Effect of heterogeneity on spatiotemporal variations of groundwater level in a bounded unconfined aquifer

Liang, Xiuyu; Zhang, You‐Kuan; Schilling, Keith E.

doi:10.1007/s00477-014-0990-4

Cited by 9 publications

(4 citation statements)

References 21 publications

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“…So, the lower the location of soil in topohydrosequence, the smaller the impact of precipitation from the previous year on the mean annual water table depth. Lutz et al [5] and Liang et al [42] have reported that it is difficult to find a direct relationship between precipitation and the groundwater level because of the complex factors determining the groundwater depth. In our study low values of the regression coefficient were obtained taking into account the relationship between precipitation and water table depth only.…”

Section: Resultsmentioning

confidence: 99%

Temporal Variability of Water Table Depth in Topohydrosequence of Undulating Ground Moraine in Central Poland

Kozłowski¹,

Komisarek²,

Wiatrowska³

2018

Pol. J. Environ. Stud.

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Spatial and temporal variability in the water table depth is one of the most important features affecting plant cultivation, water management, and protection, as well as the formation of water and soil resources. Hence, much effort has been devoted to the issues of temporal as well as spatial water table variability. Many authors have emphasized a relationship between the water table and groundwater level fluctuations and climatological parameters such as precipitation and temperature [1-7]. Cai and Ofterdinger [2], examining the relationship between soil morphology and water table depth, have found a correlation between the average annual water table depth and annual precipitation. Shi et al. [8] emphasized that it is difficult to find a direct relation between precipitation and the groundwater level because of the complex factors determining groundwater depth. Statistically significant relationships between climatic parameters such as rainfall and temperature and groundwater level have also been found by Almedeij and Al-Ruwaih [9]. Cai and Ofterdinger [7] have noted a rapid connection between groundwater table depth and a precipitation

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Section: Resultsmentioning

confidence: 99%

Temporal Variability of Water Table Depth in Topohydrosequence of Undulating Ground Moraine in Central Poland

Kozłowski¹,

Komisarek²,

Wiatrowska³

2018

Pol. J. Environ. Stud.

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“…As mentioned above, the incremental time series exhibits anti‐persistent behaviors when the Hurst exponent is less than 0.5, which tends to induce stability within the level fluctuation process (Scafetta et al ). When the Hurst exponent is between 0.5 and 1, the increment fluctuation is positively persistent where the possible external incentive might be governed by a positive feedback mechanism, such as the system heterogeneity (i.e., preferential flow paths) enhancing the correlation between groundwater and river (Liang et al ). This trend is more obvious with a larger Hurst exponent (i.e., H > 1).…”

Section: Methodology: a Brief Reviewmentioning

confidence: 99%

“…Groundwater dynamics, represented for example by the spatiotemporal fluctuations of groundwater levels, can be nonlinear, likely due to multiscale medium heterogeneity and complex external factors such as recharge which is triggered by rainfall‐runoff processes that are spatiotemporally variable (Yu and Chu ; Rakhshandehroo and Amiri ; Chang and Yeh ; Joelson et al ; Liang et al ; Hsiao et al ). The scaling characteristics embedded in the groundwater level fluctuations may reflect the critical hydrologic features of groundwater.…”

Section: Introductionmentioning

confidence: 99%

Temporal Scaling Analytical Method to Identify Multi‐Fractionality in Groundwater Head Fluctuations

Yuan

Sun²,

Zhang³

et al. 2018

Groundwater

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Natural dynamics such as groundwater head fluctuations may exhibit multi‐fractionality, likely caused by multi‐scale aquifer heterogeneity and other controlling factors, whose statistics requires efficient quantification methods. As a scaling exponent, the Hurst exponent can describe the temporal correlation or multifractal behavior in groundwater level fluctuation processes. However, the scaling behavior may change with time under natural conditions, likely due to the non‐stationary evolution of internal and external conditions, which cannot be characterized by traditional methods using a single or several scaling exponents for the complex features of the overall process. This methods note quantifies the multi‐fractionality using the timescale local Hurst exponent (TS‐LHE) and then proposes a systematic statistical method to analyze groundwater head fluctuations. Time series of daily groundwater level fluctuations from three wells located in the lower Mississippi valley are analyzed, after removing the seasonal cycle, which leads to transient TS‐LHE, implying multi‐fractionality and multifractal‐scaling behavior that changes with time and location. Therefore, the temporal scaling analysis proposed here may provide useful and quantitative information to understand the nature of dynamic hydrologic systems.

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“…Application of the tEnKF to the identification of conductivities Multivariate spatiotemporal random fields have been used in a variety of geophysical applications. For example, Bodas-Salcedo et al (2003) combined spatiotemporal random fields with the Kalman filter method to predict solar radiation in the earth-atmosphere system; Suciu (2014) used a diffusion model to predict solutes transport in groundwater under uncertainty about spatiotemporal evolution of velocity fields; a similar approach was used by Suciu et al (2016) to model reactive transport; Sanchez et al (2016) developed a spatiotemporal dynamic model based on the classical EnKF for Bayesian inference of rainfall; and Liang et al (2016) used a stochastic groundwater flow model to analyze the effect of uncertainty in recharge and transmissivity on the spatiotemporal variations of groundwater level in an unconfined aquifer. Finally, Moslehi and de Barros (2017) investigated the impact of uncertainty in spatial variability of soil hydraulic conductivity on several environmental performance metrics that are relevant for environmental risk assessments, such as species concentrations and arrival times, using a stochastic advection-dispersion model to represent the spatiotemporal evolution of the concentration field.…”

Section: Numerical Implementationmentioning

confidence: 99%

Hydraulic conductivities identification via Ensemble Kalman Filtering with transformed data considering the risk of systematic bias

Vázquez-Guillén¹,

Auvinet

2017

GeofInt

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In subsurface hydrology, Ensemble Kalman Filtering (EnKF) has been coupled with groundwater flow and transport models to solve the inverse problem. Several extensions of the EnKF have been proposed to improve its performance when dealing with non-multi-Gaussian random field models of the hydraulic conductivity. One such variant is the EnKF with transformed data (tEnKF), which uses Gaussian anamorphosis within a conditioning step. Although this transformation has been used in the past to identify hydraulic conductivities, previous studies have ignored the risk of introducing a systematic bias in the spatiotemporal evolution of the hydraulic head field during the forecast steps that the update steps may not correct over time. This paper proposes that in order to evaluate the performance of tEnKFs, applications in synthetically generated random porous media should take into account this risk by incorporating prior knowledge with a multi-Gaussian conductivity correlation structure, and by adopting a reference field with asymmetric correlation structure. As an example of this application, hydraulic conductivities using the tEnKF were identified by solving a one-dimensional, single phase flow problem in a continuous random porous medium. Common concepts in Geostatistics are used to explain the hypothesis underlying both EnKF and tEnKF and to establish a clear link between the tEnKF and the stochastic simulation of conditional random fields.

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Effect of heterogeneity on spatiotemporal variations of groundwater level in a bounded unconfined aquifer

Cited by 9 publications

References 21 publications

Temporal Variability of Water Table Depth in Topohydrosequence of Undulating Ground Moraine in Central Poland

Temporal Variability of Water Table Depth in Topohydrosequence of Undulating Ground Moraine in Central Poland

Temporal Scaling Analytical Method to Identify Multi‐Fractionality in Groundwater Head Fluctuations

Hydraulic conductivities identification via Ensemble Kalman Filtering with transformed data considering the risk of systematic bias

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