Groundwater level status report for 2008, Los Alamos National Laboratory

Koch, Richard J.; Schmeer, Sarah

doi:10.2172/974685

Cited by 2 publications

(5 citation statements)

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“…However, changes in the recharge and discharge conditions at these regional boundaries may cause the observed long‐term decline of the water levels. Such a decline of the water levels has been observed at monitoring wells that are far from pumping wells (Koch and Schmeer 2009; available for download at http://www.ees.lanl.gov/staff/monty/). As a result, the pumping influences are superimposed on the ambient flow structure.…”

Section: Site Datamentioning

confidence: 85%

“…However, due to seasonality of the water demands, there is substantial recovery in the low pumping periods (typically in January to February). When the water supply wells are not used for significant periods of time, water levels at the pumping wells recover to levels close to prepumping levels (Koch and Schmeer 2009; available for download at http://www.ees.lanl.gov/staff/monty/). The water supply wells also capture some of the ambient flows that occur in the regional aquifer between the zone of mountain‐front recharge (approximately due west from the study area) and the zone of regional basin discharge (approximately due southeast of the study area) (Vesselinov 2004b).…”

Section: Site Datamentioning

confidence: 99%

“…As advocated by Yeh and Lee (2007), existing datasets from monitoring well networks recorded during long‐term pumping of water supply wells provide an alternative to datasets generated by dedicated pumping test. Such datasets are frequently collected in monitoring well networks established near contamination sites and municipal water supply wells (Barnett et al 2003; Mason et al 2005; Gross 2007; Hix 2007; Koch and Schmeer 2009). However, the pumping influences are often intermittent and small in magnitude compared with water level fluctuations caused by other hydrogeologic mechanisms (e.g., recharge transients), causing the identification of the pumping influences due to a complex spatially and temporally variable water supply pumping regime to be difficult.…”

Section: Introductionmentioning

confidence: 99%

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Identification of Pumping Influences in Long-Term Water Level Fluctuations

Harp¹,

Vesselinov

2011

Ground Water

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Identification of the pumping influences at monitoring wells caused by spatially and temporally variable water supply pumping can be a challenging, yet an important hydrogeological task. The information that can be obtained can be critical for conceptualization of the hydrogeological conditions and indications of the zone of influence of the individual pumping wells. However, the pumping influences are often intermittent and small in magnitude with variable production rates from multiple pumping wells. While these difficulties may support an inclination to abandon the existing dataset and conduct a dedicated cross-hole pumping test, that option can be challenging and expensive to coordinate and execute. This paper presents a method that utilizes a simple analytical modeling approach for analysis of a long-term water level record utilizing an inverse modeling approach. The methodology allows the identification of pumping wells influencing the water level fluctuations. Thus, the analysis provides an efficient and cost-effective alternative to designed and coordinated cross-hole pumping tests. We apply this method on a dataset from the Los Alamos National Laboratory site. Our analysis also provides (1) an evaluation of the information content of the transient water level data; (2) indications of potential structures of the aquifer heterogeneity inhibiting or promoting pressure propagation; and (3) guidance for the development of more complicated models requiring detailed specification of the aquifer heterogeneity.

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Section: Site Datamentioning

confidence: 85%

Section: Site Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Identification of Pumping Influences in Long-Term Water Level Fluctuations

Harp¹,

Vesselinov

2011

Ground Water

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“…Assuming that (1) each well is affected by the same barometric pressure fluctuations (i.e., ignoring small differences in the ground‐surface elevations at the well head and local impacts of the topography on the barometric pressure distribution on the ground surface), and (2) the highest barometric impact (0.28 at R‐13) is representative for 100% efficiency, the barometric efficiencies of R‐11, R‐15, and R‐28 are 99%, 86%, and 84%, respectively. Previous analyses at the site estimated that all four wells have barometric efficiency of

\sim 100 %

[ Koch and Schmeer , ]. The analyses presented here suggest that only two of the wells have efficiency close to 100% (R‐11 and R‐13).…”

Section: Resultsmentioning

confidence: 48%

“…Due to concerns related to the migration of potential LANL‐derived contaminants in the subsurface, an extensive monitoring network is established in the regional aquifer beneath LANL. The network includes more than 100 regional monitoring wells with about 350 monitoring screens [ Koch and Schmeer , ]. Pressure fluctuations at each screen are automatically monitored using pressure transducers.…”

Section: Site Datamentioning

confidence: 99%

Blind source separation for groundwater pressure analysis based on nonnegative matrix factorization

Alexandrov

Vesselinov

2014

Water Resources Research

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The identification of the physical sources causing spatial and temporal fluctuations of aquifer water levels is a challenging, yet a very important hydrogeological task. The fluctuations can be caused by variations in natural and anthropogenic sources such as pumping, recharge, barometric pressures, etc. The source identification can be crucial for conceptualization of the hydrogeological conditions and characterization of aquifer properties. We propose a new computational framework for model-free inverse analysis of pressure transients based on Nonnegative Matrix Factorization (NMF) method for Blind Source Separation (BSS) coupled with k-means clustering algorithm, which we call NMFk. NMFk is capable of identifying a set of unique sources from a set of experimentally measured mixed signals, without any information about the sources, their transients, and the physical mechanisms and properties controlling the signal propagation through the subsurface flow medium. Our analysis only requires information about pressure transients at a number of observation points, m, where m ! r, and r is the number of unknown unique sources causing the observed fluctuations. We apply this new analysis on a data set from the Los Alamos National Laboratory site. We demonstrate that the sources identified by NMFk have real physical origins: barometric pressure and water-supply pumping effects. We also estimate the barometric pressure efficiency of the monitoring wells. The possible applications of the NMFk algorithm are not limited to hydrogeology problems; NMFk can be applied to any problem where temporal system behavior is observed at multiple locations and an unknown number of physical sources are causing these fluctuations.

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Groundwater level status report for 2008, Los Alamos National Laboratory

Cited by 2 publications

References 0 publications

Identification of Pumping Influences in Long-Term Water Level Fluctuations

Identification of Pumping Influences in Long-Term Water Level Fluctuations

Blind source separation for groundwater pressure analysis based on nonnegative matrix factorization

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