Seasonal and interannual changes in the Earth's gravity field are mainly due to mass exchange among the atmosphere,ocean,and continental water sources.The terrestrial water storage changes,detected as gravity changes by the Gravity Recovery and Climate Experiment(GRACE) satellites,are mainly caused by precipitation,evapotranspiration,river transportation and downward infiltration processes.In this study,a land data assimilation system LDAS-G was developed to assimilate the GRACE terrestrial water storage(TWS) data into the Community Land Model(CLM3.5) using the POD-based ensemble four-dimensional variational assimilation method PODEn4 DVar,disaggregating the GRACE large-scale terrestrial water storage changes vertically and in time,and placing constraints on the simulation of vertical hydrological variables to improve land surface hydrological simulations.The ideal experiments conducted at a single point and assimilation experiments carried out over China by the LDAS-G data assimilation system showed that the system developed in this study improved the simulation of land surface hydrological variables,indicating the potential of GRACE data assimilation in large-scale land surface hydrological research and applications.
Freeze-thaw processes in soils,including changes in frost and thaw fronts(FTFs),are important physical processes.The movement of FTFs affects soil hydrothermal characteristics,as well as energy and water exchanges between the land surface and the atmosphere and hydrothermal processes in the land surface.This paper reduces the issue of soil freezing and thawing to a multiple moving-boundary problem and develops a soil water and heat transfer model which considers the effects of FTF on soil hydrothermal processes.A local adaptive variable-grid method is used to discretize the model.Sensitivity tests based on the hierarchical structure of the Community Land Model(CLM)show that multiple FTFs can be continuously tracked,which overcomes the difficulties of isotherms that cannot simultaneously simulate multiple FTFs in the same soil layer.The local adaptive variable-grid method is stable and offers computational efficiency several times greater than the high-resolution case.The simulated FTF depths,soil temperatures,and soil moisture values fit well with the observed data,which further demonstrates the potential application of this simulation to the land-surface process model.
WANG AiWenXIE ZhengHuiFENG XiaoBingTIAN XiangJunQIN PeiHua
A proper orthogonal decomposition（POD） method was successfully used in the reduced-order modeling of complex systems.In this paper,we extend the applications of POD method,namely,apply POD method to a classical finite element（FE） formulation for second-order hyperbolic equations with real practical applied background,establish a reduced FE formulation with lower dimensions and high enough accuracy,and provide the error estimates between the reduced FE solutions and the classical FE solutions and the implementation of algorithm for solving reduced FE formulation so as to provide scientific theoretic basis for service applications.Some numerical examples illustrate the fact that the results of numerical computation are consistent with theoretical conclusions.Moreover,it is shown that the reduced FE formulation based on POD method is feasible and efficient for solving FE formulation for second-order hyperbolic equations.
To improve the capability of numerical modeling of climate-groundwater interactions, a groundwater component and new surface/subsurface runoff schemes were incorporated into the regional climate model RegCM3, renamed RegCM3_Hydro. 20-year simulations from both models were used to investigate the effects of groundwater dynamics and surface/subsurface runoff parameterizations on regional climate over seven river basins in China. A comparison of results shows that RegCM3_Hydro reduced the positive biases of annual and summer （June, July, August） precipitation over six river basins, while it slightly increased the bias over the Huaihe River Basin in eastern China. RegCM3_Hydro also reduced the cold bias of surface air temperature from RegCM3 across years, especially for the Haihe and the Huaihe river basins, with significant bias reductions of 0.80~C and 0.88~C, respectively. The spatial distribution and seasonal variations of water table depth were also well captured. With the new surface and subsurface runoff schemes, RegCM3_Hydro increased annual surface runoff by 0.11 0.62 mm d 1 over the seven basins. Though previous studies found that incorporating a groundwater component tends to increase soil moisture due to the consideration of upward groundwater recharge, our present work shows that the modified runoff schemes cause less infiltration, which outweigh the recharge from groundwater and result in drier soil, and consequently cause less latent heat and more sensible heat over most of the basins.
Bayesian model averaging(BMA) is a recently proposed statistical method for calibrating forecast ensembles from numerical weather models.However,successful implementation of BMA requires accurate estimates of the weights and variances of the individual competing models in the ensemble.Two methods,namely the Expectation-Maximization(EM) and the Markov Chain Monte Carlo(MCMC) algorithms,are widely used for BMA model training.Both methods have their own respective strengths and weaknesses.In this paper,we first modify the BMA log-likelihood function with the aim of removing the addi-tional limitation that requires that the BMA weights add to one,and then use a limited memory quasi-Newtonian algorithm for solving the nonlinear optimization problem,thereby formulating a new approach for BMA(referred to as BMA-BFGS).Several groups of multi-model soil moisture simulation experiments from three land surface models show that the performance of BMA-BFGS is similar to the MCMC method in terms of simulation accuracy,and that both are superior to the EM algo-rithm.On the other hand,the computational cost of the BMA-BFGS algorithm is substantially less than for MCMC and is al-most equivalent to that for EM.
