools in an operational forecasting context by introducing soil moisture data into streamflow modelling. A sequential assimilation procedure, based on an extended Kalman filter, is developed and coupled with a lumped conceptual rainfall–runoff model. It updates the internal states of the model (soil and routing reservoirs) by assimilating daily soil moisture and streamflow data in order to better fit these external observations. We present in this paper the results obtained on the Serein, a Seine sub-catchment (France), during a period of about 2 years and using Time Domain Reflectivity probe soil moisture measurements from 0–10 to 0–100 cm and stream gauged data. Streamflow prediction is improved by assimilation of both soil moisture and streamflow individually and by coupled assimilation. Assimilation of soil moisture data is particularly effective during flood events while assimilation of streamflow data is more effective for low flows. Combined assimilation is therefore more adequate on the entire forecasting period. Finally, we discuss the adequacy of this methodology coupled with Remote Sensing data.     

Wavelet analysis-based projection pursuit autoregression model and its application in the runoff forecasting of Li Xiangjiang basin

ABSTRACT

The wavelet analysis technique was combined in this study with the projection pursuit autoregression (PPAR) model, and a new mid- and long-term runoff forecasting model, the wavelet analysis-based PPAR (PPAR-WA) is proposed, which realizes runoff forecasting from the perspective of the internal mechanism of a sequence. The runoff forecasting of the leading hydropower station in the Li Xianjiang cascade reservoirs in China was carried out to test the performance of the proposed model, and the accuracy and stability of the forecasting results were evaluated and analysed. The results show that the average relative error of the forecasting period can reach 9.6%, and the best relative error is less than 5% in some years. In addition, compared with PPAR, a back-propagation neural network and autoregression moving average model through three evaluation indexes, the results of PPAR-WA have higher accuracy and stronger stability. So, it has a certain value of popularization and application.     

一种新的综合物理指数构建及其在强降水预报中的应用

强降水是洪灾及相关衍生灾害的最主要原因之一,而过去单靠某一种变量诊断预报强降水,具有较大难度.本文在已有研究的基础上,根据强降水发生发展的物理机制,将引起降水的热力、动力和水汽条件综合考虑,尝试性地构建了一个新的综合指数THP(Temperature, Helicity and Precipitable water).然后针对两次强降水过程,利用NCEP/NCAR 1°×1°的再分析资料和地面常规观测资料,对THP指数进行了诊断分析,并选用2012年7月1日-8月15日的降水实况,对该指数进行了普适性检验.结果表明:(1) THP指数的变化可以有效表征强降水过程的发展和移动.对于降水落区的预报,THP指数的大值区与未来6 h的降水中心基本对应;对于降水发生时刻的预报,THP指数的位相变化超前于地面降水的变化,具有较好的指示性;(2) 对于高空槽前型降水,THP指数对降水强度也有一定的诊断意义,且普适性检验表明,该指数在我国中东部地区的盛夏期间具有良好的适用性;(3) 基于配料法的思想,THP指数将有利于强降水出现的、具有清晰物理意义的信号进行了集成,相比于表征单一物理量的指数,其稳定性得到了增强.     

改进的特征量方法及其在震源动力学模拟中的应用

基于曲线网格有限差分方法, 针对震源动力学模型中的边界条件的问题, 本文提出了一种改进的特征量方法, 即将牵引力镜像方法和特征量方法相结合, 在保守形式的方程中通过镜像操作完成单边导数的计算, 然后有约束地调整边界处的速度和应力值, 以此保证满足模型中的边界条件.改进的特征量方法可用于处理曲线网格当中一般的边界条件, 从而也可用于构建曲线网格有限差分法的分裂节点模型.我们将改进的特征量方法分别用于自由表面和断层面, 并分别与前人计算结果进行了比较.计算结果表明, 在相同的网格划分的情况下, 改进的特征量方法能够得到与其他方法相似的计算结果, 但改进方法的异常震荡更小, 从而证明了本文提出的改进的特征量方法用于曲线网格有限差分法分裂节点模型的可行性.

     

A data based mechanistic approach to nonlinear flood routing and adaptive flood level forecasting

Operational flood forecasting requires accurate forecasts with a suitable lead time, in order to be able to issue appropriate warnings and take appropriate emergency actions. Recent improvements in both flood plain characterization and computational capabilities have made the use of distributed flood inundation models more common. However, problems remain with the application of such models. There are still uncertainties associated with the identifiability of parameters; with the computational burden of calculating distributed estimates of predictive uncertainty; and with the adaptive use of such models for operational, real-time flood inundation forecasting. Moreover, the application of distributed models is complex, costly and requires high degrees of skill. This paper presents an alternative to distributed inundation models for real-time flood forecasting that provides fast and accurate, medium to short-term forecasts. The Data Based Mechanistic (DBM) methodology exploits a State Dependent Parameter (SDP) modelling approach to derive a nonlinear dependence between the water levels measured at gauging stations along the river. The transformation of water levels depends on the relative geometry of the channel cross-sections, without the need to apply rating curve transformations to the discharge. The relationship obtained is used to transform water levels as an input to a linear, on-line, real-time and adaptive stochastic DBM model. The approach provides an estimate of the prediction uncertainties, including allowing for heterescadasticity of the multi-step-ahead forecasting errors. The approach is illustrated using an 80 km reach of the River Severn, in the UK.     

世界地磁场模型EMM2010及其应用

本文主要介绍了世界地磁场模型EMM2010及其子模型NDGC-720,采用了实测值和计算值比对、不同条件下的实验数据来验证模型的精度、算法和程序的准确性的方法,并在MATLAB下进行仿真实验.结果表明,两模型具有很高的计算精度,适合大区域长时间的磁场变化趋势的预测,而在小区域的精确匹配上则有其内在的局限性.     

中国地区电离层foF2重构方法及其在短期预报中的应用

提出了一种适合于中国地区电离层f_oF₂的重构方法——以中国参考电离层为背景的改进克里格法.该方法把f_oF₂的估计值与中国参考电离层模型值之差值的相对值作为区域化变量,引入电离层距离,采用克里格法实现区域电离层重构.与直接利用f_oF₂进行克里格重构相比,以中国参考电离层为背景场保持了电离层的区域特征,提高了重构的准确性和稳定性.利用我国电离层垂测台站网的数据对该方法应用于中国地区的重构精度进行了评估.该方法与单站预报的自相关分析法相结合可实现中国地区电离层f_oF₂短期预报.     

肾脏CT扫描前准备工作的改进方法

本文回顾分析１９８７年３月￣１９９２年５月行肾脏ＣＴ扫描１０２例，其中诊断肾结石３１，当时因ＣＴ扫描前准备方法问题，及开始经验不足造成４例无结石的病人误诊为结石。找到误诊石的原因是ＣＴ扫描前静脉试验及口服泛影葡胺造影剂造成，采取改进ＣＴ扫描前准备方法；（１）扫描前不行静脉试验（２）口服造影剂改为口服温水或牛奶，经上述改进方法，以后未出现造影剂造成肾结石误诊。     

改进的模拟退火算法及其在叠前储层参数反演中的应用

快速模拟退火算法中,要求扰动后新的模型参数必须在取值范围内,对此,人们通常采用两种方法:(1)反复扰动直到新的模型参数满足要求;(2)当新的模型参数超过最大值时,参数等于最大值;新的模型参数小于最小值时,参数等于最小值.研究发现,方法1在计算过程中增加了寻找新模型参数的时间开销,计算效率明显降低;方法2当目标函数在边界或边界的小邻域存在多个极小值时,在有限的时间内,其陷入局部极小的概率大大增加,优化效果明显下降.本文在前人研究的基础上提出了改进的模拟退火算法,同时利用改进的算法反演了地层泊松比、纵波速度和密度.结果表明,改进的算法加快了模拟退火算法的收敛速度,提高了模拟退火算法的效率和精度.     

Parallelization of a coupled wave-circulation model and its application

Using the technique of Message Passing Interface, a parallelized version of a coupled wave-circulation model was set up. The tested model is a regional one for simulating the seas off China, which is comprised of 450,625 elements and 30 vertical sigma layers. The implementation efficiency was evaluated on two kinds of computers, the HP Integrity Superdome and SGI Altix 4700 multiprocessor. The numerical results show that the low-communication high-extra-computation scheme (LCHC) produces higher efficiency than the high-communication no-extra-computation scheme (HCNC) while the number of processors exceeds 24 for HP Integrity Superdome and eight for SGI Altix 4700, respectively. The experiments with both LCHC and HCNC scheme show super-linear speed-up when the number of processors is small. The model with the LCHC scheme is preferred as it achieves parallel efficiency in excess of 90% on the HP machines for all experiments with the number of processors no more than 100, while the efficiency decreases rapidly with the HCNC scheme after the number of processors increases to more than 24. Numerical results suggest that the parallelization of this coupled wave-circulation model is efficient and portable to a variety of parallel architectures.     

Cloud tracking using satellite data for predicting the probability of heavy rainfall events in the Mediterranean area

The use of cloud tracking techniques and storm identification procedures is proposed in this paper with the aim of predicting the evolution of cloud entities associated with the highest rainfall probability within a given meteorological scenario. Suitable algorithms for this kind of analysis are based on the processing of digital images in the thermal infrared (IR) band from geostationary satellites: a selection of such algorithms is described in some detail together with a few real case applications. Three heavy rainfall events have been selected for this purpose with reference to the extreme meteorological situation observed during Fall 1992 and 1993 over the Mediterranean area. A window from 30 to 60 °N and from 20 °W to 30 °E has been identified for the analysis of data from the radiometer on board the ESA Meteosat platform. In conclusion, the suitability of cloud tracking techniques for predicting the probability of heavy rainfall events is discussed provided that the former are associated with proper modeling of small scale rainfall distribution.     

Porous-grain-upper-boundary model and its application to Tarim Basin carbonates

Most of the carbonates in the Tarim Basin in northwest China are low-porosity and low-permeability rocks. Owing to the complexity of porosity in carbonates, conventional rockphysics models do not describe the relation between velocity and porosity for the Tarim Basin carbonates well. We propose the porous-grain-upper-boundary (PGU) model for estimating the relation between velocity and porosity for low-porosity carbonates. In this model, the carbonate sediments are treated as packed media of porous elastic grains, and the carbonate pores are divided into isolated and connected pores The PGU model is modified from the porous-grain-stiff-sand (PGST) model by replacing the critical porosity with the more practical isolated porosity. In the implementation, the effective elastic constants of the porous grains are calculated by using the differential effective medium (DEM) model. Then, the elastic constants of connected porous grains in dry rocks are calculated by using the modified upper Hashin-Shtrikman bound. The application to the Tarim carbonates shows that relative to other conventional effective medium models the PGU model matches the well log data well.     

The dependence of UK extreme sea levels and storm surges on the North Atlantic Oscillation

The role of the North Atlantic Oscillation (NAO) in effecting changes in winter extreme high and low waters and storm surges in UK waters has been investigated with the use of a depth-averaged tide+surge numerical model. Spatial patterns of correlation of extreme high and low waters (extreme still water sea levels) with the NAO index are similar to those of median or mean sea level studied previously. Explanations for the similarities, and for differences where they occur, are proposed. Spatial patterns of correlations of extreme high and low and median surge with the NAO index are similar to the corresponding extreme sea-level patterns. Suggestions are made as to which properties of surges (frequency, duration, magnitude) are linked most closely to NAO variability. Several climate models suggest higher (more positive) average values of NAO index during the next 100 years. However, the impact on the UK coastline in terms of increased flood risk should be low (aside from other consequences of climate change such as a global sea-level rise) if the existing relationships between extreme high waters and NAO index are maintained.     

Influence of spatial distribution of sensors and observation accuracy on the assimilation of distributed streamflow data in hydrological modelling

The aim of this study is to assess the influence of sensor locations and varying observation accuracy on the assimilation of distributed streamflow observations, also taking into account different structures of semi-distributed hydrological models. An ensemble Kalman filter is used to update a semi-distributed hydrological model as a response to measured streamflow. Various scenarios of sensor locations and observation accuracy are introduced. The methodology is tested on the Brue basin during five flood events. The results of this work demonstrate that the assimilation of streamflow observations at interior points of the basin can improve the hydrological models according to the particular location of the sensors and hydrological model structure. It is also found that appropriate definition of the observation accuracy can affect model performance and consequent flood forecasting. These findings can be used as criteria to develop methods for streamflow monitoring network design.     

Urban water system theory and its model development and application

The urban water system theory is an extension of the basin water system science on an urban scale, providing a new systematic solution for the unbalanced human-water relationship and severe water challenges, such as waterlogging, black and odorous water, and ecological degradation caused by urbanization. Most existing studies on urban water systems have focused on individual water cycle processes linked with water supply and sewage treatment plants, but mutual feedback between the water cycle an...     

Modified image algorithm to simulate seismic channel waves in 3D tunnel model with rugged free surfaces

The existence of rugged free‐surface three‐dimensional tunnel conditions in the coal seams, caused either by geological or mining processes, will inevitably influence wave propagation characteristics when the seismic waves go through the coal mines. Thus, a modified image algorithm has been developed to account for seismic channel waves propagating through this complicated topography with irregular free surfaces. Moreover, the seismic channel waves commonly exhibit damped and dispersive signatures, which is not only because of their own unique sandwich geometry of rock–coal–rock but also because of the viscoelastic behavior of coal. Considering the complexity of programming in three‐dimensional tunnel models with rugged free surfaces, an optimized vacuum grid search algorithm, enabling to model highly irregular topography and to compute efficiently, is also proposed when using high‐order staggered finite‐difference scheme to simulate seismic channel wave propagations in viscoelastic media. The numerical simulations are implemented to investigate the accuracy and stability of the method and the impact of coal's viscoelastic behavior on seismic channel wave propagation characteristics. The results indicate that the automatic vacuum grid search algorithm can be easily merged into high‐order staggered finite‐difference scheme, which can efficiently be applied to calculate three‐dimensional tunnel models with rugged free surfaces in the viscoelastic media. The simulation also suggests that the occurrence of a three‐dimensional tunnel with free surfaces has a remarkable influence on the seismic channel wave propagation characteristics and elastic energy distribution.     

Classified real-time flood forecasting by coupling fuzzy clustering and neural network

This paper presented a new classified real-time flood forecasting framework by integrating a fuzzy clustering model and neural network with a conceptual hydrological model. A fuzzy clustering model was used to classify historical floods in terms of flood peak and runoff depth, and the conceptual hydrological model was calibrated for each class of floods. A back-propagation (BP) neural network was trained by using real-time rainfall data and outputs from the fuzzy clustering model. BP neural network provided a rapid on-line classification for real-time flood events. Based on the on-line classification, an appropriate parameter set of hydrological model was automatically chosen to produce real-time flood forecasting. Different parameter sets was continuously used in the flood forecasting process because of the changes of real-time rainfall data and on-line classification results. The proposed methodology was applied to a large catchment in Liaoning province, China. Results show that the classified framework provided a more accurate prediction than the traditional non-classified method. Furthermore, the effects of different index weights in fuzzy clustering were also discussed.