Version 1
: Received: 14 August 2024 / Approved: 14 August 2024 / Online: 14 August 2024 (14:53:42 CEST)
How to cite:
Guo, Y.; Yuan, B.; Shao, C.; Niu, G.; Xu, D.; Gao, Y. Calibration for Improving the Medium‐Range Soil Forecast over Central Tibet: Effects of Objective Metrics’ Diversity. Preprints2024, 2024081085. https://doi.org/10.20944/preprints202408.1085.v1
Guo, Y.; Yuan, B.; Shao, C.; Niu, G.; Xu, D.; Gao, Y. Calibration for Improving the Medium‐Range Soil Forecast over Central Tibet: Effects of Objective Metrics’ Diversity. Preprints 2024, 2024081085. https://doi.org/10.20944/preprints202408.1085.v1
Guo, Y.; Yuan, B.; Shao, C.; Niu, G.; Xu, D.; Gao, Y. Calibration for Improving the Medium‐Range Soil Forecast over Central Tibet: Effects of Objective Metrics’ Diversity. Preprints2024, 2024081085. https://doi.org/10.20944/preprints202408.1085.v1
APA Style
Guo, Y., Yuan, B., Shao, C., Niu, G., Xu, D., & Gao, Y. (2024). Calibration for Improving the Medium‐Range Soil Forecast over Central Tibet: Effects of Objective Metrics’ Diversity. Preprints. https://doi.org/10.20944/preprints202408.1085.v1
Chicago/Turabian Style
Guo, Y., Dongmei Xu and Yong Gao. 2024 "Calibration for Improving the Medium‐Range Soil Forecast over Central Tibet: Effects of Objective Metrics’ Diversity" Preprints. https://doi.org/10.20944/preprints202408.1085.v1
Abstract
The high spatial complexities of soil temperature modelling over semiarid land have challenged the calibration—predication framework, whose composited objective lacks comprehensive evaluation. Therefore, this study, based on the Noah land surface model and its full parameter table, utilizes two global searching algorithms and eight kinds of objective with dimensional—varied metrics, combined with dense site soil moisture and temperature observations of central Tibet, to explore different metrics’ performances on the spatial heterogeneity and uncertainty of regional land surface parameters, calibration efficiency and effectiveness, as well as spatiotemporal complexities in surface forecasting. Results have shown that metrics’ diversity has shown greater influence on the calibration—predication framework than the global searching algorithms themselves. Besides being significantly better than other metrics, the enhanced multi objective metric (EMO) and the enhanced Kling-Gupta efficiency (EKGE) have their own advantages and disadvantages in simulations and parameters respectively. Especially, EMO that composited with four metrics as correlated coefficient, root mean square error, mean absolute error, and Nash–Sutcliffe efficiency, has shown relatively balanced performance in surface forecasting when compared to EKGE. In general, the calibration—predication framework that benefited from EMO could greatly reduce the spatial complexities in soil temperature modelling of the semiarid land.
Keywords
metrics diversity; Kling-Gupta efficiency; soil temperature modelling; spatial complexity; land surface parameters
Subject
Engineering, Other
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
