基于MicroStation V8的地形图更新与质检系统的开发

在MicroStation VBA环境下,对MicroStation V8进行二次开发,实现了一套基于MicroStation V8平台的多比例尺地形图要素更新与质检系统。该文着重阐述了该系统在处理点、线、面、注记、属性、出图效果及质量检查控制等方面的设计。通过二次开发,快速实现了地形图的更新与维护,并有利于质量的控制,符合入库要求。     

Probabilistic calibration of a distributed hydrological model for flood forecasting

Abstract

The complexity of distributed hydrological models has led to improvements in calibration methodologies in recent years. There are various manual, automatic and hybrid methods of calibration. Most use a single objective function to calculate estimation errors. The use of multi-objective calibration improves results, since different aspects of the hydrograph may be considered simultaneously. However, the uncertainty of estimates from a hydrological model can only be taken into account by using a probabilistic approach. This paper presents a calibration method of probabilistic nature, based on the determination of probability functions that best characterize different parameters of the model. The method was applied to the Real-time Interactive Basin Simulator (RIBS) distributed hydrological model using the Manzanares River basin in Spain as a case study. The proposed method allows us to consider the uncertainty in the model estimates by obtaining the probability distributions of flows in the flood hydrograph.

Citation Mediero, L., Garrote, L. & Martín-Carrasco, F. J. (2011) Probabilistic calibration of a distributed hydrological model for flood forecasting. Hydrol. Sci. J. 56(7), 1129–1149.     

Limitations of real-time models for forecasting river flooding from monsoon rainfall

Very intense rainfall during the southwest and northeast monsoons causes severe river flooding in India. Some traditional techniques used for real-time forecasting of flooding involve the relationship between effective rainfall and direct surface runoff, which simplifies the complex interactions between rainfall and runoff processes. There are, however, serious problems in deducing these variables in real time, so it is highly desirable to have a real-time flood forecasting model that would directly relate the observed discharge hydrograph to the observed rainfall. The storage routing model described by Baba and Hoshi (1997), Tanaka et al. (1997), and Baba et al. (2000), and a simplified version of this model, have been used to compute observed river discharge directly from observed hourly rainfall. This method has been used to study rainfall–runoff data of the Ajay River Basin in eastern India. Five intense rainfall events of this basin were studied. Our results showed that the Nash–Sutcliffe efficiency of discharge prediction for these five events was 98.6%, 94.3%, 86.9%, 85.6%, and 67%. The hindcast for the first two events is regarded as completely satisfactory whereas for the next two events it is deemed reasonable and for the fifth it is unsatisfactory. It seems the models will yield accurate hindcast if the rainfall is uniform over the drainage basin. When the rainfall is not uniform the performance of the model is unsatisfactory. In future this problem can, in principle, be corrected by using a weighted amount if rainfall is based upon multiple rain-gauge observations over the drainage basin. This would provide some measure of the dispersion in the rainfall. The model also seems unable to simulate flooding events with multiple peaks.     

梯级-关联算法原理及其在月流量预报中的应用

传统BP网络需要预先设定网络隐含层的层数和每层的节点数,使得在预测过程中难以确定网络的最优结构。与之相反,梯级-关联算法(CC)要求初始网络仅含有输入层和输出层,通过运算不断向网络增加隐含节点。在介绍梯级-关联算法原理的基础上,分别运用梯级-关联算法和BP算法对拉萨河拉萨站的月流量进行了预测,结果显示:在不损失预测精度的前提下,梯级-关联算法的运算次数仅为5次,而BP算法则需要运算70 000次,运算效率有很大的提高,同时网络的规模也有所减小。     

青海东部及邻区强震前地震活动特征参数Wq值的研究

利用1980～2007年以来,青海省东部及邻区（34°～40°N,96°～104°E）ML≥2．3级地震资料进行特征参数耽值的空间扫描。结果表明,在该区域内发生的9次6．0级以上地震中有6次震前7—28个月在震中周围出现叻值异常区,并且大部分地震前的异常区存在异常出现一异常区面积扩大一面积收缩一发震的变化过程,为地震的中短期预报提供了判定依据。     

A univariate model for long-term streamflow forecasting

This paper, the first in a series of two, employs the principle of maximum entropy (POME) via maximum entropy spectral analysis (MESA) to develop a univariate model for long-term streamflow forecasting. Three cases of streamflow forecasting are investigated: forward forecasting, backward forecasting (or reconstruction) and intermittent forecasting (or filling in missing records). Application of the model is discussed in the second paper.     

洪水预报中特征值预报的若干数学方法比较

讨论研究了水文特征值预报的数学方法,统计回归模型、神经网络模型和模糊回归模型。三个计处实例表明如果系统的线性关系较好,统计回归模型的结果最好;如果系统的线民生关系差,神经网络模型的结果最好;如果用于率定模型的资料太短,任何一个模型都不可靠。     

How to obtain alert velocity thresholds for large rockslides

A reliable forecast of the failure stage of large rockslides is difficult, because of non-linear time dependency of displacements and seasonal effects. Aim of this paper is to suggest a practical method to prepare alert thresholds for large rockslides, assessing critical values of velocity for carrying out civil protection actions using monitoring data. Adopted data concern the 20 Mm³ Ruinon rockslide (Valfurva, Central Alps, Italy), still evolving and suitable to originate a fast moving rock avalanche. Multitemporal analysis of aerial photos, LIDAR-ALTM laser topography, field survey and geomechanical analyses allowed to infer the rockslide kinematics and better understand data provided by a monitoring network including distometers, extensometers, GPS benchmarks and inclinometers. The analysis of displacement and rainfall data over five years (1997–2001) allowed to recognise three different evolutionary patterns of displacements, showing a continuously increasing rate since 1997. Data representing large-scale behaviour of the rock mass were fitted by power-law curves, according to the “accelerating creep” model by Voight, in order to evaluate a suitable failure time. This was hampered by the large seasonal deviations, which can significantly delay the occurrence of failure. Data were fitted using the Voight’s equation, expressed in terms of displacement, through non-linear estimation techniques, in order to find values of the controlling parameters (A, α and t_f) suitable to represent the mechanical behaviour of the rock mass approaching the failure. This allowed to compute velocity–time theoretical curves and to define different velocity threshold values (pre-alert, alert and emergency) to be used for emergency management.     

Effective typhoon characteristics and their effects on hourly reservoir inflow forecasting

This paper describes the identification of effective typhoon characteristics and the development of a new type of hourly reservoir inflow forecasting model with the effective typhoon characteristics. Firstly, a comparison of support vector machines (SVMs), which is a novel kind of neural networks (NNs), and back-propagation networks (BPNs) is made to select an appropriate NN-based model. The results show that SVM-based models are more appropriate than BPN-based models because of their higher accuracy and much higher efficiency. In addition, effective typhoon characteristics for improving forecasting performance are identified from all the collected typhoon information. Then the effective typhoon characteristics (the position of the typhoon and the distance between the typhoon center and the reservoir) are added to the proposed SVM-based models. Next, a performance comparison of models with and without effective typhoon characteristics is conducted to clearly highlight the effects of effective typhoon characteristics on hourly reservoir inflow forecasting. To reach a just conclusion, the performance is evaluated by cross validation, and the improvement in performance due to the addition of effective typhoon characteristics is tested by paired comparison t-tests at the 5% significance level. The results confirm that effective typhoon characteristics do improve the forecasting performance and the improvement increases with increasing lead-time, especially when the rainfall data are not available. For four- to six-hour ahead forecasts, the improvement due to the addition of effective typhoon characteristics increases from 3% to 18% and from 10% to 113% for Categories I (rainfall data are available) and II (rainfall data are not available), respectively. In conclusion, effective typhoon characteristics are recommended as key inputs for reservoir inflow forecasting during typhoons. The proposed SVM-based models with effective typhoon characteristics are expected to provide more accurate forecasts than BPN-based models. The proposed modeling technique is also expected to be useful to support reservoir operation systems and other disaster warning systems.     

Model identification for hydrological forecasting under uncertainty

Methods for the identification of models for hydrological forecasting have to consider the specific nature of these models and the uncertainties present in the modeling process. Current approaches fail to fully incorporate these two aspects. In this paper we review the nature of hydrological models and the consequences of this nature for the task of model identification. We then continue to discuss the history (“The need for more POWER‘’), the current state (“Learning from other fields”) and the future (“Towards a general framework”) of model identification. The discussion closes with a list of desirable features for an identification framework under uncertainty and open research questions in need of answers before such a framework can be implemented.