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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/gmd-2018-253
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2018-253
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Development and technical paper 29 Nov 2018

Development and technical paper | 29 Nov 2018

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This discussion paper is a preprint. It is a manuscript under review for the journal Geoscientific Model Development (GMD).

A parallel workflow implementation for PEST version 13.6 in high-performance computing for WRF-Hydro version 5.0: a case study over the Midwestern United States

Jiali Wang, Cheng Wang, Andrew Orr, and Rao Kotamarthi Jiali Wang et al.
  • Argonne National Laboratory, Environmental Science Division, 9700 South Cass Avenue, Argonne, IL 60439, USA

Abstract. Surface hydrological models must be calibrated for each application region. The Weather Research and Forecasting Hydrological system (WRF-Hydro) is a state-of-the-art numerical model that models the entire hydrological cycle based on physical principles. However, as with other hydrological models, WRF-Hydro parameterizes many physical processes. As a result, WRF-Hydro needs to be calibrated to optimize its output with respect to observations. However, when applied to a relatively large domain, both WRF-Hydro simulations and calibrations require intensive computing resources and are best performed in parallel. Typically, each physics parameterization requires a calibration process that works specifically with that model, and is not transferrable to a different process or model. Parameter Estimate Tool (PEST) is a flexible and generic calibration tool that can calibrate any numerical code. However, PEST in its current configuration is not designed to work on the current generation of massively parallel high-performance computing (HPC) clusters. This study ported the parallel PEST to HPCs and adapted it to work with the WRF-Hydro. The porting involved writing scripts to modify the workflow for different workload managers and job schedulers, as well as developing code to connect parallel PEST to WRF-Hydro. We developed a case study using a flood in the Midwestern United States in 2013 to test the operational feasibility of the HPC-enabled parallel PEST. We then evaluate the WRF-Hydro performance in water volume and timing of the flood event. We also assess the spatial transferability of the calibrated parameters for the study area. We finally discuss the scale-up capability of the HPC-enabled parallel PEST to provide insight for PEST's application to other hydrological models and earth system models on current and emerging HPC platforms. We find that, for this particular study, the HPC-enabled PEST calibration tool can speed up WRF-Hydro calibration by a factor of 30 compared to commonly-used sequential calibration approaches.

Jiali Wang et al.
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A parallel workflow implementation for PEST version 13.6 in high-performance computing for WRF-Hydro version 5.0: a case study over the Midwestern United States J. Wang, C. Wang, A. Orr, and R. Kotamarthi https://doi.org/10.5281/zenodo.1490230

Jiali Wang et al.
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Short summary
WRF-Hydro needs to be calibrated to optimize its output with respect to observations. However, when applied to a relatively large domain, both WRF-Hydro simulations and calibrations require intensive computing resources and are best performed in parallel. This study ported an independent calibration tool (Parameter Estimate Tool (PEST)) to high performance computing clusters and adapted it to work with the WRF-Hydro.
WRF-Hydro needs to be calibrated to optimize its output with respect to observations. However,...
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