Process-dependent persistence in precipitation records

Yang, Lichao; Fu, Zuntao

doi:10.1016/j.physa.2019.121459

Cited by 16 publications

(19 citation statements)

References 42 publications

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“…The DFA algorithm has been recently proposed to analyze long-term persistence in time series in a number of applications [32]. It was also used in the long-term analysis of Belesar water level for hydropower generation.…”

Section: Detrended Fluctuation Analysis For Long-term Persistence Evamentioning

confidence: 99%

Analysis and Prediction of Dammed Water Level in a Hydropower Reservoir Using Machine Learning and Persistence-Based Techniques

Castillo-Botón

Casillas-Pérez

Casanova-Mateo

et al. 2020

Water

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This paper presents long- and short-term analyses and predictions of dammed water level in a hydropower reservoir. The long-term analysis was carried out by using techniques such as detrended fluctuation analysis, auto-regressive models, and persistence-based algorithms. On the other hand, the short-term analysis of the dammed water level in the hydropower reservoir was modeled as a prediction problem, where machine learning regression techniques were studied. A set of models, including different types of neural networks, Support Vector regression, or Gaussian processes was tested. Real data from a hydropower reservoir located in Galicia, Spain, qwew considered, together with predictive variables from upstream measuring stations. We show that the techniques presented in this paper offer an excellent tool for the long- and short-term analysis and prediction of dammed water level in reservoirs for hydropower purposes, especially important for the management of water resources in areas with hydrology stress, such as Spain.

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Section: Detrended Fluctuation Analysis For Long-term Persistence Evamentioning

confidence: 99%

Analysis and Prediction of Dammed Water Level in a Hydropower Reservoir Using Machine Learning and Persistence-Based Techniques

Castillo-Botón

Casillas-Pérez

Casanova-Mateo

et al. 2020

Water

View full text Add to dashboard Cite

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“…1) The periodic annual cycle of the time series is first removed, following the procedure explained in detail in [22]. The process consists on standardizing the input time series x[n] of length N as follows:…”

Section: Detrended Fluctuation Analysis (Dfa)mentioning

confidence: 99%

“…The main conclusion raised in the study is that instrumental records are too short to be used for inferring long-term properties of the rainfall variability, however, proxy data usually lead to the underestimation of the persistence structure regardless of proxy type, or to overestimation due to low resolution of the time series. In [22] a DFA analysis of rainfall time series over USA is carried out. The analysis showed a clear two-ranges structure in the log-log DFA plot, with characteristic time around 160hours.…”

Section: Long-term Persistence In Atmospheric and Climatic Processesmentioning

confidence: 99%

“…are intrinsically persistent. It has recently been also applied to other systems in economy [15,16], complex networks [17,18], renewable energy [19,20], hydrology [21,22], systems in non-equilibrium thermodynamics [23], optimization [24], Machine Learning (ML) [11], geophysics [25,26], engineering optimization [24], or biomedical applications [27].…”

Section: Introductionmentioning

confidence: 99%

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Persistence in complex systems

et al. 2022

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“…At least it seems that the persistence of the precipitation is scale dependent. For example, Yang and Fu [2019] studied hourly-based precipitation and found that there is a crossover at the timescale of 200 hours such that scaling exponent is about 0.74 below this timescale and about 0.54 above this timescale. Markonis and Koutsoyiannis [2016] examined all available precipitation records over Europe with length above 200 years, as well as the CRU gridded data and found a mean for α coefficient close to 0.6, suggesting very weak long-term persistence for their annually averaged data.…”

Section: Introductionmentioning

confidence: 99%

Persistence of temperature and precipitation: From local to global anomalies

Takalo

2021

Annals of Geophysics

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ract: Using detrended fluctuation analysis (DFA) we find that the all continents are persistent in temperature. The scaling exponents of the southern hemisphere (SH) continents, i.e., South America (α=0.78) and Oceania (0.75) are somewhat higher than scaling exponents of Europe (0.70), Asia (0.69) and North America (0.65), but the scaling of Africa is by far the highest (0.89). The scaling exponents of the precipitation are much smaller, i.e., between 0.54 (Europe) and 0.67 (North America). The scaling exponent of Europe is near the exponent of random Brownian noise, which is 0.5. The other continents are slightly persistent in precipitation. The slopes of the logarithmic power spectra of the continents are in line with the scaling exponents confirming the DFA analysis results. We also show that the persistence is real and not the intrinsic property of the data itself. We find that scaling exponent α, i.e., persistence of the monthly temperature increases when going from local to larger area averages, at least in the case where climate type does not change much. The situation is not so obvious for the monthly precipitation. We also show that the three southernmost climate regions of USA, Ohio Valley, South and Southeast, have smaller scaling exponents for temperature anomalies, and are thus less persistent in temperature than other six climate regions. On the other hand they are slightly more persistent in precipitation that the other regions. The scaling exponents of the monthly temperature anomalies of the latitude zones of land area are 0.71 and 0.68 for the southern zones S44-S24, and S64-S44, respectively. For the northern zones N24-N44, N44-N64 and N64-N90, the scaling exponents are between 0.67-0.70. Interestingly the scaling exponents are extremely high for the equatorial zones S24-EQ and EQ-N24, i. e., 1.02 and 0.95, respectively. The corresponding exponents of the monthly precipitation anomalies are between 0.55-0.61 for other zones than S24-EQ and EQ-N24, which have exponents 0.72 and 0.77, respectively.

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Process-dependent persistence in precipitation records

Cited by 16 publications

References 42 publications

Analysis and Prediction of Dammed Water Level in a Hydropower Reservoir Using Machine Learning and Persistence-Based Techniques

Analysis and Prediction of Dammed Water Level in a Hydropower Reservoir Using Machine Learning and Persistence-Based Techniques

Persistence in complex systems

Persistence of temperature and precipitation: From local to global anomalies

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