Analysis of data value in numerical groundwater modelling at a mining site: Focus on comparison between open-source data model and site-specific data model

Abstract

Groundwater is a hidden but crucial water source. Studying groundwater flow dynamics is challenging as these systems operate beneath the Earth's surface, surrounded by complexity due to their concealed nature. Understanding groundwater flow is essential for managing water resources and predicting potential contamination risks. This study provides significant insights into the groundwater flow system at the Kankberg mine site. This research aimed to identify a more affordable way to learn about the site than the more costly site testing method. The process involves comparing the hydrogeological results of numerical modeling using open-source data compared to using site-specific data. In this way, assessing the accuracy and dependability of modeling groundwater flow using readily available data will become more feasible. MODFLOW-2005 with NWT solver is selected because of its versatility and acceptance as an industry standard. Two separate data sources were used in the research methodology: open-source data that was readily available to the public and data that was gathered through field investigation. The topographical data and hydrogeological data are collected from the SGU and SLU websites, and the water level of four observation wells is used to calibrate the site-specific model. The water head in different places was manually calibrated to make sure it matched the site's topography. The results show that, compared to the open-source data model, the site-specific data model offered a more accurate simulation. Nonetheless, the open-source data model can still make reasonable predictions and is not all that dissimilar from the field-specific data model. Therefore, for complicated projects with substantial environmental impacts, a balanced approach that combines both open-source data and customized field data can produce more reliable and comprehensive results. This hybrid approach allowed us to evaluate the applicability of these modeling strategies in similar working environments, ultimately leading to better decision-making for environmental management. In conclusion, this comprehensive approach fills a significant gap in understanding the data requirements for numerical modeling in the mining industry. Results from an open-source data model are not too far off from reality. Overall, this research highlights the importance of refining data collection methods and utilizing a combination of data sources for more accurate predictions in the mining sector.