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

The recent development of the non-invasive cosmic-ray soil moisture sensing technique fills the gap be- tween point-scale soil moisture measurements and regional- scale soil moisture measurements by remote sensing. A cosmic-ray probe measures soil moisture for a footprint with a diameter of 600 m (at sea level) and with an effective measurement depth between 12 and 76 cm, depending on the soil humidity. In this study, it was tested whether neu- tron counts also allow correcting for a systematic error in the model forcings. A lack of water management data of- ten causes systematic input errors to land surface models. Here, the assimilation procedure was tested for an irrigated corn field (Heihe Watershed Allied Telemetry Experimen- tal Research - HiWATER, 2012) where no irrigation data were available as model input although for the area a sig- nificant amount of water was irrigated. In the study, the mea- sured cosmic-ray neutron counts and Moderate-Resolution Imaging Spectroradiometer (MODIS) land surface tempera- ture (LST) products were jointly assimilated into the Com- munity Land Model (CLM) with the local ensemble trans- form Kalman filter. Different data assimilation scenarios were evaluated, with assimilation of LST and/or cosmic-ray neutron counts, and possibly parameter estimation of leaf area index (LAI). The results show that the direct assimila- tion of cosmic-ray neutron counts can improve the soil mois- ture and evapotranspiration (ET) estimation significantly, correcting for lack of information on irrigation amounts. The joint assimilation of neutron counts and LST could improve further the ET estimation, but the information content of neu- tron counts exceeded the one of LST. Additional improve- ment was achieved by calibrating LAI, which after calibra- tion was also closer to independent field measurements. It was concluded that assimilation of neutron counts was use- ful for ET and soil moisture estimation even if the model has a systematic bias like neglecting irrigation. However, also the assimilation of LST helped to correct the systematic model bias introduced by neglecting irrigation and LST could be used to update soil moisture with state augmentation.

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