Assimilation of SMOS soil moisture and brightness temperature products into a land surface model

Lievens, Hans; Lannoy, Gabriëlle J. M. De; Bitar, Ahmad Al; Drusch, Matthias; Dumedah, Gift; Franssen, Harrie‐Jan Hendricks; Kerr, Yann H.; Tomer, Sat Kumar; Martens, Brecht; Merlin, Olivier; Pan, Ming; Roundy, Joshua K.; Vereecken, Harry; Walker, Jeffrey P.; Wood, Eric F.; Verhoest, Niko; Pauwels, Valentijn R. N.

doi:10.1016/j.rse.2015.10.033

Cited by 75 publications

(79 citation statements)

References 63 publications

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“…Calibrating the radiative transfer model to closely match the observed time series is a possible solution, as shown by ), De Lannoy et al (2013 and Lievens et al (2015a), with the alternative being the rescaling of the measurements to mimic more closely the forward simulations (Lievens et al, 2015b), as mentioned above. The details of preparing the observations prior to assimilation are given here.…”

Section: Observations and Anomaly Preparationmentioning

confidence: 99%

“…For plants, these deeper layers act as the root zone, where soil moisture has a profound effect on biochemical processes, thus limiting the effect of data assimilation not only to soil moisture . Examples for assimilating SMOS soil moisture retrievals are, among others, given by Martens et al (2016), showing that the GLEAM evapotranspiration model can benefit from assimilating these data over Australia, or Lievens et al (2015b), who conclude that the positive assimilation impact on soil moisture can improve streamflow simulations for the variable infiltration capacity (VIC) model, as shown in the Murray-Darling Basin. The impact on both streamflow and evaporation is evaluated by Ridler et al (2014) for western Denmark.…”

Section: Introductionmentioning

confidence: 99%

“…This inevitably leads to cross-correlated errors between the model and the retrievals, which may have a negative impact on the assimilation performance (De Lannoy and Reichle, 2016a). Some examples of brightness temperature assimilation studies are given by Jia et al (2009), Muñoz-Sabater (2015, De Lannoy and Reichle (2016a) and Lievens et al (2016). Taken as a whole, assimilating L-band brightness temperatures in practical terms is quite a new concept which still needs further exploring.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, soil moisture information based on satellite data is often advertised as being especially useful for monitoring hydrological extremes such as floods and droughts, both of which Australia is susceptible to Johnson et al, 2016;Kiem et al, 2016). In addition to the ones already mentioned, a number of studies have focused on Australia, covering soil moisture retrieval (Van der Schalie et al, 2015), assimilation studies (Lievens et al, 2015b), validation studies and field campaigns for SMOS (Peischl et al, 2009;Panciera et al, 2008), as well as SMAP (Panciera et al, 2014) and soil moisture downscaling experiments (Piles et al, 2011;Merlin et al, 2012;Dumedah et al, 2015). More recently, SMOS soil moisture and GRACE water storage data have been jointly assimilated by Tian et al (2017).…”

Section: Introductionmentioning

confidence: 99%

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SMOS brightness temperature assimilation into the Community Land Model

Rains

Han²,

Lievens

et al. 2017

Hydrol. Earth Syst. Sci.

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Abstract. SMOS (Soil Moisture and Ocean Salinity mission) brightness temperatures at a single incident angle are assimilated into the Community Land Model (CLM) across Australia to improve soil moisture simulations. Therefore, the data assimilation system DasPy is coupled to the local ensemble transform Kalman filter (LETKF) as well as to the Community Microwave Emission Model (CMEM). Brightness temperature climatologies are precomputed to enable the assimilation of brightness temperature anomalies, making use of 6 years of SMOS data (2010)(2011)(2012)(2013)(2014)(2015). Mean correlation R with in situ measurements increases moderately from 0.61 to 0.68 (11 %) for upper soil layers if the root zone is included in the updates. A reduced improvement of 5 % is achieved if the assimilation is restricted to the upper soil layers. Root-zone simulations improve by 7 % when updating both the top layers and root zone, and by 4 % when only updating the top layers. Mean increments and increment standard deviations are compared for the experiments. The longterm assimilation impact is analysed by looking at a set of quantiles computed for soil moisture at each grid cell. Within hydrological monitoring systems, extreme dry or wet conditions are often defined via their relative occurrence, adding great importance to assimilation-induced quantile changes. Although still being limited now, longer L-band radiometer time series will become available and make model output improved by assimilating such data that are more usable for extreme event statistics.

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Section: Observations and Anomaly Preparationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

SMOS brightness temperature assimilation into the Community Land Model

Rains

Han²,

Lievens

et al. 2017

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

show abstract

“…In several data assimilation studies, the problem of differences in climatologies is resolved by bias-correcting the observations towards the model (e.g., Crow et al, 2005;Kumar et al, 2014;Lievens et al, 2015a, b;Martens et al, 2016;Reichle and Koster, 2004;Sahoo et al, 2013;Verhoest et al, 2015). Yet, such (statistical) operations may not be appropriate for scaling studies.…”

Section: Modeling Framework Requirementsmentioning

confidence: 99%

Scaling, similarity, and the fourth paradigm for hydrology

Peters‐Lidard

Clark

Samaniego

et al. 2017

Hydrol. Earth Syst. Sci.

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Abstract. In this synthesis paper addressing hydrologic scaling and similarity, we posit that roadblocks in the search for universal laws of hydrology are hindered by our focus on computational simulation (the third paradigm) and assert that it is time for hydrology to embrace a fourth paradigm of dataintensive science. Advances in information-based hydrologic science, coupled with an explosion of hydrologic data and advances in parameter estimation and modeling, have laid the foundation for a data-driven framework for scrutinizing hydrological scaling and similarity hypotheses. We summarize important scaling and similarity concepts (hypotheses) that require testing; describe a mutual information framework for testing these hypotheses; describe boundary condition, state, flux, and parameter data requirements across scales to support testing these hypotheses; and discuss some challenges to overcome while pursuing the fourth hydrological paradigm. We call upon the hydrologic sciences community to develop a focused effort towards adopting the fourth paradigm and apply this to outstanding challenges in scaling and similarity.

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L band microwave remote sensing and land data assimilation improve the representation of prestorm soil moisture conditions for hydrologic forecasting

Crow

Chen

Reichle

et al. 2017

Geophysical Research Letters

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Recent advances in remote sensing and land data assimilation purport to improve the quality of antecedent soil moisture information available for operational hydrologic forecasting. We objectively validate this claim by calculating the strength of the relationship between storm‐scale runoff ratio (i.e., total streamflow divided by total rainfall accumulation in depth units) and prestorm surface soil moisture estimates from a range of surface soil moisture data products. Results demonstrate that both satellite‐based, L band microwave radiometry and the application of land data assimilation techniques have significantly improved the utility of surface soil moisture data sets for forecasting streamflow response to future rainfall events.

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Assimilation of SMOS soil moisture and brightness temperature products into a land surface model

Cited by 75 publications

References 63 publications

SMOS brightness temperature assimilation into the Community Land Model

SMOS brightness temperature assimilation into the Community Land Model

Scaling, similarity, and the fourth paradigm for hydrology

L band microwave remote sensing and land data assimilation improve the representation of prestorm soil moisture conditions for hydrologic forecasting

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