Multiscale evolution of persistence of rainfall and streamflow

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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“…A recent study has applied a DFA analysis for studying the long-term persistence of rainfall and streamflow in India and USA [33].…”

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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“…Despite this, our study finds that the climatic differences (low rainfall being received in WR subdivision to high rainfall in regions like Kerala) are not found to have any dominant influence on the multifractal properties of drought, which may be because the original structure of the daily precipitation series might have been distorted by the aggregation operation in the estimation of drought indices. Ascertaining the role of persistence is vital to the development of hydrological models and the changes in the hydrological persistence over the time spell are to be investigated (Dey and Mujumdar, ). These may influence the inter‐arrival times of extreme and rare events and also help in assessing the underlying mechanisms of their return periods (Koscielny‐Bunde et al , ).…”

Section: Resultsmentioning

confidence: 99%

Multifractal characterization of meteorological drought in India using detrended fluctuation analysis

Adarsh¹,

Kumar

Deepthi³

et al. 2019

Intl Journal of Climatology

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This study presents multifractal detrended fluctuation analysis (MF-DFA) to describe the multifractality of Standardized Precipitation Index (SPI) series from 30 meteorological subdivisions of India estimated at different aggregation timescales (3, 6 and 12 months) based on long-term monthly rainfall data sets of 1871-2016 period. The plots of fluctuation function and generalized Hurst exponents confirmed that multifractality is evident in most of the SPI series; however, it is found that its strength and persistency differ with timescale and space. The Hurst exponents of long-term (SPI-12) and medium-term (SPI-6) drought series of all subdivisions showed long-term persistence irrespective of the climatic conditions of the region, while the short-term droughts (SPI-3) of six subdivisions displayed short-term persistence. Further, the analysis by partitioning the data to four time spells

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“…Hidrologia (Dey & Mujumdar, 2018) e Finanças (Podobnik et al, 2009). A implementação do algoritmo DCCA é descrita da seguinte forma (Podobnik & Stanley, 2008):…”

Section: Detrended Cross-correlation Analysis (Dcca)unclassified

Correlações em séries temporais de preços de frango, soja e milho

Pessoa

Barreto

Araújo

et al. 2021

RSD

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A evolução do mercado agrícola brasileiro alterou o processo de produção, exportação e consumo de commodities alimentares. Com isso, novos estudos acerca da relação entre o mercado de alimentos e outros mercados foram desenvolvidos, buscando explicar a ligação entre os preços de commodities agrícolas e não agrícolas. Visando contribuir para esse estudo, foram investigadas as correlações de longo prazo entre os preços de commodities agrícolas brasileiras, utilizando técnicas de Econofísica. Analisaram-se então as séries diárias de preços e retorno de preços da carne de frango, soja e milho, registrados entre 02/08/2004 e 16/06/2017 pelo Centro de Estudos Avançados em Economia Aplicada / Escola Superior de Agricultura Luiz de Queiroz / Universidade de São Paulo - CEPEA/ESALQ/USP. As correlações entre as séries temporais foram investigadas utilizando os métodos Detrended Fluctuation Analysis (DFA) e Detrended Cross Correlation Analysis (DCCA), para calcular o Detrended Cross Correlation Coefficient (DCCA Coefficient), que serve para quantificar correlações de longo prazo entre séries temporais não estacionárias. Os resultados obtidos apontam para a ausência de correlações nas escalas de até 30 dias e, para escalas maiores, acusam correlações mais fortes entre os preços de frango e milho que entre os preços de frango e soja. Após a crise alimentar de 2008, entretanto, as correlações entre as séries diárias de retorno de preços do frango e do milho diminuíram, enquanto que, entre as de frango e soja, aumentaram nas escalas menores e diminuíram nas escalas maiores.

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Multiscale evolution of persistence of rainfall and streamflow

Cited by 26 publications

References 32 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

Multifractal characterization of meteorological drought in India using detrended fluctuation analysis

Correlações em séries temporais de preços de frango, soja e milho

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