Comparison of detrending methods for fluctuation analysis in hydrology

Zhang, Qiang; Zhou, Yu; Singh, Vijay P.; Chen, Yongqin David

doi:10.1016/j.jhydrol.2011.01.032

Cited by 48 publications

(40 citation statements)

References 34 publications

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“…Figure 7 highlights that the h(2) values follow two different statistical distributions for the two classes of time scales, and ARFIMA, gHK and IAAFT are able to reproduce this behavior, whereas HK models (with ML and DFA estimates of H) do no reproduce the crossover at all (since H ∈ (0, 1), we refer to Serinaldi [11] and references therein for the interpretation of DFA H values greater than unity). These results are in agreement with those reported by Kantelhardt et al [19] with differences mainly related to a different choice of the crossover time scale, and confirm that the crossover results from the interplay of SRD and LRD, rather than residual seasonality as is sometimes argued in the literature [65]. Compared with previous studies, the crossover attribution relies on the use of models that explicitly account for both SRD and LRD with different degrees of complexity, and allow the simultaneous reproduction of short and long term dynamics, thus further strengthening conclusions concerning the nature of the crossover.…”

Section: Climacogram Analysis and Dfa: Highlighting Srd And Lrdsupporting

confidence: 93%

“…These local trends are defined as local linear or polynomial regressions in DFA (a three-order polynomial is used in this study) [10,[59][60][61]. It should be noted that LRD diagnostics are commonly affected by the presence of seasonal/cyclic patterns [6,[62][63][64][65][66]; therefore, the deseasonalization procedure in Equation (1) provides a simple but effective way to handle this aspect.…”

Section: Methodsmentioning

confidence: 99%

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Understanding Persistence to Avoid Underestimation of Collective Flood Risk

Serinaldi

Kilsby

2016

Water

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Abstract:The assessment of collective risk for flood risk management requires a better understanding of the space-time characteristics of flood magnitude and occurrence. In particular, classic formulation of collective risk implies hypotheses concerning the independence of intensity and number of events over fixed time windows that are unlikely to be tenable in real-world hydroclimatic processes exhibiting persistence. In this study, we investigate the links between the serial correlation properties of 473 daily stream flow time series across the major river basins in Europe, and the characteristics of over-threshold events which are used as proxies for the estimation of collective risk. The aim is to understand if some key features of the daily stream flow data can be used to infer properties of extreme events making a more efficient and effective use of the available data. Using benchmark theoretical processes such as Hurst-Kolmogorov (HK), generalized HK (gHK), autoregressive fractionally integrated moving average (ARFIMA) models, and Fourier surrogate data preserving second order linear moments, our findings confirm and expand some results previously reported in the literature, namely: (1) the interplay between short range dependence (SRD) and long range dependence (LRD) can explain the majority of the serial dependence structure of deseasonalized data, but losing information on nonlinear dynamics; (2) the standardized return intervals between over-threshold values exhibit a sub-exponential Weibull-like distribution, implying a higher frequency of return intervals longer than expected under independence, and expected return intervals depending on the previous return intervals; this results in a tendency to observe short (long) inter-arrival times after short (long) inter-arrival times; (3) as the average intensity and the number of events over one-year time windows are not independent, years with larger events are also the more active in terms of number of events; and (4) persistence influences the distribution of the collective risk producing a spike of probability at zero, which describes the probability of years with no events, and a heavier upper tail, suggesting a probability of more extreme annual losses higher than expected under independence. These results provide new insights into the clustering of stream flow extremes, paving the way for more reliable simulation procedures of flood event sets to be used in flood risk management strategies.

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Section: Climacogram Analysis and Dfa: Highlighting Srd And Lrdsupporting

confidence: 93%

Section: Methodsmentioning

confidence: 99%

Understanding Persistence to Avoid Underestimation of Collective Flood Risk

Serinaldi

Kilsby

2016

Water

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“…Hu et al [39] showed that a periodic trend induces an artificial crossover at the scale of the sinusoidal periodicity. This crossover leads to difficulties in analyzing data in a sense that it makes the estimation of scaling exponent unreliable [41]. Therefore, to measure the scaling exponent correctly, we have to minimize the effect of such trends in the data before applying the MF-DFA.…”

Section: B Singular Value Decompositionmentioning

confidence: 99%

Multiscaling behavior of atomic-scale friction

et al. 2017

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The scaling behavior of friction between rough surfaces is a well-known phenomenon. It might be asked whether such a scaling feature also exists for friction at an atomic scale despite the absence of roughness on atomically flat surfaces. Indeed, other types of fluctuations, e.g., thermal and instrumental fluctuations, become appreciable at this length scale and can lead to scaling behavior of the measured atomic-scale friction. We investigate this using the lateral force exerted on the tip of an atomic force microscope (AFM) when the tip is dragged over the clean NaCl (001) surface in ultra-high vacuum at room temperature. Here the focus is on the fluctuations of the lateral force profile rather than its saw-tooth trend; we first eliminate the trend using the singular value decomposition technique and then explore the scaling behavior of the detrended data, which contains only fluctuations, using the multifractal detrended fluctuation analysis. The results demonstrate a scaling behavior for the friction data ranging from 0.2 to 2 nm with the Hurst exponent H = 0.61 ± 0.02 at a 1σ confidence interval. Moreover, the dependence of the generalized Hurst exponent, h(q), on the index variable q confirms the multifractal or multiscaling behavior of the nanofriction data. These results prove that fluctuation of nanofriction empirical data has a multifractal behavior which deviates from white noise.

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“…Detrended analysis can quantify the contributions of variables with significant trends. By removing significant increases or decreases and recalculating the reference ET with the detrended data set, several papers have analyzed the contributions of climatic variables to reference ET or actual ET [11,[30][31][32][33]. For example, Xu found that a decrease in net total radiation was the main cause of the decrease in reference ET in the Yangtze catchment from 1960 to 2000 [30].…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Analysis of Actual Evapotranspiration and Its Causes in the Hai Basin

Niu

Tian

et al. 2018

Remote Sensing

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Evapotranspiration (ET) is an important component of the eco-hydrological process. Comprehensive analyses of ET change at different spatial and temporal scales can enhance the understanding of hydrological processes and improve water resource management. In this study, monthly ET data and meteorological data from 57 meteorological stations between 2000 and 2014 were used to study the spatiotemporal changes in actual ET and the associated causes in the Hai Basin. A spatial analysis was performed in GIS to explore the spatial pattern of ET in the basin, while parametric t-test and nonparametric Mann-Kendall test methods were used to analyze the temporal characteristics of interannual and annual ET. The primary causes of the spatiotemporal variations were partly explained by detrended fluctuation analysis. The results were as follows: (i) generally, ET increased from northwest to southeast across the basin, with significant differences in ET due to the heterogeneous landscape. Notably, the ET of water bodies was highest, followed by those of paddy fields, forests, cropland, brush, grassland and settlement; (ii) from 2000 to 2014, annual ET exhibited an increasing trend of 3.7 mm per year across the basin, implying that the excessive utilization of water resources had not been alleviated and the water resource crisis worsened; (iii) changes in vegetation coverage, wind speed and air pressure were the major factors that influenced interannual ET trends. Temperature and NDVI largely explained the increases in ET in 2014 and can be used as indicators to evaluate annual ET and provide early warning for associated issues.

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Comparison of detrending methods for fluctuation analysis in hydrology

Cited by 48 publications

References 34 publications

Understanding Persistence to Avoid Underestimation of Collective Flood Risk

Understanding Persistence to Avoid Underestimation of Collective Flood Risk

Multiscaling behavior of atomic-scale friction

Spatiotemporal Analysis of Actual Evapotranspiration and Its Causes in the Hai Basin

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