Abstract
Hydrological models play an essential role in data assimilation (DA) systems. However, it is a challenging task to acquire the distributed hydrological model parameters that affect the accuracy of the simulations at a grid scale. Remote sensing data provide an ideal observation for DA to estimate parameters and state variables. In this study, a special assimilation scheme was proposed to jointly estimate parameters and soil moisture (SM) by assimilating brightness temperature (TB) from the Soil Moisture and Ocean Salinity (SMOS) mission. Variable infiltration capacity (VIC) hydrological model and L-band microwave emission of the biosphere model (L-MEB) are coupled as model and observation operators, respectively. The scheme combines two stages of estimators, one for the static model parameters and the other for the dynamic state variables. The estimators approximate the posterior probability distribution of an unknown target through sequential Monte Carlo (SMC) sampling. Markov chain Monte Carlo (MCMC) and immune evolution strategy are embedded in both stages to solve particle impoverishment problems. To evaluate the effectiveness of the scheme, the estimated SM sets are compared with in-situ observations and SMOS products in Maqu on the Tibetan Plateau. Specifically, the root mean square error decreased from 0.126 to 0.087 m3m−3 for surface SM, with a slight impact on the root zone. The temporal correlation between DA results and in-situ measurements increased to 0.808 and 0.755 for surface SM (+0.057) and root zone SM (+0.040), respectively. The results demonstrate that assimilating TB has tremendous potential as an approach to improve the estimation of distributed model parameters and SMs of surface and root zone at a grid scale, and the immune evolution strategy is effective for increasing the accuracy of approximation in sampling.
Highlights
Soil moisture (SM) plays a fundamental role in the global energy and water cycle
The model parameters estimation was performed for the Variable infiltration capacity (VIC) simulation with the full energy balance and frozen soils mode
According to the suggestion that the optimization time window of one year for model calibration is reasonably rational for the Maqu region [17] and the assumption that model parameters are time-invariant in the short term, the preprocessed TBs were assimilated into the VIC model from March to September in 2011, including soil freezing/thawing and unfrozen periods
Summary
Soil moisture (SM) plays a fundamental role in the global energy and water cycle. It governs the partitioning of the mass and energy fluxes between the land and the atmosphere [1,2]. Modeling and observation are two typical approaches to obtain SM information. According to the means of acquisition, observation can be divided into ground-based measurements and remote sensing observations. Different approaches have their respective advantages and drawbacks.The drawbacks limit their practical applications, such as potential the bias of modeling, limited coverage of regions for ground-based measurements, and limited penetration depth of microwaves. Data assimilation (DA), merging observed data into a model, is a widely used technique to obtain continuous spatio-temporal SM in hydrology and meteorology [5,6,7]
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