Python-Based Model Emulation Workflows with PEST

Computational demands for uncertainty quantification and optimization often exceed available resources for high-fidelity environmental models. While surrogate modeling (or model emulation) offers a pragmatic solution, widespread adoption is hindered by a significant "implementation gap": practitioners often lack standardized, robust tools to integrate emulation techniques directly into existing modeling workflows, relying instead on bespoke implementations. To bridge this gap, we present the Emulator module within the open-source pyEMU package. This framework provides a "plug-and-play" architecture for deploying Gaussian Process Regression (GPR), Data-Space Inversion (DSI) and other model emulation approaches. The framework automates the complex "plumbing" of emulation-based workflows, including non-Gaussian data transformation and the generation of PEST interface files, allowing trained surrogates to serve as drop-in replacements for physics-based models. We believe this one-to-one correspondence between physics-based model and emulator-based workflows will facilitate direct comparisons between the two so that the community in general can build up the knowledge of when and how to effectively and appropriately deploy emulation. We demonstrate the utility of these tools through a benchmarking optimization problem and a history-matching application on a synthetic groundwater model.