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.     

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.     

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.     

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。     

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.     

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...     

Application of a wind-driven hydrodynamic model for the quantification of transport in an open boundary canal-lakes system in sw Friesland,The Netherlands

The south western lake district is a part of the boezem, a system of interconnected lakes and canals in the province of Friesland. The lake district has open boundaries with the other part of the boezem system. However, discharges in the boundary canals are unknown. These discharges are needed for modelling the phosphorus dynamics in the study area. Incidental water flow measurements gave a good indication of the complex water transport in the study area, but continuous water flow recording was not possible. Consequently, discharges could not be measured directly. In order to quantify the discharges, the water transport in the area was modelled by the application of a detailed wind-driven hydrodynamic model. In the model hourly mean values of wind data and water levels at the boundary locations were used as forcing functions. Model tuning was done by comparing observed and computed water levels of three stations within the system. This approach is new in surface water systems in The Netherlands. Therefore, a sensitivity analysis was done and it was verified whether the model results were reliable.The sensitivity analysis showed that the sensitivity was low for modifications of the wind exponent value and rather high for the bottom roughness coefficient. Simulations with daily or weekly mean wind and water level data resulted in an undesirable loss of detail. The sensitivity for noise at the imposed water levels at the boundary locations was moderate. The calculated discharges were used as forcing functions for a chloride mass balance model. Calculated chloride concentrations coincided with measured concentrations in three lakes, during three periods. From this it was concluded that the discharges were reliable. The simulations also lead to the quantification of the water balance and water residence times in the lakes.