Characterizing Spatial Random Fields through a Bayesian Inverse Modeling Framework and the High Throughput Computing Software - HTCondor

Session F5: Advances in Environmental Software Systems

Spatial Random Fields (SRFs) are used to characterize the behavior of variables which are difficult to measure everywhere. In the case of hydraulic conductivity used in groundwater models, heterogeneity can be modeled through structural parameters of the random fields – parameters that define the field’s spatial distribution. Characterization of these structural parameters using a stochastic inverse method requires the evaluation of thousands of potential realizations of the SRF which is a time consuming and challenging task – reducing the adoption of stochastic models. To address this issue, this paper presents the integration of high throughput computing using HTCondor with the MAD# framework – an uncertainty characterization tool that uses the Method of Anchored Distributions (MAD). MAD# is coupled with HTCondor allowing users to search for the optimal location of anchors – statistical devices located in the field – using the parallelization capabilities of HTCondor. As expected, using this approach, the simulation processing time decreases as the number of instances (CPUs) used in the HTCondor Network increases. Also, weekend results show marked improvement over weekday results likely due to fewer interruptions and reassignment of processing tasks between nodes. This demonstration and implementation of an SRF characterization process in a parallel environment shows potential for broader use of the method in environmental modeling.