Predicting soil water movement in converted soybean fields under high moisture condition
Predicting soil water movement in converted soybean fields under high moisture condition
复制标题
高湿条件下转化大豆田土壤水分运动预测
DOI：
10.1007/s10333-016-0537-z
复制
发表时间：
2019
影响因子：
2.2
通讯作者：
N. Matsuyama and T. Nishimura
中科院分区：
文献类型：
--
作者：
Kato;C.;C. Sasaki;A. Endo;N. Matsuyama and T. Nishimura
为了通过考虑流域土壤类型的空间分布来预测任意农业用地的土壤水分状况，我们研究了利用日本现有的土壤物理性质数据库“SolPhyJ”和数字土壤地图确定土壤水文参数的可能的适当方法。在这项研究中，对邻近的三个地点进行了土壤水分模拟，包括分别位于富山和福井市的监测点。与用土壤质地和干容重数据进行神经网络估计的参数相比，用Solphy J拟合水力参数得到的土壤水分模拟结果似乎更好。这可能是由于各农田特殊的土壤结构比土壤质地对土壤水力性质的影响更大。模拟结果还表明，即使在相邻的地方(<；5公里)，土壤水分也有很大的不同。我们认为，这两个数据库的结合对于估算土壤水力参数和预测任意农田的土壤水分状况是有用的。
To predict soil moisture condition in arbitral agricultural lands by taking spatial distribution of soil type in a watershed into account, we investigated the possible proper methods of determining the soil hydrological parameters using available soil physical properties database of Japanese soils, “SolphyJ”, and the digital soil map. In this study, simulation of soil moisture was conducted at three neighboring locations, including monitoring sites each in Toyama and Fukui cities. The simulated results of soil moisture appeared to be improved when hydraulic parameters were obtained by fitting water retention data of SolphyJ compared to the parameters estimated by neural network with soil texture and dry bulk density data. It is probably because peculiar soil structure in each field could affect the hydraulic properties more than the soil texture. Simulation results also indicated that soil moistures are much different even if they are located in neighbors (<5 km). We concluded that combination of these two databases is useful for estimating soil hydraulic parameters and to predict soil moisture condition in arbitrary agricultural lands.