The most promising approach for studying soil moisture is the assimilation of observation data and computational modeling. However, there is much uncertainty in the assimilation process, which affects the assimilation...The most promising approach for studying soil moisture is the assimilation of observation data and computational modeling. However, there is much uncertainty in the assimilation process, which affects the assimilation results. This research developed a one-dimensional soil moisture assimilation scheme based on the Ensemble Kalman Filter (EnKF) and Genetic Algorithm (GA). A two-dimensional hydrologic model-Distributed Hydrology-Soil-Vegetation Model (DHSVM) was coupled with a semi-empirical backscattering model (Oh). The Advanced Synthetic Aperture Radar (ASAR) data were assimilated with this coupled model and the field observation data were used to validate this scheme in the soil moisture assimilation experiment. In order to improve the assimilation results, a cost function was set up based on the distance between the simulated backscattering coefficient from the coupled model and the observed backscattering coefficient from ASAR. The EnKF and GA were used to re-initialize and re-parameterize the simulation process, respectively. The assimilation results were compared with the free-run simulations from hydrologic model and the field observation data. The results obtained indicate that this assimilation scheme is practical and it can improve the accuracy of soil moisture estimation significantly.展开更多
The watershed flow concentration scheme in the distributed hydrology-soil- vegetation model (DHSVM) is coupled with the mesoscale atmospheric model MM5 version 3.5, in which the Oregen States University land surface m...The watershed flow concentration scheme in the distributed hydrology-soil- vegetation model (DHSVM) is coupled with the mesoscale atmospheric model MM5 version 3.5, in which the Oregen States University land surface model (OSULSM) was involved. The flood event which happened in July 2002 in the upper reaches of Heihe river basin is simulated and the surface flow convergence process is shown with this coupled model. It has been concluded that times water head reaches each place of the basin are different. Water amount at each point is split-flow proportionally as the drops in elevation between it and neighbor points. Large part of the water amount pass away in greater slope direction and small part pass away in smaller slope one.Adding of the slope convergence makes the atmospheric model redistributes the surface water laterally.展开更多
基金Under the auspices of Major State Basic Research Development Program of China (973 Program) (No. 2007CB714400)the Program of One Hundred Talents of the Chinese Academy of Sciences (No. 99T3005WA2)
文摘The most promising approach for studying soil moisture is the assimilation of observation data and computational modeling. However, there is much uncertainty in the assimilation process, which affects the assimilation results. This research developed a one-dimensional soil moisture assimilation scheme based on the Ensemble Kalman Filter (EnKF) and Genetic Algorithm (GA). A two-dimensional hydrologic model-Distributed Hydrology-Soil-Vegetation Model (DHSVM) was coupled with a semi-empirical backscattering model (Oh). The Advanced Synthetic Aperture Radar (ASAR) data were assimilated with this coupled model and the field observation data were used to validate this scheme in the soil moisture assimilation experiment. In order to improve the assimilation results, a cost function was set up based on the distance between the simulated backscattering coefficient from the coupled model and the observed backscattering coefficient from ASAR. The EnKF and GA were used to re-initialize and re-parameterize the simulation process, respectively. The assimilation results were compared with the free-run simulations from hydrologic model and the field observation data. The results obtained indicate that this assimilation scheme is practical and it can improve the accuracy of soil moisture estimation significantly.
基金co-supported by Orientation Project of Knowledge Innovation Program,Chinese Academy of Sciences(Grand No.KZCX3-SW-329 KZCX1-09)Key Program Project of National Natural Science Foundation of China(Grant Nos.40233035,40075022).
文摘The watershed flow concentration scheme in the distributed hydrology-soil- vegetation model (DHSVM) is coupled with the mesoscale atmospheric model MM5 version 3.5, in which the Oregen States University land surface model (OSULSM) was involved. The flood event which happened in July 2002 in the upper reaches of Heihe river basin is simulated and the surface flow convergence process is shown with this coupled model. It has been concluded that times water head reaches each place of the basin are different. Water amount at each point is split-flow proportionally as the drops in elevation between it and neighbor points. Large part of the water amount pass away in greater slope direction and small part pass away in smaller slope one.Adding of the slope convergence makes the atmospheric model redistributes the surface water laterally.
