Wavelet Analysis of Steady State Groundwater Flow in a Bounded Domain

Author(s):  
M. F. Dillin ◽  
R. M. Neupauer
2021 ◽  
Author(s):  
Liqun Jiang ◽  
Ronglin Sun ◽  
Xing Liang

<p>Protection and management of groundwater resources demand high-resolution distributions of hydraulic parameters (e.g., hydraulic conductivity (K) and specific storage (Ss)) of aquifers. In the past, these parameters were obtained by traditional analytical solutions (e.g., Theis (1935) or Cooper and Jacob (1946)). However, traditional methods assume the aquifer to be homogeneous and yield the equivalent parameter, which are averages over a large volume and are insufficient for predicting groundwater flow and solute transport process (Butler & Liu, 1993). For obtaining the aquifer heterogeneity, some scholars have used kriging (e.g., Illman et al., 2010) and hydraulic tomography (HT) (e.g., Yeh & Liu, 2000; Zhu & Yeh, 2005) to describe the K distribution.</p><p>In this study, the laboratory heterogeneous aquifer sandbox is used to investigate the effect of different hydraulic parameter estimation methods on predicting groundwater flow and solute transport process. Conventional equivalent homogeneous model, kriging and HT are used to characterize the heterogeneity of sandbox aquifer. A number of the steady-state head data are collected from a series of single-hole pumping tests in the lab sandbox, and are then used to estimate the K fields of the sandbox aquifer by the steady-state inverse modeling in HT survey which was conducted using the SimSLE algorithm (Simultaneous SLE, Xiang et al., 2009), a built-in function of the software package of VSAFT2. The 40 K core samples from the sandbox aquifer are collected by the Darcy experiments, and are then used to obtain K fields through kriging which was conducted using the software package of Surfer 13. The role of prior information on improving HT survey is then discussed. The K estimates by different methods are used to predict the process of steady-state groundwater flow and solute transport, and evaluate the merits and demerits of different methods, investigate the effect of aquifer heterogeneity on groundwater flow and solute transport.</p><p>According to lab sandbox experiments results, we concluded that compared with kriging, HT can get higher precision to characterize the aquifer heterogeneity and predict the process of groundwater flow and solute transport. The 40 K fields from the K core samples are used as priori information of HT survey can promote the accuracy of K estimates. The conventional equivalent homogeneous model cannot accurately predict the process of groundwater flow and solute transport in heterogeneous aquifer. The enhancement of aquifer heterogeneity will lead to the enhancement of the spatial variability of tracer distribution and migration path, and the dominant channel directly determines the migration path and tracer distribution.</p>


2019 ◽  
Vol 19 (6) ◽  
pp. 4041-4059 ◽  
Author(s):  
Carsten Schaller ◽  
Fanny Kittler ◽  
Thomas Foken ◽  
Mathias Göckede

Abstract. Methane (CH4) emissions from biogenic sources, such as Arctic permafrost wetlands, are associated with large uncertainties because of the high variability of fluxes in both space and time. This variability poses a challenge to monitoring CH4 fluxes with the eddy covariance (EC) technique, because this approach requires stationary signals from spatially homogeneous sources. Episodic outbursts of CH4 emissions, i.e. triggered by spontaneous outgassing of bubbles or venting of methane-rich air from lower levels due to shifts in atmospheric conditions, are particularly challenging to quantify. Such events typically last for only a few minutes, which is much shorter than the common averaging interval for EC (30 min). The steady-state assumption is jeopardised, which potentially leads to a non-negligible bias in the CH4 flux. Based on data from Chersky, NE Siberia, we tested and evaluated a flux calculation method based on wavelet analysis, which, in contrast to regular EC data processing, does not require steady-state conditions and is allowed to obtain fluxes over averaging periods as short as 1 min. Statistics on meteorological conditions before, during, and after the detected events revealed that it is atmospheric mixing that triggered such events rather than CH4 emission from the soil. By investigating individual events in more detail, we identified a potential influence of various mesoscale processes like gravity waves, low-level jets, weather fronts passing the site, and cold-air advection from a nearby mountain ridge as the dominating processes. The occurrence of extreme CH4 flux events over the summer season followed a seasonal course with a maximum in early August, which is strongly correlated with the maximum soil temperature. Overall, our findings demonstrate that wavelet analysis is a powerful method for resolving highly variable flux events on the order of minutes, and can therefore support the evaluation of EC flux data quality under non-steady-state conditions.


2014 ◽  
Vol 8 ◽  
pp. 8051-8078 ◽  
Author(s):  
D. Nkurunziza ◽  
G. Kakuba ◽  
J. M. Mango ◽  
S. E. Rugeihyamu ◽  
N. Muyinda

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