Land surface model calibration through microwave data assimilation for improving soil moisture simulations

Yang, Kun; La, Zhu; Chen, Yingying; Zhao, Long; Qin, Jun; Lu, Hui; Tang, Wenjun; Han, Menglei; Ding, Bei; Fang, Nan

doi:10.1016/j.jhydrol.2015.12.018

Cited by 90 publications

(71 citation statements)

References 52 publications

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“…from Global Land Data Assimilation System (GLDAS) models (CLM, MOS, NOAH, and VIC) for 2003-2014(Rodell et al, 2004) and Dual-Pass Microwave Land Data Assimilation System (DPMLDAS) during 2003-2010 (K Yang et al, 2007Yang et al, , 2016. and glacier mass balance observations at the Xiaodongkemadi and Gurenhekou glaciers(Yao et al, 2012;Figure 1)were also used to evaluate the performance of the VIC-glacier model over the UYA, UYE, and UB basins.…”

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confidence: 99%

Changes in Terrestrial Water Storage During 2003–2014 and Possible Causes in Tibetan Plateau

Meng

et al. 2019

JGR Atmospheres

104

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The spatial‐temporal changes in terrestrial water storage (TWS) over the Tibetan Plateau (TP) and six selected basins during 2003–2014 were analyzed by applying the Gravity Recovery and Climate Experiment data and the extended Variable Infiltration Capacity‐glacier model, including the upstream of Yangtze (UYA), Yellow (UYE), Brahmaputra (UB), and Indus river basins and the Inner TP and the Qaidam Basin. The possible causes of TWS changes were investigated from the perspective of surface water balance and TWS components through multisource data and the Variable Infiltration Capacity‐glacier model. There was a strong spatial heterogeneity in changes of Gravity Recovery and Climate Experiment TWS in the TP—with apparent mass accumulation in central and northern TP and a sharp decreasing trend in southern and northwestern TP. The TWS changes in the TP were mostly attributed to variations in precipitation and evapotranspiration from the perspective of land‐surface water balance. Precipitation played a dominant role on the TWS accumulation in the UYA and UYE, while evapotranspiration had a more important role than precipitation in TWS depletion in the UB. From the perspective of TWS components, the TWS increase in the UYA and UYE was mainly caused by an increase in soil moisture, whereas the decrease in TWS in the UB was mostly due to glacier mass loss. TWS was accumulating from March through August in southeastern TP while from November to April/May in northwestern TP. The seasonal variations of TWS are highly modulated by the large‐scale climate system, atmospheric moisture flux, and precipitation regime over the TP.

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confidence: 99%

Changes in Terrestrial Water Storage During 2003–2014 and Possible Causes in Tibetan Plateau

Meng

et al. 2019

JGR Atmospheres

104

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“…The mean value of the estimated and default parameters (sand content, clay content, soil porosity, SOC fraction and RMS) for all the 16 AMSR-E grids within the experiment area are displayed in Figure 5. The parameters measurements are the average values derived from a number of soil samples as described by Yang et al [66]. First, five parameters showed a large discrepancy with the default parameters which indicated the necessity of performing parameters estimation and demonstrated the effect of TBs for update these parameters.…”

Section: Evaluation Of Parameters Estimationmentioning

confidence: 99%

Improving Soil Moisture Estimation with a Dual Ensemble Kalman Smoother by Jointly Assimilating AMSR-E Brightness Temperature and MODIS LST

et al. 2017

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Uncertainties in model parameters can easily result in systematic differences between model states and observations, which significantly affect the accuracy of soil moisture estimation in data assimilation systems. In this research, a soil moisture assimilation scheme is developed to jointly assimilate AMSR-E (Advanced Microwave Scanning Radiometer-Earth Observing System) brightness temperature (TB) and MODIS (Moderate Resolution Imaging Spectroradiometer) Land Surface Temperature (LST) products, which also corrects model bias by simultaneously updating model states and parameters with a dual ensemble Kalman filter (DEnKS). Common Land Model (CoLM) and a Radiative Transfer Model (RTM) are adopted as model and observation operator, respectively. The assimilation experiment was conducted in Naqu on the Tibet Plateau from 31 May to 27 September 2011. The updated soil temperature at surface obtained by assimilating MODIS LST serving as inputs of RTM is to reduce the differences between the simulated and observed TB, then AMSR-E TB is assimilated to update soil moisture and model parameters. Compared with in situ measurements, the accuracy of soil moisture estimation derived from the assimilation experiment has been tremendously improved at a variety of scales. The updated parameters effectively reduce the states bias of CoLM. The results demonstrate the potential of assimilating AMSR-E TB and MODIS LST to improve the estimation of soil moisture and related parameters. Furthermore, this study indicates that the developed scheme is an effective way to retrieve downscaled soil moisture when assimilating the coarse-scale microwave TB.

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“…over one or more whole years) this allows us to avoid seasonally varying parameters. Using variational methods to assimilate remotely sensed observations for land surface model parameter estimation has previously been shown to improve soil moisture estimates in several studies (Yang et al, 2007Rasmy et al, 2011;Sawada and Koike, 2014;Yang et al, 2016). These studies all optimise both model parameters and state.…”

Section: Introductionmentioning

confidence: 99%

Impact of remotely sensed soil moisture and precipitation on soil moisture prediction in a data assimilation system with the JULES land surface model

Pinnington

Quaife

Black

2018

Hydrol. Earth Syst. Sci.

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We show that satellite-derived estimates of shallow soil moisture can be used to calibrate a land surface model at the regional scale in Ghana, using data assimilation techniques. The modified calibration significantly improves model estimation of soil moisture. Specifically, we find an 18 % reduction in unbiased root-mean-squared differences in the north of Ghana and a 21 % reduction in the south of Ghana for a 5-year hindcast after assimilating a single year of soil moisture observations to update model parameters. The use of an improved remotely sensed rainfall dataset contributes to 6 % of this reduction in deviation for northern Ghana and 10 % for southern Ghana. Improved rainfall data have the greatest impact on model estimates during the seasonal wetting-up of soil, with the assimilation of remotely sensed soil moisture having greatest impact during dryingdown. In the north of Ghana we are able to recover improved estimates of soil texture after data assimilation. However, we are unable to do so for the south. The significant reduction in unbiased root-mean-squared difference we find after assimilating a single year of observations bodes well for the production of improved land surface model soil moisture estimates over sub-Saharan Africa.

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Land surface model calibration through microwave data assimilation for improving soil moisture simulations

Cited by 90 publications

References 52 publications

Changes in Terrestrial Water Storage During 2003–2014 and Possible Causes in Tibetan Plateau

Changes in Terrestrial Water Storage During 2003–2014 and Possible Causes in Tibetan Plateau

Improving Soil Moisture Estimation with a Dual Ensemble Kalman Smoother by Jointly Assimilating AMSR-E Brightness Temperature and MODIS LST

Impact of remotely sensed soil moisture and precipitation on soil moisture prediction in a data assimilation system with the JULES land surface model

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