Exploring the physical interpretation of long-term memory in hydrology

Habib, Abrar

doi:10.1007/s00477-020-01883-0

Cited by 10 publications

(3 citation statements)

References 36 publications

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“…The contour maps of the Hurst exponent for the studied GWL time series are shown in Figure 8. Thus, by comparing the maps of the spatial distribution of the GWL depth (Figure 5) and the Hurst exponent (Figure 8), we can roughly determine that, for the eastern and The higher the Hurst exponent value, the stronger the memory, the longer it will remember previous values, and the more consistent its ability to maintain the previous change trend [30,46]. Moreover, an increase in the sliding Hurst exponent value specifies that the time series can remember the later GWL depth trends better.…”

Section: Hurst Exponent Spatial and Temporal Distribution And Groundw...mentioning

confidence: 90%

Spatiotemporal Variation and Long-Range Correlation of Groundwater Levels in Odessa, Ukraine

Melkonyan,

Sugathan

2023

Water

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Increasing groundwater levels (GWLs) may become one of the most serious issues for the city of Odessa, Ukraine. This study investigated the spatial distribution characteristics and multifractal scaling behaviour of the groundwater-level/-depth fluctuations for a Quaternary aquifer in the city of Odessa using a geostatistical approach and multifractal detrended fluctuation analysis (MF-DFA). These two methods were applied to monthly GWL fluctuation time series from 1970 to 2020 to monitor 72 hydrogeological wells situated in different parts of the city of Odessa. The spatial distribution of the GWLs revealed an overall trend of decline and recovery from 1970 to 2020 in the study area, except for most of the southern region, where a persistent recovery of the groundwater depth was observed. The MF-DFA results suggest that the dynamics of the GWL fluctuations have multifractal characteristics in the Odessa area. In addition, both long-range correlations and fat-tail probability distribution contribute to the multifractality. However, long-range correlations among the fluctuations made a major contribution to the observed multifractality of the GWL fluctuation time series. The generalised Hurst exponents show a wide range of change (0.20 < h(q) < 2.85), indicating the sensitivity of the GWL fluctuations to changes in small-scale factors and large-scale factors. Regarding the long-range correlations of the GWL depths, the Hurst exponents (q = 2) demonstrated the positive persistence of groundwater-depth recovery in the southern region and the persistence of groundwater-depth variation in the other regions of the study area. The dynamic changes in the GWL depths in the Odessa area may be influenced by both natural and anthropogenic factors.

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Section: Hurst Exponent Spatial and Temporal Distribution And Groundw...mentioning

confidence: 90%

Spatiotemporal Variation and Long-Range Correlation of Groundwater Levels in Odessa, Ukraine

Melkonyan,

Sugathan

2023

Water

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“…From Water Supply Vol 22 No 9, 7285 continued to the module (day n) formed by d n on the last day of drought. This characteristic of drought is actually the wellknown Hurst phenomenon (Habib 2020). It reflects the result of a long series of interrelated events.…”

Section: Process Description Of Drought Eventsmentioning

confidence: 93%

The dynamic identification mechanism research of drought based on process description

Xie

Yang

et al. 2022

Water Supply

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Drought is one of the most common natural disasters, which can cause heavy losses on a global scale. Strengthening the research on drought identification mechanism has an important guiding role in drought disaster prevention and mitigation. The current drought identification is mostly static in the process of drought, and there is a problem that the historical evaluation information and the drought prediction information are not closely combined, with limited application value. Based on the physical cumulative and recessional effects of drought events, this paper further uses the Standardized Precipitation Index (SPI) to divide the whole drought process into four stages: accumulation → outbreak → reaction → restoration. In addition, this study proposes a new dynamic identification mechanism based on drought process, and develops a drought prediction model combining singular spectrum analysis and BP neural network (SSA-BPNN), filling the gap between scientific research and practical application. Using three drought events in the Yulin region of China as examples for simulation studies, the results show that the use of the new mechanism can not only improve the application value of the SSA-BPNN model, but also effectively advance the drought preparation time and resistance level.

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“…The larger the Hurst exponent, the stronger the memory, the longer it will remember previous values, and the more consistent its ability to maintain the previous change trend [30,46]. Moreover, an increase in the sliding Hurst exponent indicates that the time series can remember the later GWL depth trends better.…”

Section: Hurst Exponent Spatial and Temporal Distribution And Groundw...mentioning

confidence: 99%

Spatiotemporal Variation and Long-Range Correlation of Groundwater Level in Odessa, Ukraine

Melkonyan,

Sugathan

2023

Preprint

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Increasing groundwater levels (GWL) may become one of the most serious issues for Odessa City, Ukraine. This study investigated the spatial distribution characteristics and multifractal scaling behaviour of groundwater level/depth fluctuation for a Quaternary aquifer in Odessa City using a geostatistical approach and a Multifractal Detrended Fluctuation Analysis (MF-DFA). These two methods were applied to monthly GWL fluctuation time series from 1970 to 2020 to monitor 72 hydrogeological wells situated in different parts of Odessa City. The spatial distribution of GWL revealed an overall trend of decline and recovery from 1970 to 2020 in the study area, except for most of the southern region, where a persistent recovery of groundwater depth was observed. The MF-DFA results suggest that the dynamics of GWL fluctuations have multifractal characteristics in the Odessa City area. In addition, both long-range correlation and fat-tail probability distribution contribute to multifractality. However, long-range correlations among fluctuations made a major contribution to the observed multifractality of the GWL fluctuations time series. The generalized Hurst exponent shows a wide range of change (0.20 &lt; h(q) &lt; 2.85), indicating the sensitivity of GWL fluctuations to changes in small scale factors and large-scale factors. Regarding the long range correlations of GWL depth, the Hurst exponents (q = 2) demonstrated the positive persistence of groundwater depth recovery in the southern region and the persistence of groundwater depth variation in the other regions of the study area. The dynamic changes in GWL depth in the Odessa City area may be affected by both natural structural and anthropogenic factors.

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Exploring the physical interpretation of long-term memory in hydrology

Cited by 10 publications

References 36 publications

Spatiotemporal Variation and Long-Range Correlation of Groundwater Levels in Odessa, Ukraine

Spatiotemporal Variation and Long-Range Correlation of Groundwater Levels in Odessa, Ukraine

The dynamic identification mechanism research of drought based on process description

Spatiotemporal Variation and Long-Range Correlation of Groundwater Level in Odessa, Ukraine

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