全过程联合校正方法在饶河流域洪水预报中的应用研究

为提高饶河流域洪水预报精度,将全过程联合校正(EPJC)方法与三水源新安江(XAJ)模型结合,按一定比例划分洪水预报总误差为各过程误差,基于系统响应理论反演得到面雨量计算误差和模型参数误差,重新输入流域水文模型正演得出校正后的洪水过程.选取洪峰流量相对误差、峰现时间绝对误差、径流深相对误差和确定性系数作为模型评价指标,...     

用时间序列模拟和预报天山乌鲁木齐河源1号冰川年径流总量

本文把1958—1984年天山乌鲁木齐河源1号冰川的融水径流总量的时间序列X(t),分解为趋势项L(t)、周期项P(t)、平稳项S(t)、随机项ε(t),使该时间序列表示为X(t)=L(t)+P(t)+S(t)+ε(t)。采用非线性回归提取L(t),用谱分析和Fourier级数提取P(t),余差用自回归方程建模,用上述项的叠加作出预报,按相关指数公式计算R=0.90,效果良好。     

考虑时变影响的拱坝坝肩空间变形场预测

坝肩变形是拱坝坝肩稳定状态的综合反映,在运行期呈小变形变化,监测点测值在空间上具有连续性,同时影响拱坝坝肩变形的各种因素具有时变性。通过融合统计回归和多层递阶方法的特点,建立了反映变形场空间和时变特性的空间时变分析模型。实例分析表明,该模型实用有效,预测精度高。考虑时变影响可以提高拱坝坝肩空间变形场的预测能力,对监控拱坝坝肩的实际工作状况有重要意义。     

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。     

广州金沙洲岩溶地面塌陷灾害预警预报研究

受武广客运专线金沙洲隧道施工抽排大量地下水的影响, 2007—2012年广州金沙洲发生了大规模岩溶地面塌陷、地面沉降地质灾害,造成了重大经济损失。为防治地质灾害,采用综合地质调查、地下水位监测、地面沉降监测、物探、钻探和试验等手段勘查和研究,建立了完善的地下水位和地面沉降监测网络。在此基础上,根据地质环境条件、岩溶发育程度和地质灾害分布特征进行岩溶塌陷易发性分区,选择地下水位变化量和地面沉降量这两个与岩溶塌陷最直接、最敏感和最重要的参数,从预警预报参数选择、时间尺度选取、判据分析计算、预警预报方法、模型系统的建立等方面进行研究,选取临界日综合地下水位变化量和临界日综合地面沉降量作为预警预报判据,结合三维地质结构模型建立岩溶塌陷预警预报模型系统。成功地预警预报了两起岩溶地面塌陷,取得较好的预报效果,说明该岩溶地面塌陷预警预报模型系统实用、有效,为金沙洲防灾减灾提供地质科学依据,为当地的社会稳定和经济可持续发展提供技术支撑。     

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。