Streamflow drought time series forecasting

Drought is considered to be an extreme climatic event causing significant damage both in the natural environment and in human lives. Due to the important role of drought forecasting in water resources planning and management and the stochastic behavior of drought, a multiplicative seasonal autoregressive integrated moving average (SARIMA) model is applied to the monthly streamflow forecasting of the Zayandehrud River in western Isfahan province, Iran. After forecasting 12 leading month streamflow, four drought thresholds including streamflow mean, monthly streamflow mean, 2-, 5-, 10- and 20-year return period monthly drought and standardized streamflow index were chosen. Both observed and forecasted streamflow showed a drought period with different severity in the lead-time. This study also demonstrates the usefulness of SARIMA models in forecasting, water resources planning and management.     

Nonlinear multivariate rainfall–stage model for large wetland systems

Wetland restoration is often measured by how close the spatial and temporal water level (stage) patterns are to the pre-drainage conditions. Driven by rainfall, such multivariate conditions are governed by nonstationary, nonlinear, and nonGaussian processes and are often simulated by physically based distributed models which are difficult to run in real time due to extensive data requirements. The objective of this study is to provide the wetland restorationists with a real time rainfall–stage modeling tool of simpler input structure and capability to recognize the wetland system complexity. A dynamic multivariate Nonlinear AutoRegressive network with eXogenous inputs (NARX) combined with Principal Component Analysis (PCA) was developed. An implementation procedure was proposed and an application to Florida Everglade’s wetland systems was presented. Inputs to the model are time lagged rainfall, evapotranspiration and previously simulated stages. Data locations, preliminary time lag selection, spatial and temporal nonstationarity are identified through exploratory data analysis. PCA was used to eliminate input variable interdependence and to reduce the problem dimensions by more than 90% while retaining more than 80% of the process variance. A structured approach to select optimal time lags and network parameters was provided. NARX model results were compared to those of the linear Multivariate AutoRegressive model with eXogenous inputs. While one step ahead prediction shows comparable results, recursive prediction by NARX is far more superior to that of the linear model. Also, NARX testing under drastically different climatic conditions from those used in the development demonstrates a very good and robust performance. Driven by net rainfall, NARX exhibited robust stage prediction with an overall Efficiency Coefficient of 88%, Mean Square Error less than 0.004 m², a standard error less than 0.06 m, a bias close to zero and normal probability plots show that the errors are close to normal distributions.     

布尔莎模型在GPS时间序列融合中的应用

利用“中国地壳运动观测网络”基准网与全球IGS站的公共站点,采用布尔莎模型求取坐标转换7参数,将地壳观测网络工程基准站点位时间序列与全球解进行绑定.结果显示,转换得到的时间序列N、E向中误差约为2、3mm,与直接融合得到的时间序列的单天误差水平相当,转换得到的站间大地线误差大部分在6mm内,表明该转换方法是可行的.     

An improved time series model for monthly stream flows

This study aims to develop an improved time series model to overcome difficulties in modeling monthly short term stream flows. The periodic, serial dependent and independent components of the classical time series models are improved separately by information transfer from a surrounding long term gauging station to the considered flow section having short term records. Eventually, an improved model preserving the mathematical model structure of the classical time series model, while improving general and monthly statistics of the monthly stream flows, is derived by using the improved components instead of the short term model components in the time series modeling. The correlative relationships between the current short term and surrounding long term stations are used to improve periodic and serial dependent behaviors of monthly flows. Independent components (residuals) are improved via the parameters defining their theoretical probability distribution. The improved model approach is tested by using 50 year records of Göksu-Himmetli (1801) and Göksu-Gökdere (1805) flow monitoring stations located on the Ceyhan river basin, in south of Turkey. After 50 year records of the station 1801 are separated into five 10 year sub series, their improved and classical time series models are computed and compared with the real long-term (50 year) time series model of this station to reveal efficiencies of the improved models for each subseries (sub terms with 10 year observation). The comparisons are realized based on the model components, model estimates and general/monthly statistics of model estimates. Finally, some evaluations are made on the results compared to the regression method classically applied in the literature.     

用EOF分析方法研究强震前中国大陆水平位移场时空分布特征

利用大气科学常用的经验正交函数(EOF)分解方法, 对中国大陆连续观测的基准站的水平位移每周解时间序列进行分析。 首先对时间序列中不连续的数据进行内插处理, 并通过线性拟合从时间序列中去掉长期滑动速率的影响。 结合震例研究强震前水平位移场在NS向和EW向的时间和空间的分布特征。     

Time series analysis for evaluating hydrological responses of pore-water pressure to rainfall in a slope

Characterization of pore-water pressure at the soil–atmosphere interface is a major requirement in relation to slope instability. A rain-gauge and five piezometers (BH1–BH5) were installed on a slope located in a Korean military base. The upper slope (BH4, BH5) was covered with plastic sheets to prevent rainwater from percolating into the slope due to safety issues. Rainfall is matched by prompt changes in the pore-water pressure except at BH5. Due to the plastic cover, the pore water does not show any significant change in the early period by evapotranspiration. From correlation analysis, two wells (BH3, BH5) have longer memory effects due to matrix flow of past precipitation. Two principal components show hydrological responses of pore water to rainfall during intense rainfall, but PC2 does not indicate any important changes in low or no rainfall. This study suggests that correlation analysis with PCA can be a valuable tool for interpreting datasets consisting only of pore-water pressure.     

Revisiting the Phasor-Walkout method for detailed investigation of harmonic signals in time series

A well-known but rarely used powerful method to investigate the presence of harmonic signals in time series is the Phasor Walkout method (other names are: Graphical Fourier Transform, Summation Dial, Complex Demodulation). At a given test frequency the complex contributions (phasors) to the Fourier Transform of each sample in an equidistantly sampled series are added vectorially in the complex plane. The resulting pattern, the walkout, reveals information about the properties of the signal which is not easy to obtain by other methods. Synthetic examples are used to demonstrate the resolving power of the method. The following geophysical examples for the application of this method are shown: determination of the frequency of the breathing mode₀ S ₀ of the earth after a large earthquake; the study of superconducting gravimeter records after a large deep earthquake used in the core mode interpretation of a spectral peak, the study of the residualS ₃ (8 h period) signal in a tidal record and the bichromatic Rayleigh-waves from Mount Pinatubo on June 15, 1991.     

Error analysis for the evaluation of model performance: rainfall–runoff event time series data

This paper provides a procedure for evaluating model performance where model predictions and observations are given as time series data. The procedure focuses on the analysis of error time series by graphing them, summarizing them, and predicting their variability through available information (recalibration). We analysed two rainfall–runoff events from the R‐5 data set, and evaluated 12 distinct model simulation scenarios for these events, of which 10 were conducted with the quasi‐physically‐based rainfall–runoff model (QPBRRM) and two with the integrated hydrology model (InHM). The QPBRRM simulation scenarios differ in their representation of saturated hydraulic conductivity. Two InHM simulation scenarios differ with respect to the inclusion of the roads at R‐5. The two models, QPBRRM and InHM, differ strongly in the complexity and number of processes included. For all model simulations we found that errors could be predicted fairly well to very well, based on model output, or based on smooth functions of lagged rainfall data. The errors remaining after recalibration are much more alike in terms of variability than those without recalibration. In this paper, recalibration is not meant to fix models, but merely as a diagnostic tool that exhibits the magnitude and direction of model errors and indicates whether these model errors are related to model inputs such as rainfall. Copyright © 2004 John Wiley & Sons, Ltd.     

A hidden Markov model segmentation procedure for hydrological and environmental time series

In this paper we present a procedure for the segmentation of hydrological and enviromental time series. We consider the segmentation problem from a purely computational point of view which involves the minimization of Huberts segmentation cost; in addition this least squares segmentation is equivalent to Maximum Likelihood segmentation. Our segmentation procedure maximizes Likelihood and minimizes Huberts least squares criterion using a hidden Markov model (HMM) segmentation algorithm. This algorithm is guaranteed to achieve a local maximum of the Likelihood. We evaluate the segmentation procedure with numerical experiments which involve artificial, temperature and river discharge time series. In all experiments, the procedure actually achieves the global minimum of the Likelihood; furthermore execution time is only a few seconds, even for time series with over a thousand terms.     

Statistical characteristics of detectable inhomogeneities in observed meteorological time series

Statistical characteristics of detectable inhomogeneities [IHs] in more than 600 observed meteorological time series have been investigated using 16 objective homogenisation methods. Forty and 100 year long series of monthly or annual characteristics of surface air temperature, precipitation total and relative air humidity from the Czech Republic and Hungary were examined. The area of the part of the Czech observing network used here is smaller, and the density of sites is larger, than in the Hungarian network, resulting in higher spatial correlations among data in the Czech dataset relative to the Hungarian dataset. Time series with low number of gaps were supplied with interpolated data. Before homogenisation relative time series were created, using weighted averages of time series from the same geographical region as reference series. For ease of comparison, the magnitudes of the detected IHs are normalised with the standard deviation of the noise in the relative time series. Results show that observed meteorological time series usually contain large number of small IHs, and that the magnitude distribution of IHs from different data segments are surprisingly similar. Effects of different spatial coherences on the results are discussed.     

基于AR模型模拟超高层建筑的脉动风速时程

风荷载是超高层建筑设计的主要荷载之一,而且通过风振时域分析可以更全面地了解超高层建筑风振响应特性,更直观地反映超高层建筑风致振动控制的有效性。因此,本文使用线性滤波法即白噪声滤波法(white noise filtration method,WNFM)中的自回归(auto-regressive,AR)模型模拟超高层建筑的风速时程。首先,考虑超高层建筑风速时程的时间和空间相关性,导出了四阶AR模型的参数表达式。接着,基于AR模型模拟了一幢高度为200 m超高层建筑的风速时程。最后,通过比较模拟风速功率谱、模拟自相关函数和互相关函数与目标风速功率谱、目标自相关函数和互相关函数的吻合程度,验证基于AR模型模拟超高层建筑风速时程的可行性。     

Use of time series analysis for the identification of tidal effect on groundwater in the coastal area of Kimje, Korea

The purpose of this study is to apply time series analysis to investigate whether the groundwater quality in the coastal area is affected by the tide. Continuous and regular in situ monitoring data of electrical conductivity (EC) and groundwater level, and tidal level data measured by the National Oceanographic Research Institute were used for the time series analysis. Through the time series analysis, it is known that EC and groundwater level conspicuously fluctuate with two periodicities (15.4 and 0.52-day), which is very similar to those of the tide. Also the behaviors of their fluctuations vary in accordance with the tidal period. These indicate that the groundwater quality has been mainly controlled by the tidal level, and the strength of tidal effect on the groundwater quality is different according to the tidal period.     

A two-stage time series model for monthly hydrological projections under climate change in the Lim River basin (southeast Europe)

ABSTRACT

Climate change projections of precipitation and temperature suggest that Serbia could be one of the most affected regions in southeastern Europe. To prepare adaptation measures, the impact of climate changes on water resources needs to be assessed. Pilot research is carried out for the Lim River basin, in southeastern Europe, to predict monthly flows under different climate scenarios. For estimation of future water availability, an alternative approach of developing a deterministic-stochastic time series model is chosen. The proposed two-stage time series model consists of several components: trend, long-term periodicity, seasonality and the stochastic component. The latter is based on a transfer function model with two input variables, precipitation and temperature, as climatic drivers. The Nash-Sutcliffe model efficiency for the observed period 1950–2012 is 0.829. The model is applied for the long-term hydrological prediction under the representative concentration pathway (RCP) emissions scenarios for the future time frame 2013–2070.     

Seasonal and spatial variability of water quality parameters in the Port of Genoa, Italy, from 2000 to 2007

This paper presents the results of the statistical analysis of a set of physico-chemical and biological water quality parameters, monthly collected from 2000 to 2007 in the Genoa Harbour area (Ligurian Sea). We applied multivariate methods, such as principal component analysis (PCA) and dynamic factor analysis (DFA) for investigating the spatial and temporal variability and for providing important background information on pollution problems in the region. PCA evidenced the role of the sewage and river discharges and of the exchanges with the open sea in determining the harbour water quality. DFA was used to estimate underlying common trends in the time series. The DFA results partly show a general improvement of water quality over the 8-years period. However, in other areas, we found inter-annual variations but no significant multi-annual trend. Furthermore, we included meteorological variables in our statistical analyses because of their potential influence on the water quality parameters. These natural forcings explain part of the variability in water quality parameters that are superimposed on the dominating anthropogenic pollution factors.     

Segmentation-based approach for trend analysis and structural breaks in rainfall time series (1851–2006) over India

ABSTRACT

This study analysed long-term rainfall data (1851–2006) over seven climatic zones of India at seasonal and annual scales based on three techniques: (i) linear regression, (ii) multifractal detrended fluctuation analysis (MFDFA) and (iii) Bayesian algorithm. The linear regression technique was used for trend analysis of short-term (30 years) and long-term (156 years) rainfall data. The MFDFA revealed small- and large-scale fluctuations, whereas the Bayesian algorithm helped in quantifying the uncertainty in break-point detection from the rainfall time series. Major break points years identified through Bayesian algorithm were 1888, 1904 and 1976. The MFDFA technique identified that high fluctuation years were between 1871–1890, 1891–1910 and 1951–1970. Linear regression-based analysis revealed 1881–1910 and 1971–2006 as break-point periods in the North Mountainous Indian region. A similar analysis was carried out for India as a whole, as well as its seven climatic zones.     

Analysis and interpretation of patterns within and between hydroclimatological time series in an alpine glacier basin

This paper explores patterns within and between climatological and hydrological time series from an alpine glacier basin. Time series recorded in the basin of the Haut Glacier d'Arolla over the 1989 ablation season are subdivided into five subperiods. Box-Jenkins ARIMA (AutoRegressive Integrated Moving Average) and TFN (Transfer Function-Noise) models are estimated for each of the five subperiods and differences between the models are interpreted in the context of changing glacier hydrology, particularly the changing nature and extent of the glacier drainage network.     

A review of continental scale hydrological models and their suitability for drought forecasting in (sub-Saharan) Africa

The aim of this review is to provide a basis for selecting a suitable hydrological model, or combination of models, for hydrological drought forecasting in Africa at different temporal and spatial scales; for example short and medium range (1–10 days or monthly) forecasts at medium to large river basin scales or seasonal forecasts at the Pan-African scale. Several global hydrological models are currently available with different levels of complexity and data requirements. However, most of these models are likely to fail to properly represent the water balance components that are particularly relevant in arid and semi-arid basins in sub-Saharan Africa. This review critically looks at weaknesses and strengths in the representation of different hydrological processes and fluxes of each model. The major criteria used for assessing the suitability of the models are (1) the representation of the processes that are most relevant for simulating drought conditions, such as interception, evaporation, surface water-groundwater interactions in wetland areas and flood plains and soil moisture dynamics; (2) the capability of the model to be downscaled from a continental scale to a large river basin scale model; and (3) the applicability of the model to be used operationally for drought early warning, given the data availability of the region. This review provides a framework for selecting models for hydrological drought forecasting, conditional on spatial scale, data availability and end-user forecast requirements. Among 16 well known hydrological and land surface models selected for this review, PCR-GLOBWB, GWAVA, HTESSEL, LISFLOOD and SWAT show higher potential and suitability for hydrological drought forecasting in Africa based on the criteria used in this evaluation.     

基于长时序T/P和Jason系列卫星高度计数据的中国南海潮汐信息提取研究

本文利用j、v模型对26年长时间序列的多个卫星高度计(TOPEX/Poseidon和Jason-1、Jason-2、Jason-3)变轨前后两种轨道的数据,使用调和分析法提取南海M₂、S₂、K₁和O₁分潮的调和常数.首次将Jason-3高度计数据用于潮汐信息提取研究,证明了Jason-3高度计与Jason-2高度计数据具有较好的一致性和连续性,且数据可用性强.提出并使用回归诊断法分析卫星轨道的交叉点处上升轨和下降轨之间调和分析结果的误差和相关系数,同时检验与评估潮汐信息提取方法的稳定性;选取南海47个验潮站,将验潮站实测数据与得到的卫星高度计数据提取的调和分析结果进行比较,进而检验潮汐提取结果的精度.最后,绘制同潮图验证提取潮汐调和常数的准确性.结果表明:M₂、S₂、K₁和O₁分潮的振幅均方根分别为4.28 cm、2.24 cm、2.92 cm、3.10 cm,迟角均方根分别为10.88°、11.10°、7.95...     

南极半岛地区GPS坐标时间序列噪声分析及形变模式初探

有色噪声广泛存在于各种连续GPS站坐标时间序列,对GPS时间序列分析有重要影响.利用GAMIT/GLOBK软件解算了南极半岛地区8个GPS测站2010—2014年的实测数据,对坐标时间序列使用主分量分析法(PCA)进行了空间滤波,利用CATS软件估计了不同噪声模型下和空间滤波前后的噪声量级、站坐标时间序列参数及其不确定度,最后对南极半岛地区水平和垂向的形变模式进行了分析和讨论.结果表明,南极半岛地区GPS时间序列不仅存在白噪声,还存在较大量级的闪烁噪声,部分测站E方向在滤波前可能存在随机游走噪声;空间滤波能够有效降低这三种噪声的量级,从而有效减小线性项和周期项估计的不确定度;南极半岛地区在水平方向主要表现为板块运动,还可能存在局部性构造运动;在垂直方向上由冰川均衡调整(Glacial Isostatic Adjustment, GIA)因素引起的抬升较小,主要表现为现今冰雪质量损失引起的弹性抬升运动.