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

Abstract

Abstract. The Weather Research and Forecasting Hydrological (WRF-Hydro) system is a state-of-the-art numerical model that models the entire hydrological cycle based on physical principles. As with other hydrological models, WRF-Hydro parameterizes many physical processes. Hence, WRF-Hydro needs to be calibrated to optimize its output with respect to observations for the application region. When applied to a relatively large domain, both WRF-Hydro simulations and calibrations require intensive computing resources and are best performed on multimode, multicore high-performance computing (HPC) systems. Typically, each physics-based model requires a calibration process that works specifically with that model and is not transferrable to a different process or model. The parameter estimation tool (PEST) is a flexible and generic calibration tool that can be used in principle to calibrate any of these models. In its existing configuration, however, PEST is not designed to work on the current generation of massively parallel HPC clusters. To address this issue, we ported the parallel PEST to HPCs and adapted it to work with WRF-Hydro. The porting involved writing scripts to modify the workflow for different workload managers and job schedulers, as well as to connect the parallel PEST to WRF-Hydro. To test the operational feasibility and the computational benefits of this first-of-its-kind HPC-enabled parallel PEST, we developed a case study using a flood in the midwestern United States in 2013. Results on a problem involving the calibration of 22 parameters show that on the same computing resources used for parallel WRF-Hydro, the HPC-enabled parallel PEST can speed up the calibration process by a factor of up to 15 compared with commonly used PEST in sequential mode. The speedup factor is expected to be greater with a larger calibration problem (e.g., more parameters to be calibrated or a larger size of study area).

Improving WRF‐Hydro runoff simulations of heavy floods through the sea surface temperature fields with higher spatio‐temporal resolution

“…The SLOPE parameter (default value of 0.1) controlling the openness of the bottom soil column to the conceptual bucket is calibrated between the values of 0.1 and 1.0 with 0.3 increments. Similar to Wang et al (2019), only the first class of the nine SLOPE_DATA categories represented in GENPARM.TBL is subjected to tuning. OVROUGHRTFAC (default value of 1.0) is calibrated for values ranging from 0.1 to 1.0 with 0.3 increments.…”

Climate Change Induced Morphological Response Assessment in Estuary: Case Study of Arakawa River Mouth, Niigata, Japan

“…Furthermore, hydrological simulation in WRF-Hydro is parameterized for various hydrological processes. Thus, this sophisticated study uses the parameter estimation tool (PEST) to automatically determine the parameter settings (Wang et al 2019). However, the parameters were uniform in all cases since our study aims to elucidate changes in the present and future climate.…”

Impact of Fully Coupled Hydrology-Atmosphere Processes on Atmosphere Conditions: Investigating the Performance of the WRF-Hydro Model in the Three River Source Region on the Tibetan Plateau, China

“…The runoff simulations are produced after the parameters have been calibrated. Because WRF-Hydro includes many parameterized nonlinear physical processes, the default parameters given by WRF-Hydro are only valid over a small region; calibration of related model parameters is often required to use in the new domain [66].…”