基于面平均雨量误差修正的实时洪水预报修正方法

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.     

栅格新安江模型在天津于桥水库流域上游的应用

栅格新安江模型是在概念性新安江模型的理论基础上,以栅格为计算单元,结合地形地貌和下垫面特性构建出来的水文模型.在于桥水库流域上游的水平口流域应用栅格新安江模型,研究该地区洪水要素的空间变化以及洪水形成过程,讨论洪水模拟效果来验证模型在半湿润地区的适用性.选取水平口流域1978-2012年的洪水进行模型计算,模拟结果较好地反映了流域产流面积的时空变化,且均达到乙级以上精度.初步表明栅格新安江模型在半湿润地区有较好的适用性.     

大渡河猴子岩水库入库洪水过程预报-实时校正-概率预报集成

与传统确定性预报相比,洪水概率预报能够为防洪调度决策提供更为丰富的信息。以大渡河猴子岩水库以上流域为研究区,建立新安江次洪模型,并采用动态系统响应曲线进行实时洪水预报校正。在确定性预报校正基础上,建立基于水文不确定性处理器（HUP）的次洪概率预报模型,定量分析预报不确定性,实现入库洪水概率预报。结果表明：(1)利用猴子岩流域2009 2019年水文气象资料,建立的新安江次洪模型整体精度较高,率定期和验证期的洪量和洪峰相对误差均在±20%以内,平均确定性系数分别为0.69和0.72;经动态系统响应曲线校正后,洪峰和洪量误差均有降低,率定期和验证期的确定性系数分别提高0.13和0.09。(2)以2020年3场洪水未来48 h预报降雨为输入,新安江模型预报精度不高,且随着预见期增长而降低,但经动态系统响应曲线校正后,整体预报精度有所提高,洪量相对误差减小幅度超50%,确定性系数提高幅度超60%。(3)HUP次洪概率预报模型提供的分布函数中位数Q50的预报精度在一定程度上优于校正后的确定性预报;提供的90%置信区间覆盖率均在90%左右,离散度均在0.40以下,能以相对较窄的区间覆盖大部分实测值...     

新安江模型在资料匮乏的长江中下游山区中小流域洪水预报应用

水文资料匮乏流域的洪水预报(PUBs)是水文科学与工程中一个尚未解决的重大挑战.中国湿润山区中小流域大多是水文资料匮乏的流域,在此地区进行洪水预报的重要手段之一就是水文模型参数的估计.对基于参数物理意义的估算方法(以下简称物理估算法)及两种区域化方法进行了研究,将其用于新安江模型参数的估算及移植.皖南山区的29个中小流...     

基于土壤墒情抗差的降雨径流预报模型——设计与实验流域应用

本文旨在将实时监测得到的土壤墒情转化为流域水文模型可以直接使用的土壤含水量,论证将实时土壤墒情资料用于实时预报的可行性;利用实时监测土壤墒情,改进传统的模型结构,设计基于实测土壤墒情的降雨径流水文预报模型.采用土壤含水量误差抗差估计技术以抵御观测资料粗差的影响,提高系统的稳定性;并在此基础上提出了土壤含水量系统响应修正方法,以提高模型计算精度.将该模型应用于实验流域——宝盖洞流域进行应用检验,洪水模拟合格率达到92.3%,整体模拟精度达到甲级.     

半湿润半干旱流域空间组合模型研究

在半湿润半干旱地区,下垫面条件复杂,产流机制混合多变,而现有的水文模型由于其固定的结构和模式,无法灵活地模拟不同下垫面特征的洪水过程.本文利用CN-地形指数法将流域划分为超渗主导子流域和蓄满主导子流域.将新安江模型(XAJ)、新安江-Green-Ampt模型(XAJG)和Green-Ampt模型(GA)相结合,在子流域分类的基础上构建空间组合模型(SCMs),并在半湿润的东湾流域和半干旱的志丹流域进行检验.结果表明:东湾流域的参数由水文模型来主导;而志丹流域的参数受主导径流影响很大.在东湾流域,偏蓄满的模型模拟结果优于偏超渗的模型,且SCM2模型(XAJ和XAJG的组合模型)的模拟效果最好(径流深合格率为75%,洪峰合格率75%);而SCM5模型(GA和XAJG的组合模型)在以超渗产流为主的志丹流域模拟最好(径流深合格率53.3%,洪峰合格率53.3%).在半干旱半湿润流域,SCMs模型结构灵活,在地形和土壤数据的驱动下,具有更合理的模型结构和参数,模拟精度较高,适应性较强.     

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

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.     

基于微分响应的流域产流分单元修正方法

为考虑洪水预报误差的空间变化,提出一种基于微分响应的流域产流分单元修正方法.该方法建立了各单元流域产流与流域出口流量之间的微分响应关系,采用正则化最小二乘法结合逐步迫近进行反演求解,将产流误差估计量分配给相应单元流域实现流域产流分单元修正.将构建的方法应用于大坡岭流域和七里街流域进行新安江模型产流修正,比较分析了流域产流分单元修正、流域面平均产流修正和自回归修正的效果.结果表明：流域产流分单元修正效果优于流域面平均产流修正;随着预见期的增大,产流微分响应修正效果优于自回归修正.该方法通过汇流系统将流域出口断面流量信息进行分解用于修正各单元流域产流,有利于提高实时洪水预报精度.     

水文学与水力学相结合的南四湖洪水预报模型

南四湖流域是一个复杂的大流域，是东线南水北调的重要调节湖泊之一，也是干旱和洪水频繁流域．本文首先采 用分布式的新安江模型，对有实测流量资料的支流流域进行了模型参数率定，洪量预报达到了一定的精度，建立了南四湖 流域的洪水预报模型．采用一维、二维水力学模型并与水文学模型耦合进行上级湖的流量演进以及二级坝水利枢纽的 调度．     

基于洪水预报系统误差反演的多河段联合校正方法

提出一种基于洪水预报误差系统反演的多河段联合校正方法.采用马斯京根法矩阵方程描述多河段多区间入流的河道汇流过程,基于动力系统反演理论建立洪水预报误差的递推方程,最后利用修正后的多河段状态变量经演算得到预报断面的洪水过程,进而达到多河段联合校正目的.对大渡河上游的应用示例结果表明：多河段联合校正方法考虑了河系中断面间的水力联系及预报误差在时程上的传递规律,可充分利用上游多断面实测和校正信息进行下游预报断面的误差修正,因此具有更高的校正精度和稳定性.     

Updating Real-Time Flood Forecasting Using a Fuzzy Rule-Based Model/Mise à Jour de Prévision de Crue en Temps Réel Grâce à un Modèle à Base de Règles Floues

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.     

Hydrological data assimilation with the Ensemble Square-Root-Filter: Use of streamflow observations to update model states for real-time flash flood forecasting

The objective of the study is to evaluate the potential of a data assimilation system for real-time flash flood forecasting over small watersheds by updating model states. To this end, the Ensemble Square-Root-Filter (EnSRF) based on the Ensemble Kalman Filter (EnKF) technique was coupled to a widely used conceptual rainfall-runoff model called HyMOD. Two small watersheds susceptible to flash flooding from America and China were selected in this study. The modeling and observational errors were considered in the framework of data assimilation, followed by an ensemble size sensitivity experiment. Once the appropriate model error and ensemble size was determined, a simulation study focused on the performance of a data assimilation system, based on the correlation between streamflow observation and model states, was conducted. The EnSRF method was implemented within HyMOD and results for flash flood forecasting were analyzed, where the calibrated streamflow simulation without state updating was treated as the benchmark or nature run. Results for twenty-four flash-flood events in total from the two watersheds indicated that the data assimilation approach effectively improved the predictions of peak flows and the hydrographs in general. This study demonstrated the benefit and efficiency of implementing data assimilation into a hydrological model to improve flash flood forecasting over small, instrumented basins with potential application to real-time alert systems.     

On the criteria of model performance evaluation for real-time flood forecasting

Model performance evaluation for real-time flood forecasting has been conducted using various criteria. Although the coefficient of efficiency (CE) is most widely used, we demonstrate that a model achieving good model efficiency may actually be inferior to the naïve (or persistence) forecasting, if the flow series has a high lag-1 autocorrelation coefficient. We derived sample-dependent and AR model-dependent asymptotic relationships between the coefficient of efficiency and the coefficient of persistence (CP) which form the basis of a proposed CE–CP coupled model performance evaluation criterion. Considering the flow persistence and the model simplicity, the AR(2) model is suggested to be the benchmark model for performance evaluation of real-time flood forecasting models. We emphasize that performance evaluation of flood forecasting models using the proposed CE–CP coupled criterion should be carried out with respect to individual flood events. A single CE or CP value derived from a multi-event artifactual series by no means provides a multi-event overall evaluation and may actually disguise the real capability of the proposed model.     

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.     

WRF-Hydro模型与新安江模型在陈河流域的应用对比

本文利用全球陆面数据同化系统与降雨观测数据,以陕西半湿润区陈河流域为研究对象,驱动WRF-Hydro模型,研究该模型的表现和适用性,并在结构、参数、输入输出和模拟结果方面与新安江模型对比.考虑到次表面层与实际包气带的区别,引入土层厚度乘子ZSOILFAC对前者进行等比缩放,发现其与新安江模型反推包气带的厚度有较好的一致性.研究表明:在陈河流域中WRF-Hydro计算步长须在建议值的基础上缩小; WRF-Hydro模型善于模拟洪水细节,新安江模型表现好且稳定;前者的径流深和洪峰合格率平于或略低于后者;在两个指标均合格的洪水中,前者平均均方根误差比后者小21.5%,但对于其他洪水,前者平均均方根误差比后者大56.2%; WRF-Hydro在洪水起涨时刻模拟较好,表现出其在中小流域应用的潜力.     

Assessment of flood forecasting lead time based on generalized likelihood uncertainty estimation approach

Real time updating of rainfall-runoff (RR) models is traditionally performed by state-space formulation in the context of flood forecasting systems. In this paper, however, we examine applicability of generalized likelihood uncertainty estimation (GLUE) approach in real time modification of forecasts. Real time updating and parameter uncertainty analysis was conducted for Abmark catchment, a part of the great Karkheh basin in south west of Iran. A conceptual-distributed RR model, namely ModClark, was used for basin simulation, such that the basin’s hydrograph was determined by the superposition of runoff generated by individual cells in a raster-based discretization. In real time updating of RR model by GLUE method, prior and posterior likelihoods were computed using forecast errors that were obtained from the results of behavioral models and real time recorded discharges. Then, prior and posterior likelihoods were applied to modify forecast confidence limits in each time step. Calibration of parameters was performed using historical data while distribution of parameters was modified in real time based on new data records. Two scenarios of rainfall forecast including prefect-rainfall-forecast and no-rainfall-forecast were assumed in absence of a robust rainfall forecast model in the study catchment. The results demonstrated that GLUE application could offer an acceptable lead time for peak discharge forecast at the expense of high computational demand.