Temporal scaling phenomena in groundwater-floodplain systems using robust detrended fluctuation analysis

Habib, Abrar; Sorensen, James; Bloomfield, John P.; Muchan, Katie; Newell, Andrew J.; Butler, Adrian P.

doi:10.1016/j.jhydrol.2017.04.034

Cited by 29 publications

(14 citation statements)

References 52 publications

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“…Watts et al (2015) noted that although baseline groundwater temperatures are poorly understood, groundwater contributes much of the summer flow in some rivers, directly influencing water temperature. Using sub-hourly air, river and groundwater temperature data for a site where River Terrace Gravels are in hydraulic connection with the Thames at Wallingford, Habib et al (2017) showed a lag in temperature between groundwater in the gravel aquifer and the air and river temperature and a seasonal contrast between relatively warm winter groundwater and cooler summer groundwater compared with air and river temperatures ( Figure 14). At a range of sites across the Basin the thermal effects of upwelling groundwater in Chalk subcatchments has been recorded, however these effects are typically highly localised, e.g.…”

Section: Water Temperaturementioning

confidence: 99%

A conceptual model for the analysis of multi-stressors in linked groundwater–surface water systems

Kaandorp

Molina‐Navarro

Andersen

et al. 2018

Science of The Total Environment

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Groundwater and surface water are often closely coupled and are both under the influence of multiple stressors. Stressed groundwater systems may lead to a poor ecological status of surface waters but to date no conceptual framework to analyse linked multi-stressed groundwater - surface water systems has been developed. In this paper, a framework is proposed showing the effect of groundwater on surface waters in multiple stressed systems. This framework will be illustrated by applying it to four European catchments, the Odense, Denmark, the Regge and Dinkel, Netherlands, and the Thames, UK, and by assessing its utility in analysing the propagation or buffering of multi-stressors through groundwater to surface waters in these catchments. It is shown that groundwater affects surface water flow, nutrients and temperature, and can both propagate stressors towards surface waters and buffer the effect of stressors in space and time. The effect of groundwater on drivers and states depends on catchment characteristics, stressor combinations, scale and management practises. The proposed framework shows how groundwater in lowland catchments acts as a bridge between stressors and their effects within surface waters. It shows water managers how their management areas might be influenced by groundwater, and helps them to include this important, but often overlooked part of the water cycle in their basin management plans. The analysis of the study catchments also revealed a lack of data on the temperature of both groundwater and surface water, while it is an important parameter considering future climate warming.

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Section: Water Temperaturementioning

confidence: 99%

A conceptual model for the analysis of multi-stressors in linked groundwater–surface water systems

Kaandorp

Molina‐Navarro

Andersen

et al. 2018

Science of The Total Environment

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“…It has since been improved and used in many signal processing applications [28][29][30][31][32]. This study used the method proposed by Habib et al [45] to calculate a time-scale local Hurst exponent using the following four equations:…”

Section: Hurst Exponentmentioning

confidence: 99%

Statistical Analysis of Extreme Events in Precipitation, Stream Discharge, and Groundwater Head Fluctuation: Distribution, Memory, and Correlation

Dawley

Zhang

Liu

et al. 2019

Water

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Hydrological extremes in the water cycle can significantly affect surface water engineering design, and represents the high-impact response of surface water and groundwater systems to climate change. Statistical analysis of these extreme events provides a convenient way to interpret the nature of, and interaction between, components of the water cycle. This study applies three probability density functions (PDFs), Gumbel, stable, and stretched Gaussian distributions, to capture the distribution of extremes and the full-time series of storm properties (storm duration, intensity, total precipitation, and inter-storm period), stream discharge, lake stage, and groundwater head values observed in the Lake Tuscaloosa watershed, Alabama, USA. To quantify the potentially non-stationary statistics of hydrological extremes, the time-scale local Hurst exponent (TSLHE) was also calculated for the time series data recording both the surface and subsurface hydrological processes. First, results showed that storm duration was most closely related to groundwater recharge compared to the other storm properties, while intensity also had a close relationship with recharge. These relationships were likely due to the effects of oversaturation and overland flow in extreme total precipitation storms. Second, the surface water and groundwater series were persistent according to the TSLHE values, because they were relatively slow evolving systems, while storm properties were anti-persistent since they were rapidly evolving in time. Third, the stretched Gaussian distribution was the most effective PDF to capture the distribution of surface and subsurface hydrological extremes, since this distribution can capture the broad transition from a Gaussian distribution to a power-law one.process-based models requiring intensive data that are typically not available for many study sites, statistical analysis of hydrological extremes is an attractive tool to interpret these extreme events within and across systems from simple measurements [7-10].There are two major challenges when applying basic statistics to analyze hydrologic extremes. First, hydrologic processes in the water cycle are interconnected, while basic statistical analysis of extremes often does not consider multiple systems and tends to oversimplify the complexity of the correlated processes like precipitation and groundwater table fluctuations [11,12]. Understanding how the subtle properties of one system's extreme events can affect the other interconnected systems requires more in-depth analysis and use of advanced statistical techniques. Second, these basic statistical techniques often rely on assumptions or major simplifications of properties for water systems, such as stationarity in both space and time, which may not always be valid for real world dynamics [13][14][15]. These issues motivated this study.One example of the assumption/simplification used by basic statistical studies in hydrological extremes is the well-known Gumbel distribution. Statistics of extremes is one of th...

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“…The Hurst component is a non-stationary statistic technique that quantifies the long-term persistence of a time series as a whole (Hurst 1951). To account for the fractal structure of a time series, researchers have applied the Hurst component locally on hydrological time series considering non-overlapping moving time windows sliding through the dataset (Habib et al 2017). Dynamic factor analysis (DFA) has been frequently used to reveal the dynamic structure of multifactor time series (Kuo et al 2014;Muñoz-Carpena et al 2005;Oh et al 2017).…”

Section: Introductionmentioning

confidence: 99%

Identifying and quantifying the latent factors controlling spring discharge in an industrialized karst watershed

Jamshidi

Qian

et al. 2021

Preprint

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Karst springs have largely been impacted by human activity and climate change, which increases the uncertainties to supply and management of water resources. It’s essential to understand the footprint of the controlling factors on their hydrological behavior. Taking the example of Niangziguan springs in China, this study explores the time series of spring discharge over a 50-year time-window (1959–2007). Local Hurst exponent is employed to analyze its temporal correlation behavior. The prime latent factors of spring discharge are extracted and identified using dynamic factor analysis and wavelet analysis, respectively. Moreover, we quantified the contributions of latent factors to spring discharge. The results exhibit that the spring discharge is controlled by the positive feedback mechanism before the exploitation of karst groundwater, however, human activities lead to the feature of noise for spring discharge fluctuations. With the development of industry and agriculture, the effect of the combination of controlling factors on spring discharge tends to be stable. Before groundwater exploited, the local recharge cycle of groundwater shows the largest contribution (43%) on spring discharge focusing on the scales of 4–16 months, and the construction of the industrial region weakens the impact of local groundwater recharge (41%). The contribution of human activities, regional groundwater recharge, and rainfall/runoff to spring discharge (27%-30%) is almost equal. Therefore, human activities have become one of the most important factors controlling the spring discharge. The WNPM (West North Pacific Monsoon) and ISM (Indian Summer Monsoon) show the smallest effect (3%) on spring discharge.

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Temporal scaling phenomena in groundwater-floodplain systems using robust detrended fluctuation analysis

Abstract: 13In order to determine objectively the fractal behaviour of a time series, and to facilitate potential 14 future attempts to assess model performance by incorporating fractal behaviour, a multi-order 15

Cited by 29 publications

References 52 publications

A conceptual model for the analysis of multi-stressors in linked groundwater–surface water systems

A conceptual model for the analysis of multi-stressors in linked groundwater–surface water systems

Statistical Analysis of Extreme Events in Precipitation, Stream Discharge, and Groundwater Head Fluctuation: Distribution, Memory, and Correlation

Identifying and quantifying the latent factors controlling spring discharge in an industrialized karst watershed

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