资源环境科技发展态势分析平台

A method for the stochastic design of groundwater quality observation well network is presented. The method uses calibration-constrained Null-space Monte Carlo analysis for the stochastic simulation of the reduction ratio of peak concentration and the time corresponding to this in an injection well field. The numerical groundwater model simulations are constrained with a limited amount of field measurements. The objective of the monitoring network design is to identify optimal monitoring locations that allow for prediction of spatial fields from the data collected at limited number of points in the spatial domain. These locations need to be robust to different possible outcomes simulated using the stochastic model runs, and result in good spatial predictions, regardless of which one of the many possibilities turned out to be the true representation of nature. Multiple simulated fields of concentration and time are used to identify a small set of empirical orthogonal functions (spatial basis functions) for reduced-rank prediction of the spatial patterns in these two fields. The Differential Evolution algorithm was used to find the monitoring locations that allowed for optimal reconstruction of all the simulated fields (potential future states of reality) from the set of empirical orthogonal functions. The applicability is demonstrated for designing a monitoring network for an injection well field. Optimal locations of 10 monitoring wells were identified. The method has the capability to simultaneously identify the optimal locations and inform optimal times for monitoring reduction ratio of peak concentration. The method is flexible to iteratively combine stochastic modeling and monitoring for optimal groundwater management.