Correction of systematic model forcing bias of CLM using assimilation of cosmic-ray Neutrons and land surface temperature: a study in the Heihe Catchment, China

Han, Xiaoyang; Franssen, Harrie‐Jan Hendricks; Rosolem, Rafael; Jin, Rui; Li, Xiaoqiong; Vereecken, Harry

doi:10.5194/hess-19-615-2015

Cited by 31 publications

(33 citation statements)

References 62 publications

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“…A more detailed description of the LETKF can be found in Hunt et al, (2007), and details on the implementation of the LETKF in combination with CLM are given by Han et al (2015).…”

Section: Data Assimilationmentioning

confidence: 99%

“…The COSMIC operator was implemented in a python interface that couples the land surface model CLM and the LETKF for joint state-parameter updating (Han et al, 2015). Neutron counts measured by CRNSs have been used in data assimilation studies to update model states (Han et al, 2015;Rosolem et al, 2014). Soil hydraulic parameters were also updated by assimilation of neutron counts in one synthetic study (Han et al, 2016), showing its feasibility.…”

Section: Introductionmentioning

confidence: 99%

“…The surface soil moisture information was propagated into greater soil depth than only the measurement depth using COSMIC in combination with data assimilation (Rosolem et al, 2014). The COSMIC operator was implemented in a python interface that couples the land surface model CLM and the LETKF for joint state-parameter updating (Han et al, 2015). Neutron counts measured by CRNSs have been used in data assimilation studies to update model states (Han et al, 2015;Rosolem et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

“…Montzka et al, 2011), because for high-dimensional problems the EnKF requires a much smaller ensemble size to achieve reasonably good predictions. The ensemble Kalman filter (Reichle et al, 2002a;Dunne and Entekhabi, 2005;Crow, 2003;De Lannoy and Reichle, 2016), variants such as the extended Kalman filter (Draper et al, 2009;Reichle et al, 2002b) and the local ensemble transform Kalman filter (Han et al, 2013(Han et al, , 2015 were applied for updating soil moisture states in land surface models. Reichle et al (2002a) performed a synthetic experiment using L-band microwave observations of the Southern Great Plains Hydrology Experiment (Jackson et al, 1999) to analyse the effect of ensemble size and forecast errors.…”

Section: Introductionmentioning

confidence: 99%

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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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“…A more detailed description of the LETKF can be found in Hunt et al, (2007), and details on the implementation of the LETKF in combination with CLM are given by Han et al (2015).…”

Section: Data Assimilationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Baatz¹,

Franssen²,

Han³

et al. 2017

Hydrol. Earth Syst. Sci.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Ragab, 1995;Calvet and Noilhan, 2000;Walker et al, 2001;Heathman et al, 2003). Soil moisture collected from cosmic-ray neutron probe installations have also been assimilated into models with similar complexity, such as the NOAH land surface model (Shuttleworth et al, 2013;Rosolem et al, 2014), Community Land Model (Han et al, 2015), and HYDRUS-1D (Rivera Villareyes et al, 2014). In the present study we combine the cosmic-ray neutron probe data with a simpler modelling approach, termed the exponential filter method or soil water index (SWI) method.…”

Section: A M Peterson Et Almentioning

confidence: 99%

Estimating field-scale root zone soil moisture using the cosmic-ray neutron probe

Peterson

Helgason

Ireson

2016

Hydrol. Earth Syst. Sci.

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Abstract. Many practical hydrological, meteorological, and agricultural management problems require estimates of soil moisture with an areal footprint equivalent to field scale, integrated over the entire root zone. The cosmic-ray neutron probe is a promising instrument to provide field-scale areal coverage, but these observations are shallow and require depth-scaling in order to be considered representative of the entire root zone. A study to identify appropriate depthscaling techniques was conducted at a grazing pasture site in central Saskatchewan, Canada over a 2-year period. Areaaveraged soil moisture was assessed using a cosmic-ray neutron probe. Root zone soil moisture was measured at 21 locations within the 500 m × 500 m study area, using a downhole neutron probe. The cosmic-ray neutron probe was found to provide accurate estimates of field-scale surface soil moisture, but measurements represented less than 40 % of the seasonal change in root zone storage due to its shallow measurement depth. The root zone estimation methods evaluated were: (a) the coupling of the cosmic-ray neutron probe with a time-stable neutron probe monitoring location, (b) coupling the cosmic-ray neutron probe with a representative landscape unit monitoring approach, and (c) convolution of the cosmicray neutron probe measurements with the exponential filter. The time stability method provided the best estimate of root zone soil moisture (RMSE = 0.005 cm 3 cm −3 ), followed by the exponential filter (RMSE = 0.014 cm 3 cm −3 ). The landscape unit approach, which required no calibration, had a negative bias but estimated the cumulative change in storage reasonably. The feasibility of applying these methods to field sites without existing instrumentation is discussed. Based upon its observed performance and its minimal data requirements, it is concluded that the exponential filter method has the most potential for estimating root zone soil moisture from cosmic-ray neutron probe data.

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Soil moisture background error covariance and data assimilation in a coupled land‐atmosphere model

Lin

Ebtehaj

Wang

et al. 2017

Water Resources Research

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This study characterizes the space-time structure of soil moisture background error covariance and paves the way for the development of a soil moisture variational data assimilation system for the Noah land surface model coupled to the Weather Research and Forecasting (WRF) model. The soil moisture background error covariance over the contiguous United States exhibits strong seasonal and regional variability with the largest values occurring in the uppermost soil layer during the summer. Large background error biases were identified, particularly over the southeastern United States, caused mainly by the discrepancy between the WRF-Noah simulations and the initial conditions derived from the used operational global analysis data set.

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Correction of systematic model forcing bias of CLM using assimilation of cosmic-ray Neutrons and land surface temperature: a study in the Heihe Catchment, China

Cited by 31 publications

References 62 publications

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

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

Estimating field-scale root zone soil moisture using the cosmic-ray neutron probe

Soil moisture background error covariance and data assimilation in a coupled land‐atmosphere model

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