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首页> 外文期刊>Journal of hydrometeorology >Evaluation of NOAA National Water Model Parameter Calibration in Semiarid Environments Prone to Channel Infiltration
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Evaluation of NOAA National Water Model Parameter Calibration in Semiarid Environments Prone to Channel Infiltration

机译：Evaluation of NOAA National Water Model Parameter Calibration in Semiarid Environments Prone to Channel Infiltration

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摘要

The NOAA National Water Model (NWM), maintained and executed by the NOAA National Weather Service (NWS) Office of Water Prediction, provides operational hydrological guidance throughout the contiguous United States. Based on the WRF-Hydro model architecture developed by the National Center for Atmospheric Research (NCAR), the NWM was recently modified for semiarid domains, by permitting it to explicitly resolve infiltration from ephemeral channels into the underlying channel bed as an added model sink term. To analyze the added value of channel infiltration in semiarid environments, we calibrated NWM v2.1 (with the channel infiltration function) to 56 independent basins in the western CONUS, following identical calibration methods as the preoperational NWM v2.1 (not including channel infiltration). Calibration of the model consists of two parts, including 1) calibration of channel infiltration only with other parameters set to the calibrated parameters used for preoperational NWM v2.1 and 2) calibration of all parameters including channel infiltration with settings otherwise equivalent to the calibration of NWM v2.1. The calibrated channelinfiltration enhanced NWM improves predictive skill compared to the control NWM in 85% of evaluated basins, for the calibration period. The current NWM settings for physical processes and the biases of the calibration scheme limit model performance in semiarid environments. To explore whether channel infiltration paired with an alternative calibration scheme could address these limitations, NWM v2.1 was calibrated with a new objective function in selected basins. We found that this updated objective function could ameliorate model biases in some semiarid environments.

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