气候变化领域集成服务门户(CCPortal): Assimilation of Soil Moisture and Ocean Salinity (SMOS) brightness temperature into a large-scale distributed conceptual hydrological model to improve soil moisture predictions: The Murray–Darling basin in Australia as a test case

CCPortal

DOI	10.5194/hess-24-4793-2020
	Assimilation of Soil Moisture and Ocean Salinity (SMOS) brightness temperature into a large-scale distributed conceptual hydrological model to improve soil moisture predictions: The Murray–Darling basin in Australia as a test case
	Hostache R.; Rains D.; Mallick K.; Chini M.; Pelich R.; Lievens H.; Fenicia F.; Corato G.; Verhoest N.E.C.; Matgen P.
发表日期	2020
ISSN	1027-5606
起始页码	4793
结束页码	4812
卷号	24 期号:10
英文摘要	The main objective of this study is to investigate how brightness temperature observations from satellite microwave sensors may help to reduce errors and uncertainties in soil moisture and evapotranspiration simulations with a large-scale conceptual hydro-meteorological model. In addition, this study aims to investigate whether such a conceptual modelling framework, relying on parameter calibration, can reach the performance level of more complex physically based models for soil moisture simulations at a large scale. We use the ERA-Interim publicly available forcing data set and couple the Community Microwave Emission Modelling (CMEM) platform radiative transfer model with a hydro-meteorological model to enable, therefore, soil moisture, evapotranspiration and brightness temperature simulations over the Murray–Darling basin in Australia. The hydrometeorological model is configured using recent developments in the SUPERFLEX framework, which enables tailoring the model structure to the specific needs of the application and to data availability and computational requirements. The hydrological model is first calibrated using only a sample of the Soil Moisture and Ocean Salinity (SMOS) brightness temperature observations (2010–2011). Next, SMOS brightness temperature observations are sequentially assimilated into the coupled SUPERFLEX–CMEM model (2010–2015). For this experiment, a local ensemble transform Kalman filter is used. Our empirical results show that the SUPERFLEX–CMEM modelling chain is capable of predicting soil moisture at a performance level similar to that obtained for the same study area and with a quasi-identical experimental set-up using the Community Land Model (CLM) . This shows that a simple model, when calibrated using globally and freely available Earth observation data, can yield performance levels similar to those of a physically based (uncalibrated) model. The correlation between simulated and in situ observed soil moisture ranges from 0.62 to 0.72 for the surface and root zone soil moisture. The assimilation of SMOS brightness temperature observations into the SUPERFLEX–CMEM modelling chain improves the correlation between predicted and in situ observed surface and root zone soil moisture by 0.03 on average, showing improvements similar to those obtained using the CLM land surface model. Moreover, at the same time the assimilation improves the correlation between predicted and in situ observed monthly evapotranspiration by 0.02 on average. © Author(s) 2020. This work is distributed under the Creative Commons Attribution 4.0 License.
语种	英语
scopus关键词	Atmospheric temperature; Climate models; Evapotranspiration; Hydrology; Luminance; Microwave sensors; Radiative transfer; Brightness temperatures; Computational requirements; Meteorological modeling; Physically based models; Radiative transfer model; Root zone soil moistures; Soil Moisture and Ocean Salinity (SMOS); Soil moisture predictions; Soil moisture; brightness temperature; calibration; correlation; data assimilation; evapotranspiration; experimental study; Kalman filter; land surface; radiative transfer; rhizosphere; SMOS; surface temperature; Australia; Murray-Darling Basin
来源期刊	Hydrology and Earth System Sciences
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/159289
作者单位	Hostache, R., Department Environmental Research and Innovation, Luxembourg Institute of Science and Technology (LIST), Belvaux, Luxembourg; Rains, D., Department of Environment, Ghent University, Ghent, Belgium, Department of Physics and Astronomy, Earth Observation Science, University of Leicester, Leicester, United Kingdom; Mallick, K., Department Environmental Research and Innovation, Luxembourg Institute of Science and Technology (LIST), Belvaux, Luxembourg; Chini, M., Department Environmental Research and Innovation, Luxembourg Institute of Science and Technology (LIST), Belvaux, Luxembourg; Pelich, R., Department Environmental Research and Innovation, Luxembourg Institute of Science and Technology (LIST), Belvaux, Luxembourg; Lievens, H., Department of Environment, Ghent University, Ghent, Belgium, Department of Earth and Environmental Sciences, Katholieke Universiteit Leuven, Heverlee, Belgium; Fenicia, F., Department of Systems Analysis, Integrated Assessment and Modelling, Swiss Federal Institute...
推荐引用方式 GB/T 7714	Hostache R.,Rains D.,Mallick K.,等. Assimilation of Soil Moisture and Ocean Salinity (SMOS) brightness temperature into a large-scale distributed conceptual hydrological model to improve soil moisture predictions: The Murray–Darling basin in Australia as a test case[J],2020,24(10).
APA	Hostache R..,Rains D..,Mallick K..,Chini M..,Pelich R..,...&Matgen P..(2020).Assimilation of Soil Moisture and Ocean Salinity (SMOS) brightness temperature into a large-scale distributed conceptual hydrological model to improve soil moisture predictions: The Murray–Darling basin in Australia as a test case.Hydrology and Earth System Sciences,24(10).
MLA	Hostache R.,et al."Assimilation of Soil Moisture and Ocean Salinity (SMOS) brightness temperature into a large-scale distributed conceptual hydrological model to improve soil moisture predictions: The Murray–Darling basin in Australia as a test case".Hydrology and Earth System Sciences 24.10(2020).