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首页> 外文期刊>Hydrology and Earth System Sciences Discussions >Improving soil moisture prediction of a high-resolution land surface model by parameterising pedotransfer functions through assimilation of SMAP satellite data
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Improving soil moisture prediction of a high-resolution land surface model by parameterising pedotransfer functions through assimilation of SMAP satellite data

机译：通过同化SMAP卫星数据来改善PEDOTRANSFER功能的高分辨率陆地表面模型的土壤水分预测

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Pedotransfer functions are used to relate gridded databases of soil texture information to the soil hydraulic and thermal parameters of land surface models. The parameters within these pedotransfer functions are uncertain and calibrated through analyses of point soil samples. How these calibrations relate to the soil parameters at the spatial scale of modern land surface models is unclear because gridded databases of soil texture represent an area average. We present a novel approach for calibrating such pedotransfer functions to improve land surface model soil moisture prediction by using observations from the Soil Moisture Active Passive (SMAP) satellite mission within a data assimilation framework. Unlike traditional calibration procedures, data assimilation always takes into account the relative uncertainties given to both model and observed estimates to find a maximum likelihood estimate. After performing the calibration procedure, we find improved estimates of soil moisture and heat flux for the Joint UK Land Environment Simulator (JULES) land surface model (run at a 1?km resolution) when compared to estimates from a cosmic-ray soil moisture monitoring network (COSMOS-UK) and three flux tower sites. The spatial resolution of the COSMOS probes is much more representative of the 1?km model grid than traditional point-based soil moisture sensors. For 11 cosmic-ray neutron soil moisture probes located across the modelled domain, we find an average 22?% reduction in root mean squared error, a 16?% reduction in unbiased root mean squared error and a 16?% increase in correlation after using data assimilation techniques to retrieve new pedotransfer function parameters.
机译：PEDOTRANSFER功能用于将土壤纹理信息的网格数据库与陆地模型的土壤液压和热参数联系起来。这些网兜传输功能中的参数通过点土样品分析不确定和校准。这些校准如何与现代陆地表面模型的空间尺度的土壤参数有何清楚，因为土壤纹理的网格数据库代表了面积平均值。我们介绍了一种新的方法，用于校准这种网兜传输功能，通过使用数据同化框架内的土壤湿度活跃被动（SMAP）卫星任务的观察来改善土地表面模型土壤水分预测。与传统的校准程序不同，数据同化始终考虑到模型的相对不确定性，并观察到估计值以找到最大的似然估计。在进行校准程序后，与宇宙射线水分监测的估计相比，我们发现对联合英国土地环境模拟器（Jules）陆地模型（Jules）陆地表面模型（以1克里分辨率运行）的改进的土壤水分和热通量估计网络（COSMOS-UK）和三个助焊塔网站。宇宙探针的空间分辨率比传统的基于点的土壤湿度传感器更重要地代表1 km模型网格。对于位于模拟结构域的11个宇宙射线中子水分探针中，我们发现均方根平均误差的平均减少了22？％，在非偏见的根部平均平方误差下降16？％，在使用后的相关性增加16？％数据同化技术检索新的网兜传输功能参数。

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