A comparison of methods for a priori bias correction in soil moisture data assimilation

Kumar, Sujay V.; Reichle, Rolf H.; Harrison, Kenneth W.; Peters‐Lidard, Christa D.; Yatheendradas, Soni; Santanello, Joseph A.

doi:10.1029/2010wr010261

Cited by 143 publications

(128 citation statements)

References 70 publications

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“…All evaluation indices were determined for both CLM-Ref and for the ensemble mean of CLM-Ens P . Post et al (2016) showed that parameters estimated for the forest sites WÜ and FR-Fon are well transferable to other FLUXNET sites of corresponding PFTs located more than 600 km from WÜ and FR-Fon: Tharandt in Germany (DE-Tha; e.g., Grünwald and Bernhofer, 2007) and Hainich in Germany (DE-Hai; e.g., Knohl et al, 2003). Because WÜ is the only forested EC tower site in the Rur-catchment and no additional EC towers were available for the validation of the estimated parameters, we assume the parameter estimates provide more reliable NEE data for a large part of the forested area in the Rur catchment.…”

Section: Evaluation Of Parameter Estimates With Eddy Covariance Nee Datamentioning

confidence: 92%

Evaluation and uncertainty analysis of regional-scale CLM4.5 net carbon flux estimates

Post

Franssen²,

Han³

et al. 2018

Biogeosciences

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Abstract. Modeling net ecosystem exchange (NEE) at the regional scale with land surface models (LSMs) is relevant for the estimation of regional carbon balances, but studies on it are very limited. Furthermore, it is essential to better understand and quantify the uncertainty of LSMs in order to improve them. An important key variable in this respect is the prognostic leaf area index (LAI), which is very sensitive to forcing data and strongly affects the modeled NEE. We applied the Community Land Model (CLM4.5-BGC) to the Rur catchment in western Germany and compared estimated and default ecological key parameters for modeling carbon fluxes and LAI. The parameter estimates were previously estimated with the Markov chain Monte Carlo (MCMC) approach DREAM (zs) for four of the most widespread plant functional types in the catchment. It was found that the catchment-scale annual NEE was strongly positive with default parameter values but negative (and closer to observations) with the estimated values. Thus, the estimation of CLM parameters with local NEE observations can be highly relevant when determining regional carbon balances. To obtain a more comprehensive picture of model uncertainty, CLM ensembles were set up with perturbed meteorological input and uncertain initial states in addition to uncertain parameters. C 3 grass and C 3 crops were particularly sensitive to the perturbed meteorological input, which resulted in a strong increase in the standard deviation of the annual NEE sum (σ NEE ) for the different ensemble members from ∼ 2 to 3 g C m −2 yr −1 (with uncertain parameters) to ∼ 45 g C m −2 yr −1 (C 3 grass) and ∼ 75 g C m −2 yr −1 (C 3 crops) with perturbed forcings. This increase in uncertainty is related to the impact of the meteorological forcings on leaf onset and senescence, and enhanced/reduced drought stress related to perturbation of precipitation. The NEE uncertainty for the forest plant functional type (PFT) was considerably lower (σ NEE ∼ 4.0-13.5 g C m −2 yr −1 with perturbed parameters, meteorological forcings and initial states). We conclude that LAI and NEE uncertainty with CLM is clearly underestimated if uncertain meteorological forcings and initial states are not taken into account.

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Section: Evaluation Of Parameter Estimates With Eddy Covariance Nee Datamentioning

confidence: 92%

Evaluation and uncertainty analysis of regional-scale CLM4.5 net carbon flux estimates

Post

Franssen²,

Han³

et al. 2018

Biogeosciences

View full text Add to dashboard Cite

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“…Correlation in time was introduced with an AR(1) model with autoregressive parameter 0.33. These correlations and standard deviations were chosen based on previous data assimilation experiments (Reichle et al, 2010;Kumar et al, 2012;De Lannoy et al, 2012;Han et al, 2015). In this work, only results for precipitation perturbation with σ = 0.5 will be shown, as results for σ = 1.0 were similar.…”

Section: Model Ensemblementioning

confidence: 99%

Evaluation of a cosmic-ray neutron sensor network for improved land surface model prediction

Baatz¹,

Franssen²,

Han³

et al. 2017

Hydrol. Earth Syst. Sci.

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Abstract. In situ soil moisture sensors provide highly accurate but very local soil moisture measurements, while remotely sensed soil moisture is strongly affected by vegetation and surface roughness. In contrast, cosmic-ray neutron sensors (CRNSs) allow highly accurate soil moisture estimation on the field scale which could be valuable to improve land surface model predictions. In this study, the potential of a network of CRNSs installed in the 2354 km 2 Rur catchment (Germany) for estimating soil hydraulic parameters and improving soil moisture states was tested. Data measured by the CRNSs were assimilated with the local ensemble transform Kalman filter in the Community Land Model version 4.5. Data of four, eight and nine CRNSs were assimilated for the years 2011 and 2012 (with and without soil hydraulic parameter estimation), followed by a verification year 2013 without data assimilation. This was done using (i) a regional high-resolution soil map, (ii) the FAO soil map and (iii) an erroneous, biased soil map as input information for the simulations. For the regional soil map, soil moisture characterization was only improved in the assimilation period but not in the verification period. For the FAO soil map and the biased soil map, soil moisture predictions improved strongly to a root mean square error of 0.03 cm 3 cm −3 for the assimilation period and 0.05 cm 3 cm −3 for the evaluation period. Improvements were limited by the measurement error of CRNSs (0.03 cm 3 cm −3 ). The positive results obtained with data assimilation of nine CRNSs were confirmed by the jackknife experiments with four and eight CRNSs used for assimilation. The results demonstrate that assimilated data of a CRNS network can improve the characterization of soil moisture content on the catchment scale by updating spatially distributed soil hydraulic parameters of a land surface model.

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“…The DA method has been widely applied in hydrology for soil moisture estimation (Han et al, 2012;Kumar et al, 2012;Yan et al, 2015) and flood forecasting (Y. Liu et al, 2012;Abaza et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Identification of hydrological model parameter variation using ensemble Kalman filter

Deng

Liu

Guo

et al. 2016

Hydrol. Earth Syst. Sci.

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Abstract. Hydrological model parameters play an important role in the ability of model prediction. In a stationary context, parameters of hydrological models are treated as constants; however, model parameters may vary with time under climate change and anthropogenic activities. The technique of ensemble Kalman filter (EnKF) is proposed to identify the temporal variation of parameters for a two-parameter monthly water balance model (TWBM) by assimilating the runoff observations. Through a synthetic experiment, the proposed method is evaluated with time-invariant (i.e., constant) parameters and different types of parameter variations, including trend, abrupt change and periodicity. Various levels of observation uncertainty are designed to examine the performance of the EnKF. The results show that the EnKF can successfully capture the temporal variations of the model parameters. The application to the Wudinghe basin shows that the water storage capacity (SC) of the TWBM model has an apparent increasing trend during the period from 1958 to 2000. The identified temporal variation of SC is explained by land use and land cover changes due to soil and water conservation measures. In contrast, the application to the Tongtianhe basin shows that the estimated SC has no significant variation during the simulation period of 1982-2013, corresponding to the relatively stationary catchment properties. The evapotranspiration parameter (C) has temporal variations while no obvious change patterns exist. The proposed method provides an effective tool for quantifying the temporal variations of the model parameters, thereby improving the accuracy and reliability of model simulations and forecasts.

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A comparison of methods for a priori bias correction in soil moisture data assimilation

Cited by 143 publications

References 70 publications

Evaluation and uncertainty analysis of regional-scale CLM4.5 net carbon flux estimates

Evaluation and uncertainty analysis of regional-scale CLM4.5 net carbon flux estimates

Evaluation of a cosmic-ray neutron sensor network for improved land surface model prediction

Identification of hydrological model parameter variation using ensemble Kalman filter

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