Augmenting Groundwater Monitoring Networks near Landfills with Slurry Cutoff Walls

2004 ◽  
Vol 90 (1-3) ◽  
pp. 113-120 ◽  
Author(s):  
Paul F. Hudak
Keyword(s):  
Groundwater Monitoring ◽  
Monitoring Networks ◽  
Cutoff Walls

Geostatistical screening of flood events in the groundwater levels of the diverted inner delta of the Danube River: implications for river bed clogging

2018 ◽  
Vol 10 (1) ◽  
pp. 64-78 ◽  
Author(s):  
Balázs Trásy ◽  
Tamás Garamhegyi ◽  
Péter Laczkó-Dobos ◽  
József Kovács ◽  
István Gábor Hatvani
Keyword(s):  
Spatial Autocorrelation ◽  
Groundwater Monitoring ◽  
Shallow Groundwater ◽  
Monitoring Network ◽  
Danube River ◽  
Monitoring Networks ◽  
Flood Events ◽  
Raw Data ◽  
Monitoring Wells ◽  
Autocorrelation Structure

Abstract The efficient operation of shallow groundwater (SGW) monitoring networks is crucial to water supply, in-land water protection, agriculture and nature conservation. In the present study, the spatial representativity of such a monitoring network in an area that has been thoroughly impacted by anthropogenic activity (river diversion/damming) is assessed, namely the Szigetköz adjacent to the River Danube. The main aims were to assess the spatial representativity of the SGW monitoring network in different discharge scenarios, and investigate the directional characteristics of this representativity, i.e. establish whether geostatistical anisotropy is present, and investigate how this changes with flooding. After the subtraction of a spatial trend from the time series of 85 shallow groundwater monitoring wells tracking flood events from 2006, 2009 and 2013, variography was conducted on the residuals, and the degree of anisotropy was assessed to explore the spatial autocorrelation structure of the network. Since the raw data proved to be insufficient, an interpolated grid was derived, and the final results were scaled to be representative of the original raw data. It was found that during floods the main direction of the spatial variance of the shallow groundwater monitoring wells alters, from perpendicular to the river to parallel with it for over a period of about two week. However, witht the passing of the flood, this returns to its original orientation in ~2 months. It is likely that this process is related first to the fast removal of clogged riverbed strata by the flood, then to their slower replacement. In addition, the study highlights the importance of assessing the direction of the spatial autocorrelation structure of shallow groundwater monitoring networks, especially if the aim is to derive interpolated maps for the further investigation or modeling of flow.

Design of groundwater monitoring networks for landfills

1995 ◽  
pp. 190-196 ◽  
Author(s):  
P. D. Meyer ◽  
J. W. Eheart ◽  
S. Ranjithan ◽  
A. J. Valocchi
Keyword(s):  
Groundwater Monitoring ◽  
Monitoring Networks

Reliability assessment of groundwater monitoring networks at landfill sites

2005 ◽  
Vol 308 (1-4) ◽  
pp. 1-17 ◽  
Author(s):  
N. Buket Yenigül ◽  
Amro M.M. Elfeki ◽  
Johannes C. Gehrels ◽  
Cees van den Akker ◽  
André T. Hensbergen ◽  
...  
Keyword(s):  
Groundwater Monitoring ◽  
Reliability Assessment ◽  
Monitoring Networks ◽  
Landfill Sites

Optimal Design of Multitype Groundwater Monitoring Networks Using Easily Accessible Tools

Ground Water ◽  
2016 ◽  
Vol 54 (6) ◽  
pp. 861-870 ◽  
Author(s):  
Thomas Wöhling ◽  
Andreas Geiges ◽  
Wolfgang Nowak
Keyword(s):  
Optimal Design ◽  
Groundwater Monitoring ◽  
Monitoring Networks

scholarly journals Multi-objective optimization of groundwater monitoring network using a probability Pareto genetic algorithm and entropy method (case study: Silakhor plain)

2020 ◽  
Author(s):  
Mehdi Komasi ◽  
Hesam Goudarzi
Keyword(s):  
Genetic Algorithm ◽  
Groundwater Monitoring ◽  
Monitoring Network ◽  
Entropy Theory ◽  
Monitoring Networks ◽  
Nsga Ii ◽  
Potential Wells ◽  
Optimal Monitoring ◽  
Observation Wells ◽  
Optimal Monitoring Network

Abstract Optimal groundwater monitoring networks have an important role in water resources management. For this purpose, two scenarios were presented. The first scenario designs a monitoring network and the second scenario chooses optimal wells from the existing ones in the study area of the monitoring network. At the first step, a database including groundwater elevation in potential wells was produced using the Kriging method. The optimal monitoring network in the first scenario was determined by preset conventions and found by the non-dominated sorting genetic algorithm (NSGA-II). In the second scenario, the optimal monitoring network was determined by entropy theory through calculating entropy for each of the 29 observation wells. Finally, the first scenario obtained a network with 12 observation stations showing root mean square error (RMSE) value given as 0.61 m. Comparison between entropy of rainfall and groundwater level time series in the first scenario had the same variation. The optimal monitoring network in the first scenario has been able to reduce the number of monitoring stations by 60% in comparison with the existing observation network. The second scenario used entropy theory and calculated the energy of each of the 29 observation wells which obtained a monitoring network with 11 stations.

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