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Simulating Temperature in a Soil Incubation Experiment
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[Assessment for spatial uncertainty of daily minimum temperature by using sequential Gaussian simulation].

Guo-Feng Zhang, Ming-Kai Qu, Zhao-Jin Cheng

    Ying Yong Sheng Tai Xue Bao = the Journal of Applied Ecology
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    Summary

    Sequential Gaussian simulation (SGS) better maps daily minimum temperature spatial distribution than Kriging methods. SGS overcomes Kriging

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    Area of Science:

    • Geosciences
    • Agricultural Meteorology
    • Spatial Statistics

    Background:

    • Daily minimum temperature is crucial for crop damage assessment and food security.
    • Kriging is common for temperature mapping but suffers from smoothing effects, overestimating low and underestimating high values.
    • Accurate spatial temperature data is vital for managing cold air events and agro-meteorological disasters.

    Purpose of the Study:

    • To evaluate the prediction accuracy of Ordinary Kriging (OK) and Kriging with External Drift (KED) for daily minimum temperature.
    • To compare the spatial distribution maps generated by OK and Sequential Gaussian Simulation (SGS).
    • To assess the suitability of SGS for reflecting true spatial temperature distribution and quantifying uncertainty in low-temperature events.

    Main Methods:

    • Cross-validation was used to assess the prediction accuracy of OK and KED.
    • Spatial distribution maps were generated using OK and SGS for daily minimum temperature data on Hainan Island during a cold air event.
    • The ability of SGS to reproduce data distribution and variance, and overcome Kriging's smoothing effect was analyzed.

    Main Results:

    • KED (r = 0.86) did not show significantly superior prediction accuracy compared to OK (r = 0.86).
    • Sequential Gaussian Simulation (SGS) generated multiple realizations that reproduced the original data's distribution and variance.
    • SGS effectively overcame the smoothing effect inherent in Kriging, providing a more accurate spatial representation of minimum temperatures.

    Conclusions:

    • SGS provides a more realistic spatial distribution of daily minimum temperature compared to Kriging methods.
    • SGS can quantify spatial uncertainty in potential chilling damage areas through multiple simulation realizations.
    • Sequential Gaussian Simulation is a valuable tool for assessing agro-meteorological disasters caused by low temperatures.