SVAT Modelling, Land Surface Parameterization, Soil Moisture and Scaling Issues

AbstractThe datasets of the five Land-offline Model Intercomparison Project (LMIP) experiments using the Chinese Academy of Sciences Land Surface Model (CAS-LSM) of CAS Flexible Global-Ocean-Atmosphere-Land System Model Grid-point version 3 (CAS FGOALS-g3) are presented in this study. These experiments were forced by five global meteorological forcing datasets, which contributed to the framework of the Land Surface Snow and Soil Moisture Model Intercomparison Project (LS3MIP) of CMIP6. These datasets have been released on the Earth System Grid Federation node. In this paper, the basic descriptions of the CAS-LSM and the five LMIP experiments are shown. The performance of the soil moisture, snow, and land-atmosphere energy fluxes was preliminarily validated using satellite-based observations. Results show that their mean states, spatial patterns, and seasonal variations can be reproduced well by the five LMIP simulations. It suggests that these datasets can be used to investigate the evolutionary mechanisms of the global water and energy cycles during the past century.

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A framework for quantifying hydrologic effects of soil structure across scales

Communications Earth & Environment ◽

10.1038/s43247-021-00180-0 ◽

2021 ◽

Vol 2 (1) ◽

Author(s):

Sara Bonetti ◽

Zhongwang Wei ◽

Dani Or

Keyword(s):

Soil Texture ◽

Land Surface ◽

Soil Structure ◽

Pedotransfer Functions ◽

Small Scale ◽

Spatial Correlations ◽

Surface Parameterization ◽

Exchange Processes ◽

Local Soil ◽

Spatially Distributed

AbstractEarth system models use soil information to parameterize hard-to-measure soil hydraulic properties based on pedotransfer functions. However, current parameterizations rely on sample-scale information which often does not account for biologically-promoted soil structure and heterogeneities in natural landscapes, which may significantly alter infiltration-runoff and other exchange processes at larger scales. Here we propose a systematic framework to incorporate soil structure corrections into pedotransfer functions, informed by remote-sensing vegetation metrics and local soil texture, and use numerical simulations to investigate their effects on spatially distributed and areal averaged infiltration-runoff partitioning. We demonstrate that small scale soil structure features prominently alter the hydrologic response emerging at larger scales and that upscaled parameterizations must consider spatial correlations between vegetation and soil texture. The proposed framework allows the incorporation of hydrological effects of soil structure with appropriate scale considerations into contemporary pedotransfer functions used for land surface parameterization.

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Evaluating Downscaling Factors of Microwave Satellite Soil Moisture Based on Machine Learning Method

Remote Sensing ◽

10.3390/rs13010133 ◽

2021 ◽

Vol 13 (1) ◽

pp. 133

Author(s):

Hao Sun ◽

Yajing Cui

Keyword(s):

Machine Learning ◽

Soil Moisture ◽

Land Surface ◽

Regional Scale ◽

Support Vector ◽

Machine Learning Method ◽

Learning Method ◽

Feed Forward Neural Network ◽

Comparative Performance ◽

Geographical Factors

Downscaling microwave remotely sensed soil moisture (SM) is an effective way to obtain spatial continuous SM with fine resolution for hydrological and agricultural applications on a regional scale. Downscaling factors and functions are two basic components of SM downscaling where the former is particularly important in the era of big data. Based on machine learning method, this study evaluated Land Surface Temperature (LST), Land surface Evaporative Efficiency (LEE), and geographical factors from Moderate Resolution Imaging Spectroradiometer (MODIS) products for downscaling SMAP (Soil Moisture Active and Passive) SM products. This study spans from 2015 to the end of 2018 and locates in the central United States. Original SMAP SM and in-situ SM at sparse networks and core validation sites were used as reference. Experiment results indicated that (1) LEE presented comparative performance with LST as downscaling factors; (2) adding geographical factors can significantly improve the performance of SM downscaling; (3) integrating LST, LEE, and geographical factors got the best performance; (4) using Z-score normalization or hyperbolic-tangent normalization methods did not change the above conclusions, neither did using support vector regression nor feed forward neural network methods. This study demonstrates the possibility of LEE as an alternative of LST for downscaling SM when there is no available LST due to cloud contamination. It also provides experimental evidence for adding geographical factors in the downscaling process.

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Estimating the aboveground biomass of coniferous forest in Northeast China using spectral variables, land surface temperature and soil moisture

The Science of The Total Environment ◽

10.1016/j.scitotenv.2021.147335 ◽

2021 ◽

Vol 785 ◽

pp. 147335

Author(s):

Fugen Jiang ◽

Mykola Kutia ◽

Kaisen Ma ◽

Song Chen ◽

Jiangping Long ◽

...

Keyword(s):

Soil Moisture ◽

Surface Temperature ◽

Land Surface Temperature ◽

Aboveground Biomass ◽

Land Surface ◽

Northeast China ◽

Coniferous Forest

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NOAA Satellite Soil Moisture Operational Product System (SMOPS) Version 3.0 Generates Higher Accuracy Blended Satellite Soil Moisture

Remote Sensing ◽

10.3390/rs12172861 ◽

2020 ◽

Vol 12 (17) ◽

pp. 2861

Author(s):

Jifu Yin ◽

Xiwu Zhan ◽

Jicheng Liu

Keyword(s):

Soil Moisture ◽

Real Time ◽

Land Surface ◽

Vital Role ◽

Product System ◽

Global Soil ◽

One Stop ◽

Satellite Sensors ◽

Blended Data

Soil moisture plays a vital role for the understanding of hydrological, meteorological, and climatological land surface processes. To meet the need of real time global soil moisture datasets, a Soil Moisture Operational Product System (SMOPS) has been developed at National Oceanic and Atmospheric Administration to produce a one-stop shop for soil moisture observations from all available satellite sensors. What makes the SMOPS unique is its near real time global blended soil moisture product. Since the first version SMOPS publicly released in 2010, the SMOPS has been updated twice based on the users’ feedbacks through improving retrieval algorithms and including observations from new satellite sensors. The version 3.0 SMOPS has been operationally released since 2017. Significant differences in climatological averages lead to remarkable distinctions in data quality between the newest and the older versions of SMOPS blended soil moisture products. This study reveals that the SMOPS version 3.0 has overwhelming advantages of reduced data uncertainties and increased correlations with respect to the quality controlled in situ measurements. The new version SMOPS also presents more robust agreements with the European Space Agency’s Climate Change Initiative (ESA_CCI) soil moisture datasets. With the higher accuracy, the blended data product from the new version SMOPS is expected to benefit the hydrological, meteorological, and climatological researches, as well as numerical weather, climate, and water prediction operations.

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