Research For Soil Moisture In Land Data Assimilation With The Simultaneous Estimation Of States And Parameters

Soil moisture is not only the essential part of the water cycle in the land,but also the important influence factor for the partition of the surface energy.Thus,the acciurate estimation of soil moisture plays a significance role in the recognition of land surface condition,the research of the land-atmosphere coaction and the management of the agricultural production,etc.As a fusion method for model and data,land data assimilation which utilizes the observation to modifiy the forecast trajectory of the dynamic models based on the calculation of the error of background the observation can produce the accurate soil moisture estimation continuous distributing in space and time.Obviously,model and data are the two major components of the land data assimilation framework which also contribute to the constraint and challenge for soil moisture estimation in land data assimilation.Thus,this study paid attention to the uncertainty of model parameters and the combination assimilation of mulit-types of observations which established reasonable data assimilation framework to improve the soil moisture estimation in ground stations and regions.The main contributions are summarized as follows:(1)Simultaneous estimation of state and parameter with EnKF.The system deviation between the simulations and observations which caused by the uncertainty of model parameters often deteriorated the performance of state assimilation.Thus,simultaneous estimation of state and parameter is employed to reduce the biase of the simulations and improve the estimation of soil moisture in this study.Ground stations in the Mongolia from CEOP were chosen as the study area,and we established the soil moisture assimilation framework for both linear observation operator(in-situ soil moisture as observation)and nonlinear observation operator(brightness temperature as observation).We compared the performance of three simultaneous states-parameters estimation algorithms(AEnKF,DEnKF and SODA)with different experiment scene,observation internal and observation combination.The results showed that the soil moisture estimation form simultaneous states-parameters estimation algorithms surpass the state assimilation algorithm(EnKF).AEnKF is highly efficient with accurate results of both states and parameters under the situation of explicit relationship lying between states and parameters.Unlike AEnKF,DEnKF showed advantages dealing with complicated experiment scene and consume similar computation time as AEnKF.SODA costs much time combined with the most accurate states and parameters in complicated cases and shows a slight change with the amount and sensitivity of observations declining.The performance of different algorithms under different experiment scene showed their suitability which also provided the seletion rule for the follow-up study.(2)Combination assimilation of multi-type observations from ground station for soil moisture.The absence of irrigation information leads to the difficult estimation of soil hydro-thermal states in agricultural area and also brings another uncertainty to the soil moisture assimilation framework.Given the interaction between states variables in the land surface model,we establish a combination assimilation framework of multi-type observations from ground station for soil moisture.Ground stations in the the Heihe River Basin from HIWATER were chosen as the study area,and suface soil moisture and land surface temperature were chosen as observations.Inflation and localization are implement with ensemble kalman smoother to solve the underestimation of background error and the reasonably allocation the observation information.We test the assimilation framework with unknown irrigation,known irrigation,different observation intervals,different observation errors and parameters estimation.The experiment results show the assimilation of multi-type observations improved multiple variables,surpassing the performance of the assimilation of single observations with unknown irrigation,especially for shallow soil.Moreover,the stabilized and promising effectiveness of the ESIL with varying observation intervals and standard deviations broadened its reliability in practical applications.Also,the study pointed out that the effective estimation of parameters in agricultural area relying on the reasonable consideration of the impact of irrigation.(3)Combination assimilation of multi-source observations from satellites for soil moisture.Microwave observations have significant advantage in the acquisition of soil moisture information at land surface,especially in terms of the low frequency.Considering that the coarser spatial resolution of AMSR-E observations/products cannot fulfill the needs of applications in most cases,this study investigates the possibility to downscaling the soil moisture by simultaneously assimilating AMSR-E brightness temperatures and MODIS LST product.Here,MODIS LST product is employed to provide the information of surface temperature at the fine spatial scale of CoLM.Thus,the combined assimilation experiment can retrieve soil moisture at a high resolution despite the coarse-scale TB observations and resulting in a better accuracy than modeling.The assimilation experiment produced quite encouraging improvement in the estimation of soil moisture at the surface as well as soil temperature profile,but the estimation of soil moisture at deeper layers deteriorated.A superior model parameterization scheme to achieve the effective delivery of the surface information to the deeper layers is expected in further studies.To consider the uncertainty in parameters,dual ensemble kalman smoother are used to simultaneous estimation soil moisture and related paramters in this study.The soil moisture simulation with updated parameters reduced the original bias to the measurements and performed better than the results with default soil parameter values.Moreover,an upscaling method was implemented to obtain the footprint-averaged soil moisture as the ground truth to resolve the representative problem brought about from point to area.Compared to the ATI-based upscaled soil moisture,the improvement of soil moisture at surface become more significant which further proved the positive performance of TB assimilation in soil moisture estimation and the necessity of correctly handling the representative problem.