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复杂河道洪水预报系统研究——以淮河王家坝至小柳巷区间流域为例 总被引：2，自引：1，他引：1
随着社会经济的快速发展,洪水灾害造成的损失日益严重.洪水预报作为一项重要的防洪非工程措施,对防洪、抗洪工作起着至关重要的作用.淮河洪水危害的严重性和洪水演进过程的复杂性使得淮河洪水预报系统的研究长期以来受到高度重视.本文以王家坝至小柳巷区间流域为例,以河道洪水演算为主线,采用新安江三水源模型进行子流域降雨径流预报,概化具有行蓄洪区的干流河道,进行支流与干流、行蓄洪区与干流的洪水汇流耦合计算,采用实时更新的基于多元回归的方法确定水位流量关系,并以上游站点降雨径流预报模型提供的流量作为上边界条件、以下游站点的水位流量关系作为下边界条件,结合行蓄洪调度模型,建立具有行蓄洪区的河道洪水预报系统,再与基于K-最近邻(KNN)的非参数实时校正模型耦合,建立淮河中游河道洪水预报系统.采用多年资料模拟取得了较好的预报效果,并以2003和2007年大洪水为例进行检验,模拟结果精度较高,也证明了所建预报系统的合理性和适用性. 相似文献
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淮河流域洪水极值非平稳性特征 总被引：1，自引：1，他引：0
基于淮河流域9个水文站的月径流量数据,采用Pettitt非参数检验法、GAMLSS模型与洪水频率分析模型等方法,揭示了淮河中上游洪水频率的演变规律,分析基于平稳性和非平稳性条件下的洪水发生强度及洪涝灾害所带来的影响.研究发现:潢川、横排头和蚌埠站点未发生明显变异,其余6个站点发生均值或方差变异,变异时间主要集中在2000年左右.淮河流域的最优拟合分布函数是Weibull;班台、蒋家集和横排头站适宜于非平稳性模型,其余站点选择平稳性模型.各站点非平稳性条件下10年和20年一遇设计流量值与平稳性条件下皮尔逊Ⅲ型分布设计流量值相差不大,但30年一遇、50年一遇和100年一遇的设计流量相差逐渐变大.横排头站和蚌埠站洪水放大因子随着时间增加呈上升趋势且大于1,百年一遇重现期不足80年.各站点年最大洪峰流量与淮河流域、安徽省水灾面积通过了95%或99%的显著性检验. 相似文献
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半参数球谐函数(Semiparametric Spherical Harmonic, Semi-SH)模型能减少电离层预报模型误差以及残余周期带来的系统误差.但是半参数球谐函数模型中窗宽参数的选取会带来一定的估计偏差，本文利用长短期记忆神经网络(Long-Short Term Memory Networks, LSTM)在非平稳性时间序列预报中的良好适应性，提出了一种联合Semi-SH与LSTM(Semi-SH-LSTM)的全球电离层TEC短期组合预报模型，适用于1—5天的短期预报. Semi-SH-LSTM模型通过预报球谐函数系数解算全球电离层TEC,利用二次多项式和半参数核估计方法分别拟合球谐系数趋势项和周期项，最后基于LSTM对拟合残差进行补偿预报.本文利用欧洲定轨中心(Center for Orbit Determination in Europe, CODE)球谐函数系数产品，采用单天预报和多天预报两种实验方案验证Semi-SH-LSTM模型的有效性.实验结果表明，相比于Semi-SH模型和综合半参数与自回归模型，Semi-SH-LSTM模型的单天全球TEC预报残差RMS值分... 相似文献
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空间集总式水文模型的洪水预报精度会受到面平均雨量估计误差的严重影响.点雨量监测值的误差类型、误差大小以及流域的雨量站点密度和站点的空间分布都会影响到面平均雨量的计算.为提高实时洪水预报精度,本文提出了一种基于降雨系统响应曲线洪水预报误差修正方法.通过此方法估计降雨输入项的误差,从而提高洪水预报精度.此方法将水文模型做为输入和输出之间的响应系统,用实测流量和计算流量之间的差值做为信息,通过降雨系统响应曲线,使用最小二乘估计原理,对面平均雨量进行修正,再用修正后的面平均雨量重新计算出流过程.将此修正方法结合新安江模型使用理想案例进行检验,并应用于王家坝流域的16场历史洪水以及此流域不同雨量站密度的情况下,结果证明均有明显修正效果,且在雨量站密度较低时修正效果更加明显.该方法是一种结构简单且不增加模型参数和复杂度的实时洪水修正的新方法. 相似文献
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强烈下渗条件下天然河道洪水演进模拟方法 总被引：2，自引：0，他引：2
河道洪水演进模拟是河道洪水预报与汇流计算的主要内容和关键.对于常年断流、河水与地下水长期处于脱节状态的河道,一旦行洪下渗非常强烈,渗漏量很大.不考虑河道下渗洪水演进模拟模型,无法准确模拟真实的洪水运动行为,不能用于洪水模拟预报.本文采用土壤下渗理论描述河道下渗,推导出了河道下渗流量计算公式,建立了基于霍顿下渗公式的河道下渗模拟方法;并把下渗当做单位区间出流,与基于马斯京根康吉法天然河道洪水演进模型进行耦合,构建了强烈下渗条件下天然河道洪水演进模拟模型.针对天然河道水力特性复杂特点,研究了洪水演进模型参数确定方法和波速计算方法.海河流域漳卫河水系岳城水库-蔡小庄段的典型场次洪水应用结果表明,模型能很好反映强烈下渗条件下洪水演进实际情况,具有很高模拟精度,基于土壤下渗理论的下渗量计算公式和模拟方法可以很好地模拟河道下渗过程.这一模型具有计算简便、参数确定简单、适合于洪水预报等特点,在干旱和半干旱地区河道洪水演进模拟预报与河道汇流计算中也具有一定的推广应用价值. 相似文献
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准确、及时的入库洪水预报,对三峡水库综合效益的发挥和长江流域水旱灾害防御、水资源利用、流域综合管理等具有重要作用。基于预报误差的最优分布估计和分布函数动态参数假定,提出了一种三峡水库入库洪水概率预报方法,并进行了洪水概率预报业务试验。结果表明:本文所提方法科学可行,计算快捷,使用方便,便于在实时作业预报中应用推广;概率预报结果较确定性预报结果,在水量预报、预警效果等方面均有所改善,1～5 d预见期预报的确定性系数提高0.1%～3.4%,水量误差减少0.1%～4.8%,可为三峡水库实时调度提供更可靠的预报信息;所提出的三峡水库入库洪水概率预报业务化产品,可提供更多风险信息,为三峡水库的科学调度,尤其是洪水资源化利用提供更好的优化决策支撑。 相似文献
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一种求解贝叶斯模型平均的新方法 总被引：2，自引：0，他引：2
贝叶斯模型平均（Bayesian model averaging,BMA）是最近提出的一种用于多模式集合预报的统计方法.进行贝叶斯模型平均需要准确估算模型集合中每个竞争模型的权重与方差,经常采用的方法是期望最大化（Expectation-Maximization,EM）方法与马尔可夫链蒙特卡洛（Markov Chain Monte Carlo,MCMC）方法,两种方法各有优劣.本文首先对BMA的（对数）似然函数进行改进使之无需BMA权重之和为1的显式约束,并利用一种有限记忆的拟牛顿优化算法（LBFGS-B）对其进行极大化,由此提出了一种求解贝叶斯模型平均的新方法（BMA-BFGS）.采用三个陆面模式进行的土壤湿度多模式数值模拟试验表明：在计算精度方面,BMA-BFGS的精度与MCMC方法几乎一致,优于EM算法;在计算耗时性方面,BMA-BFGS的计算耗时与EM算法相当,远小于MCMC方法. 相似文献
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本文将半参数平差模型引入电离层球谐函数系数的预报中,建立了半参数球谐函数模型(Semiparametric-Spherical Harmonic,Semi-SH)来预测全球电离层总电子含量.首先,通过快速傅里叶变换获得球谐函数系数的周期和振幅,将振幅高的主周期归入趋势函数,振幅低的剩余周期归入随机信号,建立了半参数模型,同时利用核估计方法拟合趋势函数,解算随机信号,并在时间域上进行外推,得到了预报时间的球谐函数系数,代入15阶电离层球谐函数模型,最后得出电离层总电子含量(Total Electron Content,TEC)的预报值.本文基于欧洲定轨中心(CODE)发布的球谐函数系数进行电离层TEC长期预报和短期预报分析,其中长期预报采用四年预报两年的模式对球谐函数系数进行预报,短期预报设计了三个算例,采用前30天预报后一天的模式,分别预报1天、滑动预报7天和滑动预报30天.实验结果表明:长期预报能够较好地反映全球电离层TEC的变化趋势和波动情况,Semi-SH模型对全球电离层TEC平均值(Mean TEC global,MTECglobal)的拟合值和预报值与MTECglobal实际值的相关系数分别为0.8743和0.8010,呈现出高度相关性.短期预报中,在太阳活动高年和太阳活动低年,Semi-SH模型在中纬度地区预报精度较CODE发布的电离层TEC 1天预报产品(CODE′S 1-Day Predicted GIM,C1PG)有较大提升,在高纬度与低纬度地区两种模型预报精度相当;Semi-SH模型在太阳活动高年和太阳活动低年30天滑动预报精度的均值均高于C1PG模型.实验结果说明了Semi-SH模型预报电离层TEC值的有效性. 相似文献
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洪涝灾害是世界主要自然灾害之一,优化洪水预报方案对防洪决策至关重要,然而传统水文模型存在参数多、调参受人为因素影响,泛化能力弱等问题。针对上述问题,本文提出基于改进的鲸鱼优化算法和长短期记忆网络构建自动优化参数的WOA-LSTM模型,通过优化神经网络结构进一步增强该模型的稳定性和精确度,并且建立不同预见期下的洪水预报模型来分析讨论神经网络结构与预报期之间的关系。以横锦水库流域1986—1997年洪水资料为例,其中以流域7个雨量站点的降雨以及横锦站水文资料为输入,不同预见期下洪水过程作为输出,以1986—1993年作为模型的率定期,1994—1997年作为模型的检验期,研究结果表明:(1)以峰现时差、确定性系数、径流深误差和洪峰流量误差作为评价指标,相比较于LSTM模型和新安江模型对检验期的模拟结果表明WOA-LSTM模型拥有更高的精度、预报结果更稳定;(2)结合置换特征值和SHAP法分析模型特征值重要性,增强了神经网络模型的可解释性;(3)通过改变神经网络结构在一定程度避免由于预见期增加和数据关联性下降而导致的模型预报精度下降的问题,最终实验表明该模型在预见期1~6 h下都可以满足横锦水库的洪水预报要求,可以为当地的防洪决策提供依据。 相似文献
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This study evaluates two (of the many) modelling approaches to flood forecasting for an upland catchment (the River South Tyne at Haydon Bridge, England). The first modelling approach utilizes ‘traditional’ hydrological models. It consists of a rainfall–runoff model (the probability distributed model, or PDM) for flow simulation in the upper catchment. Those flows are then routed to the lower catchment using two kinematic wave (KW) routing models. When run in forecast‐mode, the PDM and KW models utilize model updating procedures. The second modelling approach uses neural network models, which use a ‘pattern‐matching’ process to produce model forecasts.Following calibration, the models are evaluated in terms of their fit to continuous stage data and flood event magnitudes and timings within a validation period. Forecast times of 1 h, 2 h and 4 h are selected (the catchment has a response time of approximately 4 h). The ‘traditional’ models generally perform adequately at all three forecast times. The neural networks produce reasonable forecasts of small‐ to medium‐sized flood events but have difficulty in forecasting the magnitude of the larger flood events in the validation period. Possible modifications to the latter approach are discussed. © Crown copyright 2002. Reproduced with the permission of Her Majesty's stationery office. Published by John Wiley & Sons, Ltd. 相似文献
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CHENG Liang WANG ZongZhi HU SiYi WANG YinTang JIN JuLiang ZHOU YuLiang 《中国科学:地球科学(英文版)》2015,(5):718-726
Flood routing models are critical to flood forecasting and confluence calculations. In the streams that dry up and disconnect from groundwater, the streambed infiltration is intensive and has a significant effect on flood wave movement. Streambed infiltration should be considered in flood routing. A flood routing model incorporating intensive streambed infiltration is proposed. In the model a streambed infiltration simulation method based on soil infiltration theory is developed. In this method the Horton equation is used to calculate infiltration capacity. A trial-and-error method is developed to calculate infiltration rate and determine whether the flood wave can travel downstream. A formula is derived to calculate infiltration flow per unit length. The Muskingum-Cunge method with streambed infiltration flow as lateral outflow is used for flood routing. The proposed model is applied to the stream from the downstream of the Yuecheng Reservoir to the Caixiaozhuang Hydrometric Station in the Zhangwei River of the Haihe River Basin. Simulation results show that the accuracy of the model is high, and the infiltration simulation method can represent infiltration processes well. The proposed model is simple and practical for flood simulation and forecasting, and can be used in river confluence calculations in a rainfall-runoff model for arid and semiarid regions. 相似文献
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Coupling the Xinanjiang model to a kinematic flow model based on digital drainage networks for flood forecasting 总被引：1，自引：0，他引：1
The Xinanjiang model, which is a conceptual rainfall‐runoff model and has been successfully and widely applied in humid and semi‐humid regions in China, is coupled by the physically based kinematic wave method based on a digital drainage network. The kinematic wave Xinanjiang model (KWXAJ) uses topography and land use data to simulate runoff and overland flow routing. For the modelling, the catchment is subdivided into numerous hillslopes and consists of a raster grid of flow vectors that define the water flow directions. The Xinanjiang model simulates the runoff yield in each grid cell, and the kinematic wave approach is then applied to a ranked raster network. The grid‐based rainfall‐runoff model was applied to simulate basin‐scale water discharge from an 805‐km2 catchment of the Huaihe River, China. Rainfall and discharge records were available for the years 1984, 1985, 1987, 1998 and 1999. Eight flood events were used to calibrate the model's parameters and three other flood events were used to validate the grid‐based rainfall‐runoff model. A Manning's roughness via a linear flood depth relationship was suggested in this paper for improving flood forecasting. The calibration and validation results show that this model works well. A sensitivity analysis was further performed to evaluate the variation of topography (hillslopes) and land use parameters on catchment discharge. Copyright © 2009 John Wiley & Sons, Ltd. 相似文献
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A methodology is proposed for constructing a flood forecast model using the adaptive neuro‐fuzzy inference system (ANFIS). This is based on a self‐organizing rule‐base generator, a feedforward network, and fuzzy control arithmetic. Given the rainfall‐runoff patterns, ANFIS could systematically and effectively construct flood forecast models. The precipitation and flow data sets of the Choshui River in central Taiwan are analysed to identify the useful input variables and then the forecasting model can be self‐constructed through ANFIS. The analysis results suggest that the persistent effect and upstream flow information are the key effects for modelling the flood forecast, and the watershed's average rainfall provides further information and enhances the accuracy of the model performance. For the purpose of comparison, the commonly used back‐propagation neural network (BPNN) is also examined. The forecast results demonstrate that ANFIS is superior to the BPNN, and ANFIS can effectively and reliably construct an accurate flood forecast model. Copyright © 2005 John Wiley & Sons, Ltd. 相似文献
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The orthogonal conditional nonlinear optimal perturbations (CNOPs) method, orthogonal singular vectors (SVs)method and CNOP+SVs method, which is similar to the orthogonal SVs method but replaces the leading SV (LSV) with the first CNOP, are adopted in both the Lorenz-96 model and Pennsylvania State University/National Center for Atmospheric Research (PSU/NCAR) Fifth-Generation Mesoscale Model (MM5) for ensemble forecasts. Using the MM5, typhoon track ensemble forecasting experiments are conducted for strong Typhoon Matsa in 2005. The results of the Lorenz-96 model show that the CNOP+SVs method has a higher ensemble forecast skill than the orthogonal SVs method, but ensemble forecasts using the orthogonal CNOPs method have the highest forecast skill. The results from the MM5 show that orthogonal CNOPs have a wider horizontal distribution and better describe the forecast uncertainties compared with SVs. When generating the ensemble mean forecast, equally averaging the ensemble members in addition to the anomalously perturbed forecast members may contribute to a higher forecast skill than equally averaging all of the ensemble members. Furthermore, for given initial perturbation amplitudes, the CNOP+SVs method may not have an ensemble forecast skill greater than that of the orthogonal SVs method, but the orthogonal CNOPs method is likely to have the highest forecast skill. Compared with SVs, orthogonal CNOPs fully consider the influence of nonlinear physical processes on the forecast results; therefore, considering the influence of nonlinearity may be important when generating fast-growing initial ensemble perturbations. All of the results show that the orthogonal CNOP method may be a potential new approach for ensemble forecasting. 相似文献
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将雷达测雨数据与分布式水文模型相耦合进行径流过程模拟,分析雷达测雨误差及其径流过程模拟效果,研究雷达测雨误差对径流过程模拟的影响效应.在对淮河流域气象中心业务化的5种淮河流域雷达测雨数据进行误差分析的基础上,采用雷达测雨数据驱动HEC-HMS水文模型,模拟分析淮河息县水文站以上流域2007年7月1-10日强降雨集中期的径流过程.结果表明:利用雷达测雨数据的径流模拟结果与实测资料的模拟结果基本吻合,各种雷达测雨数据误差经过HEC-HMS水文模型传递后,误差明显减小.联合校准法对应的模拟效果最好,过程流量相对误差NBs'和洪峰流量相对误差Z'分别为-20.2%和-13.3%. 相似文献
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《水文科学杂志》2013,58(2)
Abstract An updating technique is a tool to update the forecasts of mathematical flood forecasting model based on data observed in real time, and is an important element in a flood forecasting model. An error prediction model based on a fuzzy rule-based method was proposed as the updating technique in this work to improve one- to four-hour-ahead flood forecasts by a model that is composed of the grey rainfall model, the grey rainfall—runoff model and the modified Muskingum flow routing model. The coefficient of efficiency with respect to a benchmark is applied to test the applicability of the proposed fuzzy rule-based method. The analysis reveals that the fuzzy rule-based method can improve flood forecasts one to four hours ahead. The proposed updating technique can mitigate the problem of the phase lag in forecast hydrographs, and especially in forecast hydrographs with longer lead times. 相似文献