Subsurface flow and transport in uncertain heterogeneous porous media such as aquifers are poorly predictable. In catchments of drinking water wells, the situation becomes even more complex, because contaminants might be released at unknown locations and moments. Still, one needs to ensure the safety of clean drinking water supply. This can be achieved with early warning systems, i.e. monitoring networks that pay special attention to the remaining time for action after a contamination has been detected somewhere in the aquifer.
The goal of this project is to develop and establish a concept to assess, design and optimize early-warning monitoring systems within well catchments. Good early-warning networks need to strike an optimal balance between three competing objectives: (1) a high detection probability, which can be achieved through maximizing the “field of vision” of the monitoring network or by monitoring close to the drinking water well, (2) a long early-warning time in order to leave enough time to take counter measures after first detection, (3) small overall operating costs. The early warning time describes the remaining travel time for contaminants to the drinking water well after they have been detected in a monitoring well. The detection probability of a contamination can only be assessed correctly, if the actual dilution, the spreading, and the large-scale uncertainty in predicting the travel path of contaminant plumes are kept separate in the involved transport simulations. The method is based on numerical simulation of flow and transport in heterogeneous porous media coupled with geostatistics and Monte-Carlo simulation, wrapped within a formal multi-objective optimization.
With this method, the safety level of existing monitoring systems can be assessed and set in relation to their operation costs. Also, selective improvements can be added to existing monitoring networks to increase the safety quality at minimal costs. Another application of this method is to design new optimal monitoring networks considering the different objective functions within multi-objective optimization.
|Principal investigator||Prof. Dr.-Ing. Wolfgang Nowak||Partner||Prof. Philip J. Binning, DTU Denmark (Denmark)
Prof. Patrick Reed, Cornell University (United States)
|Duration||07/2013 - 12/2017||Financing||International Research Training Group "NUPUS" (DFG IRTG 1398)|