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
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
为分析松花江流域水资源的演变规律,基于寒区水-热-氮素循环模型(the water and energy transfer processes and nitrogen cycle processes model in cold regions,WEP-N)和水资源评价方法,对径流发生突变的1998年前后(即1999—2018年和...为分析松花江流域水资源的演变规律,基于寒区水-热-氮素循环模型(the water and energy transfer processes and nitrogen cycle processes model in cold regions,WEP-N)和水资源评价方法,对径流发生突变的1998年前后(即1999—2018年和1956—1998年)进行比较,松花江流域年水资源总量减少217.0亿m^(3),减幅达到22.2%。其中,地表水资源量减少是水资源总量减少的主要组分,占水资源总量减少的比例为96.9%,不重复地下水资源减少量占3.1%。基于多因子归因分析方法分析可知,气候变化是松花江流域水资源减少的主要因素,对松花江流域全年水资源总量、地表水资源量、不重复地下水资源量减少的贡献率分别为81.6%、74.9%、286.6%,取用水的贡献率分别为18.4%、25.1%、-86.6%。从年内不同时期分析可知,非冻融期是全年水资源量减少的主要时期,占全年水资源总量减少的82.6%,冻融期占17.4%。和北方的海河流域、黄河流域相比,水资源减少幅度和主要影响因素各不相同,主要取决于气候变化和人类活动强度变化幅度的不同。与位于华北和西北的两大流域海河流域和黄河流域对比,气候变化对松花江流域水资源衰减的影响与海河流域相当,明显大于黄河流域,而人类活动对松花江流域水资源衰减的影响明显小于两大流域。展开更多
Flash floods in arid environments are a major hazard feature to human and to the infrastructure. Shortage of accurate environmental data is main reason for inaccurate prediction of flash flooding characteristics. The ...Flash floods in arid environments are a major hazard feature to human and to the infrastructure. Shortage of accurate environmental data is main reason for inaccurate prediction of flash flooding characteristics. The curve number (CN) is a hydrologic number used to describe the storm water runoff potential for drainage area. This study introduces an approach to determine runoff coefficient in Jeddah, Saudi Arabia using remote sensing and GIS. Remote sensing and geographic information system techniques were used to obtain and prepare input data for hydrologic model. The land cover map was derived using maximum likelihood classification of a SPOT image. The soil properties (texture and permeability) were derived using the soil maps published my ministry of water and agriculture in Saudi Arabia. These soil parameters were used to classify the soil map into hydrological soil groups (HSG). Using the derived information within the hydrological modelling system, the runoff depth was predicted for an assumed severe storm scenario. The advantages of the proposed approach are simplicity, less input data, one software used for all steps, and its ability to be applied for any site. The results show that the runoff depth is directly proportional to runoff coefficient and the total volume of runoff is more than 136 million cubic meters for a rainfall of 103.6 mm.展开更多
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.
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.
文摘为分析松花江流域水资源的演变规律,基于寒区水-热-氮素循环模型(the water and energy transfer processes and nitrogen cycle processes model in cold regions,WEP-N)和水资源评价方法,对径流发生突变的1998年前后(即1999—2018年和1956—1998年)进行比较,松花江流域年水资源总量减少217.0亿m^(3),减幅达到22.2%。其中,地表水资源量减少是水资源总量减少的主要组分,占水资源总量减少的比例为96.9%,不重复地下水资源减少量占3.1%。基于多因子归因分析方法分析可知,气候变化是松花江流域水资源减少的主要因素,对松花江流域全年水资源总量、地表水资源量、不重复地下水资源量减少的贡献率分别为81.6%、74.9%、286.6%,取用水的贡献率分别为18.4%、25.1%、-86.6%。从年内不同时期分析可知,非冻融期是全年水资源量减少的主要时期,占全年水资源总量减少的82.6%,冻融期占17.4%。和北方的海河流域、黄河流域相比,水资源减少幅度和主要影响因素各不相同,主要取决于气候变化和人类活动强度变化幅度的不同。与位于华北和西北的两大流域海河流域和黄河流域对比,气候变化对松花江流域水资源衰减的影响与海河流域相当,明显大于黄河流域,而人类活动对松花江流域水资源衰减的影响明显小于两大流域。
文摘Flash floods in arid environments are a major hazard feature to human and to the infrastructure. Shortage of accurate environmental data is main reason for inaccurate prediction of flash flooding characteristics. The curve number (CN) is a hydrologic number used to describe the storm water runoff potential for drainage area. This study introduces an approach to determine runoff coefficient in Jeddah, Saudi Arabia using remote sensing and GIS. Remote sensing and geographic information system techniques were used to obtain and prepare input data for hydrologic model. The land cover map was derived using maximum likelihood classification of a SPOT image. The soil properties (texture and permeability) were derived using the soil maps published my ministry of water and agriculture in Saudi Arabia. These soil parameters were used to classify the soil map into hydrological soil groups (HSG). Using the derived information within the hydrological modelling system, the runoff depth was predicted for an assumed severe storm scenario. The advantages of the proposed approach are simplicity, less input data, one software used for all steps, and its ability to be applied for any site. The results show that the runoff depth is directly proportional to runoff coefficient and the total volume of runoff is more than 136 million cubic meters for a rainfall of 103.6 mm.
