The high geological stress field and high temperature observed at large depth result in a complex and uncertain engineering environment for developing deep mining projects. It severely restricts the safe and efficient mining of minerals and geothermal resources. A better assessment of the temperature and stress field is thus essential. Firstly, considering the uncertainty of subsurface parameters, the distribution of the temperature field will be evaluated based on numerical simulations and in-situ tests, using a new framework called Bayesian Evidential Learning. The most sensitive parameters for deep mining will be identified and the heat storage capacity of the ground will be evaluated. Secondly, a model of the wave velocity field in the mining area will be established by combining external active source and internal micro-fracture source of the rock mass. Based on this, the dynamic change the wave velocity field under the coupled action of geothermal and mining exploitation will be systematically analyzed. Finally, the evolution of the stress field deduced from temperature and wave velocity fields will be predicted, in order to forecast the risk of rock failure in the deep mining environment. This project will provide theoretical and technical support for the safe and efficient mining of deep minerals and geothermal resources.  

This project has received funding from FWO.

This project aims at strengthening research capacities and providing scientific databases and management solutions for the sustainable exploitation and utilization of groundwater resources for the agro-food sector in the Southern Central region, thereby contributing to climate change mitigation and the sustainable socio-economic development of the region.

In terms of this project, new geophysical and hydrogeological data was collected in Binh Thuan, Vietnam. During this field work, including the ERT investigation and measurements of the water table and salinity, groundwater samples were collected for a full geochemical characterization. Automatic monitoring sensors were also installed.

In this research Linh evaluated the current salinity and freshening situation in the south-central region of Vietnam and built a conceptual model of the saltwater intrusion and freshening processes.

Currently, Linh is working on a groundwater model for the Luy’s River catchment in Binh Thuan province. In this study, the flow of groundwater with different densities is simulated to find out how aquifer properties affect the freshwater and saltwater distributions and to verify numerically the hypothesis behind the hydro-chemical conceptual model, i.e., to see if the time scale of the freshening and saltwater intrusion processes fit with the hydraulic properties and water balance in the catchment under the natural conditions. A groundwater model is simulated using SEAWAT based on the characterization of hydro-stratigraphic, the hydraulic parameters and boundary conditions distribution  in the study area.

This project has received funding from VLIR-UOS since 2019 (VN2019TEA494A103).

The lithological and stratigraphical heterogeneity of coastal aquifers has a great influence on saltwater intrusion (SI). This makes it difficult to predict SI pathways and their persistence in time. In this context, a combination of both laboratory and field geophysical methods including electrical resistivity tomography (ERT), (spectral) induced polarization (SIP, IP), and electromagnetics (EM39) was used to discriminate the presence of saltwater-bearing and clayey sediment layers. Moreover, these techniques were carried out in many phases to assess SI based on both the presence and absence of co-location data. 

For the first phase, the fresh- and saline water interface was observed in the inversion imaging to the expanding extent toward inland on the left side of the Luy River, but narrower boundaries on the opposite side where huge sandy dunes are located (Diep Cong-Thi et al, 2021). The clay-rich sediment lenses/ layers heterogeneously distributed in the saline zones are clarified in the following phases.

This project has received funding Vlir-uos through the TEAM project VN2019TEA494A103. 

Shallow geothermal energy is a sustainable, locally available alternative to provide heating and cooling to buildings. Groundwater is used to store the excess heat of buildings in summer in the subsurface with pumping and injection wells. In winter we can extract it again to heat the building in a green and sustainable way while storing cold for air conditioning in summer.  In this way the subsurface acts as a kind of heat source or sink. These aquifer thermal energy storage (ATES) systems can potentially reduce CO₂ emissions by 30% compared to conventional systems and can therefore be significant contributors to the energy mix. Along with the green and sustainable nature of this system, the currently rising energy cost drives the increasing interest in ATES. This will add more stress to our valuable subsurface system and more complex aquifers will become a target. The use of shallow geothermal energy is well known, however, there will be a need for a more thorough understanding of the scale and magnitude of the decision involved as well as optimization under uncertainty.  

The objective of this research is to improve the design of shallow geothermal systems and predict the uncertainty of their energy efficiency using a stochastic framework called Bayesian evidential learning (BEL). The method will be validated at a spatiotemporal scale relevant for ATES 

systems using two in-use systems and mimicking sparse data faced by companies and practitioners. ATES2.0 will be able to handle the currently growing complexity of data and models and it will offer a thorough and accurate methodology for proper natural resource management.  

This project has received funding from BOF (Ghent University special research fund) and FWO. 

Accurate subsurface imaging through geophysics is of prime importance for many geological and hydrogeological applications. Among many imaging techniques, application of electromagnetic method is very popular in geophysics for acquiring data set at relevant depth of investigation. But the solution of inversion of EM data is not unique. In this research Arsalan is estimating the uncertainty quantification of salinity of Belgian coast data set, rather than a deterministic solution, by using new stochastic approach called Bayesian evidential learning (BEL) in one dimension.

Groundwater is a vital natural resource, and understanding its potential, as well as the intricate dynamics of recharge and discharge systems, is crucial for sustainable water management. Groundwater potential assessment involves evaluating the capacity of an area to yield usable groundwater. This assessment considers geological formations, hydrological conditions, and human activities impacting aquifers.
Approximately 80% of water utilization in the southwestern region of Ethiopia relies on groundwater for both urban and rural needs, including drinking water and irrigation. However, there is limited detailed knowledge regarding the groundwater potential, encompassing key aspects like aquifer productivity, recharge-discharge mechanisms, and chemical attributes.
The research objective is to evaluate and quantify the groundwater potential while analyzing the recharge-discharge systems within the Gilgel Gibe and Upper Dhidhessa catchments situated in southwestern Ethiopia.The integration of various methodologies, such as desk studies, fieldwork, laboratory analysis, and software-based interpretation, indicates a well-rounded approach for sussfull of this work . Additionally, collecting primary and secondary data, including groundwater level data from data loggers and manual collection, highlights the dedication to gathering diverse sources of information. In this study, we will utilize two primary methods for estimating recharge: the Water Table Fluctuation and Chloride Mass Balance techniques. These approaches rely on groundwater level observations, rainfall data, and hydraulic properties such as the specific yield of the local aquifer system.
By comprehensively studying groundwater potential and the recharge-discharge system, we can develop strategies for sustainable water resource management, ensuring reliable access to clean water while preserving the health of aquifers and the ecosystems they support. Groundwater information will be disseminated to the decision makers so that they will be informed, and emphasis will be given to such limited resources.
This project  has recieved funding from BOF (Ghent University Special research Fund for developing countries).

Groundwater resource distribution in coastal areas is the result of the complex interaction between freshwater recharging on land and seawater. In undisturbed conditions, a natural equilibrium exists leading to typical seawater intrusion (SI) into freshwater aquifers. Fresh groundwater, recharged from rainwater on land, migrates to the sea and results in fresh submarine groundwater discharge (FSGD). At the Belgian coast, the distribution of the fresh/salt water interface is complex due to the Quaternary coastal evolution and anthropogenic factors (e.g., polders). Heterogeneity of lithology can play a major role in the groundwater distribution and the location of the FSGD. The geophysical methods based on resistivity and seismic will be used to investigate the geology and the FSGD of the Belgian coast zone in more detail, building further on Marieke Paepen’s research. Groundwater models of the Belgian coast can then be updated, leading to better predictions of the groundwater distribution. 

Groundwater (GW)-surface water (SW) interaction and recharge-discharge systems have a remarkable implications in water management, solute transport, and contaminant protection. However, due to its complexity and paucity of observed data, the study of GW-SW interaction is overlooked in developing countries like Ethiopia. In Gilgel Gibe catchment in particular, the current ambitious plan to utilize GW to meet the rapidly increasing demand for industrial, domestic, and irrigation uses would poses stress on the sustainability of water use in the catchment. Therefore, the accurate estimation of GW-SW exchange, GW recharge and its spatiotemporal distribution is indispensable for the sustainability of water resource development in the catchment..

While Soil Moisture Balance (SMB) and Base Flow Separation (BFS) methods are applied to estimate the GW recharge at points and sub-catchments scale, WetSpass and SWAT models are applied to evaluate its spatial and temporal distribution respectively. An integrated model, SWAT-MODFLOW will be applied to estimate the GW-SW fluxes in the catchment. The data required for setup, calibration and validation of the models are collected from both primary and secondary sources. Primary data collection includes drilling well observations, manual and automated piezometric level measurements, and water sampling. The secondary data includes land use land cover, soil, hydrometeorological, and hydrogeological data obtained from different offices and satellite products.

The findings of this study will be essential to improve the conjunctive use of GW and surface; to control contamination of SW caused by GW and vice versa; and to formulate and implement sound policies to minimize undesirable future impacts and devise management alternatives.

The possible geogenic impacts on the groundwater resource quality in the Mekelle area are influenced by the nature of the aquifer and geological structures (faults and joints), which serve as the main conduits for water percolation to the subsurface. The water quality can be affected by contaminants introduced through human activities associated with urban areas, such as heavy metals and chemicals. 
 
To investigate the groundwater quality of the Mekelle area and its surroundings, the project will utilize hydrogeological, hydrochemical, and Vertical Electrical Sounding (VES) geophysical integrated methods. 
 
The project has received funding from VLIROUS (Global Minds Fund Ghent University 2021) and BOF (special research fund). 

The research focuses on land-use modeling, runoff generation, groundwater recharge, and water balance modelling. Fieldwork experience includes installing and collecting meteorological data, river discharge measurements, groundwater level monitoring, and water sampling collection.