A multifractal cross-correlation investigation into sensitivity and dependence of meteorological and hydrological droughts on precipitation and temperature

Rahmani, Farhang; Fattahi, Mohammad Hadi

doi:10.1007/s11069-021-04916-1

Cited by 31 publications

(12 citation statements)

References 64 publications

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“…(2021); Legates and Outcalt (2022); Pal et al. (2020); Rahmani and Fattahi (2021, 2022c, 2022a, 2022b). The hypothesis of long‐term persistence (LTP) in annual precipitation has been explored in a number of studies of point and grid scale data.…”

Section: Introductionmentioning

confidence: 99%

“…The work of Hurst has been used to characterize LTP across multiple disciplines, ranging from climate science to the analysis of internet traffic and the flow of blood in human arteries (O'Connell et al, 2016). Recent research on Hurst behavior in the climate and hydrology fields is reported by Adarsh et al (2020); Adarsh and Priya (2021); Benavides-Bravo et al (2021); ; ; Legates and Outcalt (2022); Pal et al (2020); Rahmani and Fattahi (2021, 2022c, 2022a, 2022b. The hypothesis of long-term persistence (LTP) in annual precipitation has been explored in a number of studies of point and grid scale data.…”

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confidence: 99%

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On the Spatial Scale Dependence of Long‐Term Persistence in Global Annual Precipitation Data and the Hurst Phenomenon

O’Connell

O’Donnell

Koutsoyiannis

2023

Water Resources Research

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Long-term persistence (LTP) in geophysical time series, or the tendency of above or below average runs of years to be unusually long, was first quantified by Hurst (Hurst, 1951(Hurst, , 1956 using a coefficient H which characterizes LTP in the range 0.5 < H < 1, with H = 0.5 corresponding to the independent (white noise) case, that is, no persistence. Hurst analyzed a wide set of geophysical times series and found an average value of H = 0.73, with a standard deviation of 0.09. In particular, he reported a value of H = 0.9 for annual river Nile flows at Aswan, reflecting strong LTP. The disparity between these results, and the then current theory that predicted H = 0.5 based on the increments of Brownian motion, has come to be known as the Hurst Phenomenon. Over the years, a number of stochastic approaches to modeling LTP have emerged (e.g., fractional Gaussian noise (Mandelbrot & Wallis, 1968, 1969, ARMA models (O'Connell, 1974a(O'Connell, , 1974b, shifting mean models (Boes & Salas, 1978) and fractionally differenced models (Hosking, 1984)). Koutsoyiannis (2011a) has shown that those models exhibiting Hurst behavior asymptotically can be encapsulated within a Hurst -Kolmogorov (HK) stochastic dynamics framework characterized by a simple scaling law, acknowledging the contribution

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%

On the Spatial Scale Dependence of Long‐Term Persistence in Global Annual Precipitation Data and the Hurst Phenomenon

O’Connell

O’Donnell

Koutsoyiannis

2023

Water Resources Research

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“…A multi-headed encoder with gate mixing sub-encoders (Transformer and Wavelet Crossformer) can give the decoder a more concise signature of trending signals and improve the model’s predictive capabilities. Multifractal cross-correlation analysis has been used successfully in a range of studies involving time series pattern investigations, including economic trends 59 , 60 and climate 61 . This work also includes the multifractal analysis of cyclical patterns across multifractal regimes between pairs of time-series (stations) through the Multifractal Detrended Cross-Correlation Heatmaps (MF-DCCHM) 62 with Daubechies 4 Wavelets for high-frequency filtering.…”

Section: Introductionmentioning

confidence: 99%

Wavelet gated multiformer for groundwater time series forecasting

Rodrigues

Junior

Marinho

et al. 2023

Sci Rep

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Developing accurate models for groundwater control is paramount for planning and managing life-sustaining resources (water) from aquifer reservoirs. Significant progress has been made toward designing and employing deep-forecasting models to tackle the challenge of multivariate time-series forecasting. However, most models were initially taught only to optimize natural language processing and computer vision tasks. We propose the Wavelet Gated Multiformer, which combines the strength of a vanilla Transformer with the Wavelet Crossformer that employs inner wavelet cross-correlation blocks. The self-attention mechanism (Transformer) computes the relationship between inner time-series points, while the cross-correlation finds trending periodicity patterns. The multi-headed encoder is channeled through a mixing gate (linear combination) of sub-encoders (Transformer and Wavelet Crossformer) that output trending signatures to the decoder. This process improved the model’s predictive capabilities, reducing Mean Absolute Error by 31.26 % compared to the second-best performing transformer-like models evaluated. We have also used the Multifractal Detrended Cross-Correlation Heatmaps (MF-DCCHM) to extract cyclical trends from pairs of stations across multifractal regimes by denoising the pair of signals with Daubechies wavelets. Our dataset was obtained from a network of eight wells for groundwater monitoring in Brazilian aquifers, six rainfall stations, eleven river flow stations, and three weather stations with atmospheric pressure, temperature, and humidity sensors.

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“…SPEI input data include precipitation and evapotranspiration, which can fully reflect the effect of temperature changes on drought. Temperature also plays a crucial role in the occurrence and evolution of drought in the context of climate warming (Rahmani & Fattahi 2021), and therefore the SPEI indicator is more suitable for current drought assessment.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal evolution analysis and prediction of drought in Henan Province based on standardized precipitation evapotranspiration index

Luo

2022

Water Supply

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Drought is one of the major natural disasters in the world, and it is important to understand and grasp the spatiotemporal evolution characteristics of drought for drought monitoring and prevention. In this study, the standardized precipitation evapotranspiration index (SPEI) was calculated based on monthly precipitation and average temperature data from 15 meteorological stations in Henan Province, China during the period 1987–2020. The spatiotemporal evolution characteristics of droughts in Henan Province were analyzed using run theory and Moran's I, and drought prediction was carried out using grey disaster theory. The results show that: (1) During the period 1987–2020, the overall drought in Henan Province shows a slow intensification trend, and SPEI-12 has a certain adaptability in the drought characterization of Henan Province; (2) The frequency of droughts in the northern and eastern regions of Henan Province is high, the southern region is prone to continuous drought, the drought ephemeris and intensity show a strong positive correlation, and the spatial spillover effect of autumn drought is the most obvious; (3) The grey disaster prediction model has excellent accuracy and predicts that the next possible drought in Henan Province will be in the east of Henan Province in the spring of 2023.

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A multifractal cross-correlation investigation into sensitivity and dependence of meteorological and hydrological droughts on precipitation and temperature

Cited by 31 publications

References 64 publications

On the Spatial Scale Dependence of Long‐Term Persistence in Global Annual Precipitation Data and the Hurst Phenomenon

On the Spatial Scale Dependence of Long‐Term Persistence in Global Annual Precipitation Data and the Hurst Phenomenon

Wavelet gated multiformer for groundwater time series forecasting

Spatiotemporal evolution analysis and prediction of drought in Henan Province based on standardized precipitation evapotranspiration index

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