Abstract
Soil moisture plays a key role in water and energy exchange in the land hydrologic process. Effective soil moisture information can be used for many applications in weather and hydrological forecasting, water resources, and irrigation system management and planning. However, to accurate modeling of soil moisture variation in the soil layer is still very challenging. In this study, in situ and remote sensing information of near-surface soil moisture is assimilated into the Noah land surface model (LSM) to estimate deep-layer soil moisture variation. The sequential Monte Carlo-Particle Filter technique, being well known for capability of modeling high nonlinear and non-Gaussian processes, is applied to assimilate surface soil moisture measurement to the deep layers. The experiments were carried out over several locations over the semi-arid region of the US. Comparing with in situ observations, the assimilation runs show much improved from the control (non-assimilation) runs for estimating both soil moisture and temperature at 5-, 20-, and 50-cm soil depths in the Noah LSM.
Highlights
Soil moisture is a key element in land surface hydrologic process, and it plays a vital role in water and energy cycles
Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) into Catchment land surface model (CLSM); they found that soil moisture estimated from data assimilation is better than that retrieved from satellites and from model control runs
Sabater et al (2007) assimilated surface soil moisture from the Surface Monitoring of the Soil Reservoir Experiment (SMOSREX) into the Interaction between Soil, Biosphere, and Atmosphere Scheme (ISBA) LSM to investigate root zone soil moisture. They used four assimilation methods, including the variational methods, Kalman Filter (KF), extended KF (EKF), and ensemble KF (EnKF) and suggested that 1D-VAR is the best and that EnKF is a ‘‘promising technique.’’ Reichle et al (2008) have used adaptive EnKF to assimilate soil moisture into CLSM, and suggested that the Adaptive EnKF method can generally identify model and observation error variances and improve assimilation estimates when compared with EnKF output
Summary
Soil moisture is a key element in land surface hydrologic process, and it plays a vital role in water and energy cycles. Koster et al (2009) found that these systematic differences vary depending on many factors, such as satellite sensors, retrieval algorithms, and LSMs, which have posed challenges for combining these datasets Both KF and EnKF assume that all probability distributions involved are Gaussian, whereas most physics models are non-linear and non-Gaussian. At the bottom soil layer, the equation is very similar to Eq 1 but, first, without the last four terms on the right side, second adding soil water hydraulic conductivity from the third layer to the bottom layer, and letting the soil water hydraulic conductivity from this layer become the base flow Both KF and EnKF assume that all probability distributions involved are Gaussian, while most physical models are nonlinear and non-Gaussian. In the discrete format xt 1⁄4 xit, wheÀre i isÁ the sample index, Ns is the sample size, and gitjt 1⁄4 p xitjZt , Eqs. 4 and 5 become: gitjtÀ1
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