Using Periodic Copula to Assess the Relationship Between Two Meteorological Cyclostationary Time Series Datasets

In environmental, hydrological, and meteorological research, one of the main aims is to study the relationship between some variables. This issue has an influential role in various fields such as predicting stochastic variables, reconstructing missing data (especially in studies related to assessing the changes in climate conditions and drought characteristics), etc. For this purpose, statisticians have proposed different parametric and non-parametric techniques. Most of the proposed methods are applicable for stationary and some special non-stationary time series datasets. This work was devoted to introducing and applying a novel copula-based approach, called the periodic copula model (in 5 methods, including Gaussian, t, Clayton, Gumbel, and Frank periodic copula models) to study the relationship between some cyclostationary processes. For assessing the performance of the introduced model, two numerical studies, including the first-order periodic autoregressive (PAR (1)) and the first-order periodic moving average (PMA (1)) time series were considered. Moreover, the comparison of the relationship between observed and simulated drought severities (based on the 3month standardized precipitation evapotranspiration index (SPEI)) using the periodic copula was used. To comput SPEI, data series of 10 stations over Iran during 1967–2019 (5 groups, each group includes two stations with a short spatial distance) were used. The ability and performance of the method was evaluated based on three indices, including Willmott’s index (WI), Nash-Sutcliff’s coefficient (NSC), and correlation of coefficient (r). The results of numerical studies verify the ability of the proposed technique. In all five copula models studied in 6month (with T?=?2) and 3month (with T?=?4) periods with n equal to 100, 200, 500, and 1000, the r, NSE, and WI indices in the periodic form of data series were more than the non-periodic form. The results of testing the performance of the proposed model based on actual data also verified the greater ability of periodic copula models compared to non-periodic copula models. So that in all chosen groups and all periods, including winter, spring, summer, and autumn, the R-Square between observed drought indices and predicted data using the periodic copula models was more than the R-Square between observed drought indices and predicted data using the non-periodic copula models.

     

Copula函数在水文多变量分析计算中的问题

基于Copula函数的水文多变量分析是统计水文学新兴的研究领域,经过20多a的研究和实践,Copula函数已被应用于洪水、暴雨、干旱和枯水概率等多变量分析计算,研究成果丰富了Copula函数理论体系,拓宽了Copula函数的应用领域。根据国内外有关研究进展,从Copula函数的主要类型、Copula函数在水文应用中面临的几个问题等方面总结了实际计算研究面临的计算问题。在此基础上,提出了基于Copula函数水文多变量分析计算研究的建议:①非平稳多变量联合概率分布与设计值计算的实用化;②提高Copula函数的参数估计精度;③提高随机变量和、差、积、商分布计算的精度。     

R/S在径流时间序列周期分析中的应用

河川径流时间序列波动周期的确定较为困难。在R/S长程记忆性分析的基础上,将R/S分析方法引入到径流时间序列周期的分析中,其基本原理是时间序列的Hurst指数在出现不可逆转的减小趋势之前有一个相对稳定的最大值区间,据此可有效地分析时间序列的周期。乌江上游洪家渡、下游构皮滩水文站年径流时间序列周期分析表明:洪家渡站年径流序列具有15¹8 a的周期,构皮滩站年径流序列具有15 a左右的周期。     

A Comparative Evaluation of Short-Term Streamflow Forecasting Using Time Series Analysis and Rainfall-Runoff Models in eWater Source

Over the past few decades, many numerical streamflow prediction techniques using observed time series (TS) have been developed and widely used in water resources planning and management. Recent advances in quantitative rainfall forecasting by numerical weather prediction (NWP) models have made it possible to produce improved streamflow forecasts using continuous rainfall-runoff (RR) models. In the absence of a suitable integrated system of NWP, RR and river system models, river operators in Australia mostly use spreadsheet-based tools to forecast streamflow using gauged records. The eWater Cooperative Research Centre of Australia has recently developed a new generation software package called eWater Source, which allows a seamless integration of continuous RR and river system models for operational and planning purposes. This paper presents the outcomes of a study that was carried out using Source for a comparative evaluation of streamflow forecasting by several well-known TS based linear techniques and RR models in two selected sub-basins in the upper Murray river system of the Murray-Darling Basin in Australia. The results were compared with the actual forecasts made by the Murray River operators and the observed data. The results show that while streamflow forecasts by the river operators were reasonably accurate up to day 3 and traditional TS based approaches were reasonably accurate up to 2?days. Well calibrated RR models can provide better forecasts for longer periods when using high quality quantitative precipitation forecasts. The river operators tended to underestimate large magnitude flows.     

Improving Forecasting Accuracy of Streamflow Time Series Using Least Squares Support Vector Machine Coupled with Data-Preprocessing Techniques

Highly reliable forecasting of streamflow is essential in many water resources planning and management activities. Recently, least squares support vector machine (LSSVM) method has gained much attention in streamflow forecasting due to its ability to model complex non-linear relationships. However, LSSVM method belongs to black-box models, that is, this method is primarily based on measured data. In this paper, we attempt to improve the performance of LSSVM method from the aspect of data preprocessing by singular spectrum analysis (SSA) and discrete wavelet analysis (DWA). Kharjeguil and Ponel stations from Northern Iran are investigated with monthly streamflow data. The root mean square error (RMSE), mean absolute error (MAE), correlation coefficient (R) and coefficient of efficiency (CE) statistics are used as comparing criteria. The results indicate that both SSA and DWA can significantly improve the performance of forecasting model. However, DWA seems to be superior to SSA and able to estimate peak streamflow values more accurately. Thus, it can be recommended that LSSVM method coupled with DWA is more promising.     

基于EEMD的漳泽水库年径流周期分析

运用EEMD方法对漳泽水库水文站1956年~2008年的年径流量进行多时间尺度分解,并运用Hilbert变换对其进行周期分析。结果表明,漳泽水库的年径流序列可以分解为4个IMF分量和一个Res分量,并有效减小了模态混叠的程度;漳泽水库年径流序列可能存在2.42年,3.83年,7.09年,14.59年几个周期,并且可以推测其演化可能与大气低频振荡、厄尔尼诺现象、太阳黑子强弱变化及天体运动规律等有关。EEMD分解和Hilbert变换相结合的方法,在水文水资源系统领域具有广阔的应用前景。     

水文时间序列预报的人工神经网络模型

1　引言由于水文现象本身的复杂性 ,目前还很难用物理方法对水文现象进行描述 ,人们主要借助数理统计方法以及其它的一些不确定性方法来描述水文现象 ,以弥补物理方法的不足。不确定性方法主要运用概率统计、时间序列、模糊数学和灰色理论等来探索水文现象。但这些理论和方法都需要具有显式函数 ,而它们往往难以找到 ,或只能以近似表达式进行描述 ,一般都有比较大的误差 ,因此 ,寻找更合理与灵活的理论和方法来充实水文学的研究成了水文工作者多年来不懈的追求 ,人工神经网络途径便是一种尝试。近年来 ,人工神经网络技术已被广泛应用于水文…     

自回归模型在月径流过程概率预报中的应用

把自回归模型用于月径流过程概率预报中。首先根据历史径流的概率分布利用自回归模型预报出一个概率，再根据这个预报出的概率进行径流概率预报。并把这种方法用于三峡电站的入库径流预报中，取得了较好的效果。     

时序随机耦合模型在井灌水稻区地下水动态变化中的应用

针对目前黑龙江省井灌水稻面答逐年增长、地下水超采严重、地下水位持续下降、漏斗与“吊泵”现象时有发生等问题，以三江平原创业农场为例，运用随机过程，建立时序随机耦合模型模拟地下水动态变化，并预测未来变化趋势，对该地区今后的农业及地下水资源可持续发展提供参考。     

A Hybrid Model to Predict Monthly Streamflow Using Neighboring Rivers Annual Flows

Water Resources Management - The issue of predicting monthly streamflow data is an important issue in water resources engineering. In this paper, a hybrid model was proposed to generate monthly...     

Assessing Groundwater Contribution to Streamflow of a Large Alpine River with Heat Tracer Methods and Hydrological Modelling

The contribution of groundwater to streamflow in Alpine catchments is still poorly understood, despite the fact that it may heavily impact hydrological balance and stream habitats. This paper presents the results of a field campaign based on experiments with heat tracer methods to assess the hyporheic flow during the low‐flow period of a large Alpine river in Italy. These measurements were employed to validate a distributed hydrological model that can be used to asses river–groundwater interaction in both low‐flow and high‐flow conditions. The results show that groundwater may have a relevant role during low‐flow periods, by increasing river discharge and during floods, by subtracting direct run‐off that is stored in river banks. Copyright © 2015 John Wiley & Sons, Ltd.     

From Changing Environment to Changing Extremes: Exploring the Future Streamflow and Associated Uncertainties Through Integrated Modelling System

Water Resources Management - Climate and land-use changes can alter the dynamics of hydro-climatic extremes by modifying the flow regimes. Here, we have attempted to disentangle the relationship...     

人工神经网络非线性时序模型在水文预报中的应用

首先构造出人工神经网络非线性时序模型 ,然后用该模型进行单变量和多变量时间序列预报研究。为了与传统的随机水文模型对比 ,选择了自回归模型。以日流量序列为例 ,研究结果表明 ,人工神经网络非线性时序模型预报效果不错 ,可以在水文预报中加以应用     

Using Volume Delivery Time to Identify Independent Partial Series Events

In practice, the annual series of streamflow peaks is generally preferred than the partial series for flood frequency analysis. Flood selection criteria for the partial series tend to be arbitrary and are limited in making allowances for catchment scale. This aspect appears to be a constraint to greater acceptance of the partial series approach. The aim of this paper is to define a scalable selection criterion that reduces ambiguity in flood selection by defining floods that exceed the daily average. An approach based on the volume delivery time (VDT), analogous to the tip interval time in tipping bucket raingauges, is described and tested for three rural catchments of various sizes in South East Queensland, Australia. The VDT approach produced discharge quantile estimates similar to the partial series based on the commonly-used monthly maxima except for minor, high frequency discharges at the small, more perennial catchment. A simplified approach based on average daily volume gave similar results to the VDT method.     

Application of the Hybrid Artificial Neural Network Coupled with Rolling Mechanism and Grey Model Algorithms for Streamflow Forecasting Over Multiple Time Horizons

Water Resources Management - Streamflow forecasting is paramount process in water and flood management, determination of river water flow potentials, environmental flow analysis, agricultural...     

A Practical Guide to Discrete Wavelet Decomposition of Hydrologic Time Series

Discrete wavelet transform (DWT) is commonly used for wavelet threshold de-noising, wavelet decomposition, wavelet aided hydrologic series simulation and prediction, as well as many other hydrologic time series analyses. However, its effectiveness in practice is influenced by many key factors. In this paper the ??reference energy function?? was firstly established by operating Monte-Carlo simulation to diverse noise types; then, energy function of hydrologic series was compared with the reference energy function, and four key issues on discrete wavelet decomposition were studied and the methods for solving them were proposed, namely wavelet choice, decomposition level choice, wavelet threshold de-noising and significance testing of DWT, based on which a step-by-step guide to discrete wavelet decomposition of hydrologic series was provided finally. The specific guide is described as: choose appropriate wavelet from the recommended wavelets and according to the statistical characters relations among original series, de-noised series and removed noise; choose proper decomposition levels by analyzing the difference between energy function of the analyzed series and reference energy function; then, use the chosen wavelet and decomposition level, estimate threshold according to series?? complexity and set the same threshold under each level, and use the mid-thresholding rule to remove noise; finally, conduct significance testing of DWT by comparing energy function of the de-noised series with the reference energy function. Analyses of both synthetic and observed series indicated the better performance and easier operability of the proposed guide compared with those methods used presently. Following the guide step by step, noise and different deterministic components in hydrologic series can be accurately separated, and uncertainty can also be quantitatively estimated, thus the discrete wavelet decomposition result of series can be improved.     

Test of a Benthic Macroinvertebrate Habitat—Flow Time Series Model Incorporating Disturbance and Recovery Processes

We tested a model [Benthic Invertebrate Time series Habitat Simulation (BITHABSIM)] for simulating the effect of changes in flow on benthic macroinvertebrate habitat and relative abundance. The model calculates a habitat index (WUA2) based on weighted usable area (WUA) modified to account for reduction of invertebrate abundance by flood disturbance and drying followed by recolonization. The test involved a comparison of WUA and WUA2 with a 1‐year abundance time series of the common New Zealand mayfly Deleatidium spp. in a small gravel‐bed river with naturally variable flows. The fit of WUA and WUA2 to the Deleatidium spp. abundance time series was judged on correlation and regression analysis of the magnitudes and slopes of the mean‐standardized indices and abundance versus time. WUA fit the low flow part of the abundance time series fairly well, but not the portion after flood disturbance. Over the entire time series, WUA fit Deleatidium spp. abundance and rate of change poorly. WUA2 fit Deleatidium abundance better, but the correlation was not quite statistically significant at the 95% level. However, it did explain the essential temporal pattern. The fit of the slopes of standardized WUA2 to the slopes of standardized Deleatidium spp. abundance was significant (R² = 0.66), but with a systematic bias; high slopes were underestimated and low slopes overestimated. BITHABSIM adds biological realism to traditional hydraulic‐habitat modelling based on WUA and so improves the reliability of assessments of effects of flow change on benthic macroinvertebrates over the entire hydrograph. Parameter uncertainty and research needs to improve BITHABSIM and future process‐based models are discussed. Copyright © 2014 John Wiley & Sons, Ltd.     

高边坡时序位移滚动预测的SVM-Elman模型

基于支持向量机(SVM)和Elman神经网络,提出一种新的高边坡位移时序预测模型——SVM-Elman神经网络预测模型。在对实测数据学习的过程中,寻找最佳学习样本数和最佳测试样本数,利用经粒子群算法优化的SVM模型对边坡位移时间序列进行实时滚动预测;并运用Elman神经网络改进SVM的预测结果,得到SVM-Elman模型预测值,通过比较不同隐含层数的Elman神经网络对预测结果的影响,选择最佳隐含层数的SVM-Elman模型,实现对预测结果的改进。将SVM-Elman模型应用于某混凝土面板堆石坝左岸强卸荷岩体高边坡位移预测分析中,并与传统的SVM预测结果进行比较分析。结果表明,SVM-Elman模型在预测精度上有明显提高,预测结果科学可靠,在岩体高边坡时序位移预测中具有一定的工程应用价值。     

时序InSAR在南水北调中线形变监测中的应用

南水北调中线工程作为我国特大型线性输水建筑物,保障干渠的安全运营是推进受水区人民生活与经济发展水平的重要措施.利用改进的SBAS InSAR技术,对覆盖南水北调中线辉县段干渠自2015-04-11至2019-03-09的90景Sentinel-1A数据进行高精度处理,得到2015年4月至2019年3月期间辉县段及其周围...