Oil infiltration in the vicinity of a shallow groundwater table

2020 ◽  
pp. 17-18
Author(s):  
D. Pokrajac
Keyword(s):  
Shallow Groundwater ◽  
Groundwater Table ◽  
Shallow Groundwater Table

scholarly journals Geological and geomorphological conditions of Archar-Orsoya lowland as a factor for the formation of groundwater chemical composition and in risk of its contamination

2021 ◽  
Vol 35 (1) ◽  
pp. 63-70
Author(s):  
Sava Kolev ◽  
Mila Trayanova
Keyword(s):  
Chemical Composition ◽  
Sand Dunes ◽  
Shallow Groundwater ◽  
Groundwater Table ◽  
Danube River ◽  
Shallow Water Table ◽  
The Danube River ◽  
Sandy Layer ◽  
Shallow Groundwater Table ◽  
Landslide Slope

The Archar-Orsoya lowland is situated in the Danube floodplain west of the town of Lom, NW Bulgaria. It is aligned in a west-east direction along the Danube River and to the south it is bounded by a high landslide slope, built of Pliocene clays and sands. Parallel to the shore, sand dunes are formed with lowered sections between them, in which there are conditions for swamping. The lowland is made up of the alluvial sediments of the Danube, represented by a lower gravelly-sandy layer and an upper sandy-clayey layer. In the gravelly-sandy layer unconfined groundwater is accumulated, with shallow water table – from 0.5 to 7 m beneath the surface. Groundwater is recharged by infiltration of precipitation, surface water and groundwater, which laterally flows into the alluvium from adjacent aquifers. At high waters, the Danube River suppresses the formed groundwater flow and temporarily feeds it. Due to the described formation conditions in the lowland, the chemical composition of groundwater is formed under the influence of intense dynamics and has a low TDS (total dissolved solids). The shallow groundwater table and the corresponding thin unsaturated zone are a prerequisite for easy groundwater contamination with components entering from the surface. Therefore, a map of depth to groundwater table is drawn to identify the most vulnerable areas.

Using machine learning to downscale simulations of climate change induced changes to the shallow groundwater table

2021 ◽  
Author(s):  
Raphael Schneider ◽  
Hans Jørgen Henriksen ◽  
Julian Koch ◽  
Lars Troldborg ◽  
Simon Stisen
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Hydrological Model ◽  
Shallow Groundwater ◽  
Groundwater Table ◽  
Training Data ◽  
Computational Time ◽  
Hydrological Models ◽  
Shallow Groundwater Table ◽  
Induced Changes

<p>The DK-model (https://vandmodel.dk/in-english) is a national water resource model, covering all of Denmark. Its core is a distributed, integrated surface-subsurface hydrological model in 500m horizontal resolution. With recent efforts, a version at a higher resolution of 100m was created. The higher resolution was, amongst others, desired by end-users and to better represent surface and surface-near phenomena such as the location of the uppermost groundwater table. Being presently located close to the surface across substantial parts of the country and partly expected to rise, the groundwater table and its future development due to climate change is of great interest. A rising groundwater table is associated with potential risks for infrastructure, agriculture and ecosystems. However, the 25-fold jump in resolution of the hydrological model also increases the computational effort. Hence, it was deemed unfeasible to run the 100m resolution hydrological model nation-wide with an ensemble of climate models to evaluate climate change impact. The full ensemble run could only be performed with the 500m version of the model. To still produce the desired outputs at 100m resolution, a downscaling method was applied as described in the following.</p><p>Five selected subcatchment models covering around 9% of Denmark were run with five selected climate models at 100m resolution (using less than 3% of the computational time for hydrological models compared to a national, full ensemble run at 100m). Using the simulated changes at 100m resolution from those models as training data, combined with a set of covariates including the simulated changes in 500m resolution, Random Forest (RF) algorithms were trained to downscale simulated changes from 500m to 100m.</p><p>Generalizing the trained RF algorithms, Denmark-wide maps of expected climate change induced changes to the shallow groundwater table at 100m resolution were modelled. To verify the downscaling results, amongst others, the RF algorithms were successfully validated against results from a sixth hydrological subcatchment model at 100m resolution not used in training the algorithms.</p><p>The experience gained also opens for various other applications of similar algorithms where computational limitations inhibit running distributed hydrological models at fine resolutions: The results suggest the potential to downscale other model outputs that are desired at fine resolutions.</p>

scholarly journals An Integrative Approach for Environmental Assessment and Water Resources Management Using Direct Current Resistivity (DC), Geographic Information System (GIS), Remote Sensing, and Gain and Loss Method

2021 ◽  
Author(s):  
Dina Ragab Desouki Abdelmoneim
Keyword(s):  
Remote Sensing ◽  
Surface Water ◽  
Water Resource ◽  
Water Resource Management ◽  
Shallow Groundwater ◽  
Groundwater Table ◽  
Dc Resistivity ◽  
Seepage Rate ◽  
Loss Method ◽  
Shallow Groundwater Table

Sustainable water resource management is a crucial national and global issue (Currell et al., 2012). In arid areas, groundwater is often the major source of water or at least a crucial supplement to other freshwater resources for agriculture, industry and domestic consumption (Vrba and Renaud, 2016). The complexity associated with groundwater-surface water interactions creates uncertainty about water resource sustainability in semi-arid environments, especially with urbanization and population growth. Flood irrigation in the early 1900s increased the shallow groundwater table in the Treasure Valley (TV), but with increasing irrigation efficiencies, they have been declining since the 1960s with a mean decline rate of about 2.9-3.9x10^-9 (m/s) (Contor et al., 2011). Quantifying how much surface water is being exchanged with the shallow groundwater table through canals in the TV is necessary for gaining a better understanding of groundwater-surface water interactions in this heavily managed system. This knowledge would help evaluate alternative management options for achieving sustainable management of existing water resources. The key objectives of this project are to determine the seepage rate through some canal reaches in the TV, evaluate the integration of the gain and loss method, remote sensing, GIS, hydrogeophysical simulation, and direct current (DC) resistivity geophysical methods for water resource management. We hypothesize that the underlying lithology and size of canals affect the magnitude of the seepage rate. Flow measurements were collected weekly between July and August 2020 in canal reaches representing different sizes and lithological units to determine the seepage rate using the reach gain/loss method. Canal variability and measurement uncertainty were included in seepage estimation for the entire TV using 3 alternative scaling approaches. DC resistivity was used as a complementary method to monitor the seepage effect on the shallow GW aquifer over 2 months. This research evaluates to what extent canal size and its underlying lithology affects the seepage rate, and how the integration of methods may provide additional insight into groundwater exchange-surface water.

Clouds and vegetation modulate shallow groundwater table depth

2021 ◽  
Author(s):  
Prabhakar Shrestha
Keyword(s):  
Solar Radiation ◽  
Seasonal Cycle ◽  
Shallow Groundwater ◽  
Groundwater Table ◽  
Positive Anomaly ◽  
Terrestrial Water Storage ◽  
Near Surface ◽  
Incoming Solar Radiation ◽  
Terrestrial Water ◽  
Shallow Groundwater Table

AbstractA 10-year simulation of shallow groundwater table (GWT) depth over a temperate region in northwestern Europe, using a physics based integrated hydrological model at km-scale, exhibits a strong seasonal cycle. This is also well captured in terms of near surface soil moisture anomalies, terrestrial water storage anomalies and shallow GWT depth anomalies from observations over the region. The modeled monthly anomaly of GWT depth exhibits a statistically significant (p<0.05)moderate positive/negative correlation with non-rain and rain affected monthly anomalies of incoming solar radiation. The vegetation cover also produces a strong local control on the variability of shallow GWT depth. Thus, much of the variability in the simulated seasonal cycle of shallow GWT depth could be linked to the distribution of clouds and vegetation.The spatiotemporal distribution of clouds, partly influenced by the Rhein Massif, modulates the seasonal variability of incoming solar radiation and precipitation, over the region. Particularly, the southwestern and northern part of the Rhien Massif divided by the Rhein valley exhibits a dipole behavior with relatively high/low shallow GWT depth fluctuations, associated with positive/negative anomaly of incoming solar radiation and negative/positive anomaly of precipitation.

scholarly journals Impact of a shallow groundwater table on the global water cycle in the IPSL land–atmosphere coupled model

2017 ◽  
Vol 50 (9-10) ◽  
pp. 3505-3522 ◽  
Author(s):  
Fuxing Wang ◽  
Agnès Ducharne ◽  
Frédérique Cheruy ◽  
Min-Hui Lo ◽  
Jean-Yves Grandpeix
Keyword(s):  
Water Cycle ◽  
Shallow Groundwater ◽  
Coupled Model ◽  
Groundwater Table ◽  
Global Water Cycle ◽  
Shallow Groundwater Table ◽  
Global Water
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