论文部分内容阅读
陆面模型为区域农田土壤墒情监测提供了很好的途径,优化选择模型的网格尺度可以最有效地的利用空间输入信息,提高计算效率。本研究以海河平原内的1°×1°(115.5～116.5°(E),38～39°(N))为研究区,运用陆面模型CLM3.0分别在(1/120)～1°的14种不同网格尺度上对2003年3—5月的土壤墒情进行了独立模拟,分析在一定精度的空间输入数据条件下,陆面模型的网格尺度在该区域春季土壤墒情模拟中的优化取值。研究表明,结合模型输入数据的空间分辨率选择合适的网格尺度,可有效地减少计算机浮点计算取舍引起的误差;网格的无限精细并不能提高模拟效果,需要依据土壤砂粒百分含量数据的精度、变程及模拟目的优化选择陆面模型的网格尺度。当仅需要获得区域的土壤墒情平均值时,网格尺度的优化取值在土壤砂粒百分含量数据变程的1.4倍附近;当需要获得区域的土壤墒情空间变异特征时,网格尺度的优化取值在土壤砂粒百分含量数据变程的28%附近;当需要获得区域的土壤墒情空间变异特征及极大值时,网格尺度的优化取值在土壤砂粒百分含量数据变程的19%附近;当需要获得区域的土壤墒情的所有空间统计特征时,网格尺度的优化取值在土壤砂粒百分含量数据的空间最小尺度附近。
The land surface model provides a good way to monitor the soil moisture in the farmland. Optimizing the grid scale of the selection model can make the most efficient use of space input information and improve the computational efficiency. In this study, 1 ° × 1 ° (115.5 ~ 116.5 ° (E), 38 ~ 39 ° (N)) of the Haihe River Plain was selected as the study area. The land surface model CLM3.0 was used in the study area from (1/120) to 1 The soil moisture of March-May 2003 was simulated independently on 14 different grid scales. Under the condition of spatial input data of a certain precision, the grid scale of land-surface model was simulated in spring soil moisture in this region Optimize the value. The research shows that selecting the appropriate grid scale based on the spatial resolution of the model input data can effectively reduce the error caused by the computer floating point calculation choice. The infinite fineness of the grid can not improve the simulation effect, which needs to be based on the percentage of soil sand content The accuracy, range, and simulation objectives optimize the selection of the grid scale of the land surface model. When only the average of soil moisture in the area needs to be obtained, the optimal grid scale is about 1.4 times of the data range of soil grit percentage. When the spatial variability of soil moisture is needed, the grid scale optimization When the spatial variability and the maximum of soil moisture are needed, the optimal values of grid scale are in the range of 19% %; And when all the spatial statistical characteristics of soil moisture in the area need to be obtained, the grid scale optimization value is near the minimum spatial scale of the soil grit percentage data.