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1.
阿克苏河(中吉国际河流)现已成为塔里木河的主河源,它对塔里木河干流的形成、发展和演变过程起着决定性作用.随着国家西部开发战略--塔里木河流域综合治理的深入开展和实施,阿克苏河流域的水文特征、水文预报等研究成为热点.特别是在干旱区中纬度高海拔流域的河流中,阿克苏河是以冰雪融水补充为主河流的典型代表,对阿克苏河流域径流进行预报研究具有理论和现实意义.鉴于此:(i)结合干旱区无资料或少资料的现状,利用现有的水文气象资料,尝试并构建日尺度水文预报方法;(ii)采用高空气温代替地面实测气温与日径流相关关系法、AR(p)预报模型、气温降雨修正的AR(p)预报模型和NAM降雨径流模型,对阿克苏流域的两大支流进行日径流模拟和预报;(iii)对4种方法模拟结果进行对比分析,表明利用气温和降雨修正后的AR(p)模型所用水文气象资料少、应用简便、预报精度较高、比较适用于资料较缺乏的阿克苏流域的短期径流预报.该研究以日尺度进行水文预报,在该流域尚属首次,不仅为阿克苏河、塔里木河的水文预报、洪水防治和全流域的水量调度等提供基础,也为干旱区其他流域的水文预报提供了参考方法. 相似文献
2.
阿克苏河(中吉国际河流)现已成为塔里木河的主河源,它对塔里木河干流的形成、发展和演变过程起着决定性作用.随着国家西部开发战略--塔里木河流域综合治理的深入开展和实施,阿克苏河流域的水文特征、水文预报等研究成为热点.特别是在干旱区中纬度高海拔流域的河流中,阿克苏河是以冰雪融水补充为主河流的典型代表,对阿克苏河流域径流进行预报研究具有理论和现实意义.鉴于此(i)结合干旱区无资料或少资料的现状,利用现有的水文气象资料,尝试并构建日尺度水文预报方法;(ii)采用高空气温代替地面实测气温与日径流相关关系法、AR(p)预报模型、气温降雨修正的AR(p)预报模型和NAM降雨径流模型,对阿克苏流域的两大支流进行日径流模拟和预报;(iii)对4种方法模拟结果进行对比分析,表明利用气温和降雨修正后的AR(p)模型所用水文气象资料少、应用简便、预报精度较高、比较适用于资料较缺乏的阿克苏流域的短期径流预报.该研究以日尺度进行水文预报,在该流域尚属首次,不仅为阿克苏河、塔里木河的水文预报、洪水防治和全流域的水量调度等提供基础,也为干旱区其他流域的水文预报提供了参考方法. 相似文献
3.
以淮河流域吴家渡水文站作为试验站点,采用基于贝叶斯平均法(BMA)的集合预报模型处理来源于马斯京根法、一维水动力学方法、BPNN(Back Propagation Neural Network)的预报流量序列,通过分析BMA的参数以及其预报结果,对各方法在淮河典型站点流量预报中的适用性进行验证与分析.经2003—2016年19场洪水模拟检验可知,BMA模型能够有效避免模型选择带来的洪水预报误差放大效应,可以提供高精度、鲁棒性强的洪水预报结果.通过进一步比较各模型统计最优的频率与BMA权重值之间的相关性,发现权重值不适用于对单场洪水预报精度评定,而适用于描述多场洪水预报中,模型为最优的统计频率;基于大量先验信息,提前获取BMA的权重等参数,将是指导模型选择、降低洪水预报不确定性、改进洪水预报技术的有效手段. 相似文献
4.
简单介绍了径向基函数神经网络方法的原理和应用,发展了用径向基函数（RBF）对平滑月平均黑子数进行预报的方法. 用不同的数据序列对网络进行训练,对未来8个月的平滑月平均黑子数进行预报. 用该方法对第23周开始后的平滑月平均黑子数进行逐月预报,并与实测值进行比较,结果表明随着预报实效的延长预报误差被逐渐放大,该方法可以较准确地做出未来４个月的预报,绝对误差可以控制在20以内,标准差为4.8,相对误差控制在38％以内,大部分相对误差不超过15%（占总预报数的89％）,具有较好的应用价值. 用于网络训练的样本数量对预报结果会产生一定的影响. 相似文献
5.
分析了目前我国长，中，短期地震预报存在的主要问题和难点。认为，我国地震大形势，长趋势判定的技术思路和主要方法目前是统计，类比和外推，它不能揭示地震活动的活跃或平衡的本质，有时出现失误和偏差，地震活动长趋势实质上是个地震动力学问题；中期预报的主要困难是地震何时发生和是否发生存在很大的不确定性，目前我国使用的各类预报方法的预报效能均较低，准确的时间报难以达到；短期预报的主要难点是发震地点的不确定性和地点预报的复杂性，现“以场求源”思路受到挑战。作者对如何解决上述问题提出了一些看法和建议。 相似文献
6.
前言 从历史资料分析,某个区域的地震时间分布(时序)并非毫无规律可寻,而是近似地服从某一分布的,根据时间序列分析来进行中长期预报是有可能的。文献[1][2]利用灰色建模方法得出预报模型初步证实了这一点。本文将给出一种较方便的建模方法(回归法)来对地震发生时间进行预报,在某些情况下,其效果明显优于文献[1][2]。 相似文献
7.
极移是指地球瞬时自转轴在地球本体内运动而导致极点在地球表面上的位置发生缓慢变化的现象,是研究地球自转的一个重要的内容.LS＋AR模型被认为是目前极移预报中最好的模型之一.通过对LS＋AR模型的短期预报残差的时间序列统计分析,发现相邻期的模型预报残差具有极高的相关性,由此提出利用上一期的模型预报残差和经验调节矩阵对下一期预报结果进行修正,从而建立了一种适用于极移预报的附加误差修正的LS＋AR新模型.运用新模型进行了模拟预报,结果表明该方法对于提高极移的预报精度和可靠性均有帮助,采用新方法进行超短期和短期预报的精度均达到了目前国际最好的精度水平. 相似文献
8.
基于非线性误差信息熵理论,通过分析非线性误差信息熵和气候态信息熵随时间演变规律,引进了定量估计大气多变量系统可预报性的联合可预报期限和单变量可预报期限,该期限既适合度量气候态信息熵为常值的可预报性,也适合气候态信息熵随时间变化的情形.利用NCEP/NCAR逐日再分析资料,计算了非线性误差信息熵和气候态信息熵随时间演变以及相应的可预报期限,并对冬季大气500 hPa温度场、纬向风场和经向风场的各单变量可预报性和三变量联合可预报性进行了分析.结果表明:对于单变量可预报性来说,温度场和纬向风场的可预报性相对较大,经向风场最小,它们的可预报期限具有纬向带状分布特征,尤其是经向风场,其可预报期限在纬向上明显存在3条低值带和4条高值带;对于多变量联合可预报性来说,由于各变量之间相互联系,多变量联合可预报期限不是单变量可预报期限的简单平均或线性组合,其可小于所有单变量的可预报性期限,也可介于各单变量可预报期限之间,且这个特征具有非常明显的区域差异,不同区域具有不同的结果. 相似文献
9.
洞庭湖地处北亚热带季风湿润气候区,水情时空变化尤为明显.为了探明洞庭湖水位时空演变特征,以洞庭湖6个水位站(城陵矶、鹿角、营田、杨柳潭、南咀、小河咀)、出入湖流量("三口"总入湖流量、"四水"总入湖流量、城陵矶出湖流量)和长江干流流量(宜昌、螺山)等1985-2014年逐日数据为基础,通过构建泰森多边形计算湖泊水位,运用Morlet小波分析、层次聚类分析和地统计理论研究湖泊水位的周期性变化规律及空间分布格局和自相关性.研究结果表明:洞庭湖水位变化具有典型的季节性,且年际变化具有28和22 a的多时间尺度特征;水位空间分布格局呈现出小河咀、南咀、杨柳潭(Group 1)以及城陵矶、鹿角、营田(Group 2)两种聚类,且在不同水文季节的空间自相关性依次表现为丰水期退水期涨水期枯水期.通过建立两类水位在不同水文季节与径流量的多元逐步回归模型揭示了洞庭湖水位时空演变的驱动因素,其中Group 1水位演变主要受长江干流水文情势的影响,Group 2水位演变由出入湖径流量和长江干流径流量共同作用,并随着不同水文季节江湖关系的改变以及湖泊自身水力联系的变化而变化.研究结果对于科学认识洞庭湖水位的时空演变规律以及湖泊生态系统保护和水资源的规划、管理与调控具有重要意义. 相似文献
10.
本文简要介绍了应用门限自回归方法处理浑沌时间序列,建立了浑沌时间序列自回归预报模型,并用其进行外推预报的基本方法及步骤。作者运用该方法建立了云南及滇西、滇东和滇西南等三个片区的半年最大震级序列的震级预报模型。由此预测了1991年上半年模型相应地区可能发生的最大地震震级。 相似文献
11.
《水文科学杂志》2013,58(4)
Abstract The issue of data size (length) requirement for correlation dimension estimation continues to be the nucleus of criticisms on the (low) correlation dimensions reported for hydrological series. The present study addresses this issue from the viewpoints of both the existing theoretical guidelines and the practical reality. For this purpose, correlation dimension analysis is carried out for various data sizes from each of three types of series: (a) stochastic series (artificially generated using a random number generation technique); (b) chaotic series (artificially generated using the Henon map equation); and (c) hydrological series (real flow data observed on the Göta River in Sweden). The outcomes of the analysis of the (artificial) stochastic and chaotic series are used as a basis for interpreting the outcomes of the hydrological series. It is found that reliable dimension results for the stochastic and chaotic series are obtained even when the data size is only a few hundred points (i.e. no underestimation of dimension for small data sizes is visible), with no significant change in the scaling regimes (of the dimension plots) with respect to data size. This implies that the dimension results obtained for the hydrological series even with a few hundred points are also close to the actual ones. The insignificant difference in the scaling regimes for the various data sizes further supports this point. These results lead to the conclusions that: (1) the issue of data size requirement for correlation dimension estimation is more of a myth than reality; (2) the dimension estimates reported thus far for hydrological series could indeed be close to the actual ones (unless influenced by factors other than data size, e.g. delay time, noise, zeros, intermittency). 相似文献
12.
Hydrological uncertainty processor based on a copula function 总被引：1，自引：0，他引：1
Quantifying the uncertainty in hydrological forecasting is valuable for water resources management and decision-making processes. The hydrological uncertainty processor (HUP) can quantify hydrological uncertainty and produce probabilistic forecasts under the hypothesis that there is no input uncertainty. This study proposes a HUP based on a copula function, in which the prior density and likelihood function are explicitly expressed, and the posterior density and distribution obtained using Monte Carlo sampling. The copula-based HUP was applied to the Three Gorges Reservoir, and compared with the meta-Gaussian HUP. The Nash-Sutcliffe efficiency and relative error were used as evaluation criteria for deterministic forecasts, while predictive QQ plot, reliability, resolution and continuous rank probability score (CRPS) were used for probabilistic forecasts. The results show that the proposed copula-based HUP is comparable to the meta-Gaussian HUP in terms of the posterior median forecasts, and that its probabilistic forecasts have slightly higher reliability and lower resolution compared to the meta-Gaussian HUP. Based on the CRPS, both HUPs were found superior to deterministic forecasts, highlighting the effectiveness of probabilistic forecasts, with the copula-based HUP marginally better than the meta-Gaussian HUP. 相似文献
13.
Simulation of rainfall-runoff process in urban areas is of great importance considering the consequences and damages of extreme runoff events and floods. The first issue in flood hazard analysis is rainfall simulation. Large scale climate signals have been proved to be effective in rainfall simulation and prediction. In this study, an integrated scheme is developed for rainfall-runoff modeling considering different sources of uncertainty. This scheme includes three main steps of rainfall forecasting, rainfall-runoff simulation and future runoff prediction. In the first step, data driven models are developed and used to forecast rainfall using large scale climate signals as rainfall predictors. Due to high effect of different sources of uncertainty on the output of hydrologic models, in the second step uncertainty associated with input data, model parameters and model structure is incorporated in rainfall-runoff modeling and simulation. Three rainfall-runoff simulation models are developed for consideration of model conceptual (structural) uncertainty in real time runoff forecasting. To analyze the uncertainty of the model structure, streamflows generated by alternative rainfall-runoff models are combined, through developing a weighting method based on K-means clustering. Model parameters and input uncertainty are investigated using an adaptive Markov Chain Monte Carlo method. Finally, calibrated rainfall-runoff models are driven using the forecasted rainfall to predict future runoff for the watershed. The proposed scheme is employed in the case study of the Bronx River watershed, New York City. Results of uncertainty analysis of rainfall-runoff modeling reveal that simultaneous estimation of model parameters and input uncertainty significantly changes the probability distribution of the model parameters. It is also observed that by combining the outputs of the hydrological models using the proposed clustering scheme, the accuracy of runoff simulation in the watershed is remarkably improved up to 50% in comparison to the simulations by the individual models. Results indicate that the developed methodology not only provides reliable tools for rainfall and runoff modeling, but also adequate time for incorporating required mitigation measures in dealing with potentially extreme runoff events and flood hazard. Results of this study can be used in identification of the main factors affecting flood hazard analysis. 相似文献
14.
针对降雨输入不确定性对实时洪水预报影响的问题，本文采用不考虑未来预报降雨、考虑未来预报降雨、考虑预报降雨的降雨量误差和降雨时间误差4种方法，以陕西省两个半湿润流域（陈河流域和大河坝流域）为研究区域，分析不同预见期和不同降雨输入情况下洪水预报的精度.研究表明：相对于不考虑未来降雨情况，考虑未来降雨后在预报预见期较长时对预报结果精度提升较大，在预见期较短时对预报结果精度提升不显著；暴雨中心位置不同对预报精度影响也不同，当暴雨中心位于流域下游时降雨量误差对流量预报误差影响更大；降雨量误差主要影响洪量相对误差和洪峰相对误差，且这种影响是线性的，对确定性系数的影响是非线性的二次函数，降雨时间误差主要影响峰现时间误差. 相似文献
15.
Abstract There is a lack of consistency and generality in assessing the performance of hydrological data-driven forecasting models, and this paper presents a new measure for evaluating that performance. Despite the fact that the objectives of hydrological data-driven forecasting models differ from those of the conventional hydrological simulation models, criteria designed to evaluate the latter models have been used until now to assess the performance of the former. Thus, the objectives of this paper are, firstly, to examine the limitations in applying conventional methods for evaluating the data-driven forecasting model performance, and, secondly, to present new performance evaluation methods that can be used to evaluate hydrological data-driven forecasting models with consistency and objectivity. The relative correlation coefficient (RCC) is used to estimate the forecasting efficiency relative to the naïve model (unchanged situation) in data-driven forecasting. A case study with 12 artificial data sets was performed to assess the evaluation measures of Persistence Index (PI), Nash-Sutcliffe coefficient of efficiency (NSC) and RCC. In particular, for six of the data sets with strong persistence and autocorrelation coefficients of 0.966–0.713 at correlation coefficients of 0.977–0.989, the PIs varied markedly from 0.368 to 0.930 and the NSCs were almost constant in the range 0.943–0.972, irrespective of the autocorrelation coefficients and correlation coefficients. However, the RCCs represented an increase of forecasting efficiency from 2.1% to 37.8% according to the persistence. The study results show that RCC is more useful than conventional evaluation methods as the latter do not provide a metric rating of model improvement relative to naïve models in data-driven forecasting. Editor D. Koutsoyiannis, Associate editor D. Yang Citation Hwang, S.H., Ham, D.H., and Kim, J.H., 2012. A new measure for assessing the efficiency of hydrological data-driven forecasting models. Hydrological Sciences Journal, 57 (7), 1257–1274. 相似文献
16.
Although artificial neural networks (ANNs) have been applied in rainfall runoff modelling for many years, there are still many important issues unsolved that have prevented this powerful non‐linear tool from wide applications in operational flood forecasting activities. This paper describes three ANN configurations and it is found that a dedicated ANN for each lead‐time step has the best performance and a multiple output form has the worst result. The most popular form with multiple inputs and single output has the average performance. In comparison with a linear transfer function (TF) model, it is found that ANN models are uncompetitive against the TF model in short‐range predictions and should not be used in operational flood forecasting owing to their complicated calibration process. For longer range predictions, ANN models have an improved chance to perform better than the TF model; however, this is highly dependent on the training data arrangement and there are undesirable uncertainties involved, as demonstrated by bootstrap analysis in the study. To tackle the uncertainty issue, two novel approaches are proposed: distance analysis and response analysis. Instead of discarding the training data after the model's calibration, the data should be retained as an integral part of the model during its prediction stage and the uncertainty for each prediction could be judged in real time by measuring the distances against the training data. The response analysis is based on an extension of the traditional unit hydrograph concept and has a very useful potential to reveal the hydrological characteristics of ANN models, hence improving user confidence in using them in real time. Copyright © 2006 John Wiley & Sons, Ltd. 相似文献
17.
Short‐term Quantitative Precipitation Forecasts (QPFs) can be achieved from numerical weather prediction (NWP) models or radar nowcasting, that is the extrapolation of the precipitation at a future time from consecutive radar scans. Hybrid forecasts obtained by merging rainfall forecasts from radar nowcasting and NWP models are potentially more skilful than either radar nowcasts or NWP rainfall forecasts alone. This paper provides an assessment of deterministic and probabilistic high‐resolution QPFs achieved by implementing the Short‐term Ensemble Prediction System developed by the UK Met Office. Both radar nowcasts and hybrid forecasts have been performed. The results show that the performance of both deterministic nowcasts and deterministic hybrid forecasts decreases with increasing rainfall intensity and spatial resolution. The results also show that the blending with the NWP forecasts improves the performance of the forecasting system. Probabilistic hybrid forecasts have been obtained through the modelling of a stochastic noise component to produce a number of equally likely ensemble members, and the comparative assessment of deterministic and probabilistic hybrid forecasts shows that the probabilistic forecasting system is characterised by a higher discrimination accuracy than the deterministic one. Copyright © 2011 John Wiley & Sons, Ltd. 相似文献
18.
A problem frequently met in engineering hydrology is the forecasting of hydrological variables conditional on their historical observations and the hindcasts and forecasts of a deterministic model. On the contrary, it is a common practice for climatologists to use the output of general circulation models (GCMs) for the prediction of climatic variables despite their inability to quantify the uncertainty of the predictions. Here we apply the well-established Bayesian processor of forecasts (BPF) for forecasting hydroclimatic variables using stochastic models through coupling them with GCMs. We extend the BPF to cases where long-term persistence appears, using the Hurst-Kolmogorov process (HKp, also known as fractional Gaussian noise) and we investigate its properties analytically. We apply the framework to calculate the distributions of the mean annual temperature and precipitation stochastic processes for the time period 2016–2100 in the United States of America conditional on historical observations and the respective output of GCMs. 相似文献
19.
Francesca Pianosi Andrea Castelletti Leonardo Mancusi Elisabetta Garofalo 《水文研究》2014,28(4):2524-2534
Despite human is an increasingly significant component of the hydrologic cycle in many river basins, most hydrologic models are still developed to accurately reproduce the natural processes and ignore the effect of human activities on the watershed response. This results in non‐stationary model forecast errors and poor predicting performance every time these models are used in non‐pristine watersheds. In the last decade, the representation of human activities in hydrological models has been extensively studied. However, mathematical models integrating the human and the natural dimension are not very common in hydrological applications and nearly unknown in the day‐to‐day practice. In this paper, we propose a new simple data‐driven flow forecast correction method that can be used to simultaneously tackle forecast errors from structural, parameter and input uncertainty, and errors that arise from neglecting human‐induced alterations in conceptual rainfall–runoff models. The correction system is composed of two layers: (i) a classification system that, based on the current flow condition, detects whether the source of error is natural or human induced and (ii) a set of error correction models that are alternatively activated, each tailored to the specific source of errors. As a case study, we consider the highly anthropized Aniene river basin in Italy, where a flow forecasting system is being established to support the operation of a hydropower dam. Results show that, even by using very basic methods, namely if‐then classification rules and linear correction models, the proposed methodology considerably improves the forecasting capability of the original hydrological model. Copyright © 2013 John Wiley & Sons, Ltd. 相似文献
20.
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. 相似文献