利用土壤表层含水量序列预测深层含水量的研究

土壤剖面含水量的预测对于灌溉、防治水土流失、改善生态环境等一系列环境过程具有重要意义。根据每天测量红壤不同层次含水量,利用时间序列分析方法,依据表层10cm含水量序列预测20cm,30cm,40cm和60cm土层含水量。结果表明,各不同土层含水量之间呈极显著性相关;利用分布滞后模型根据10cm土层含水量预报各深层土壤含水量,模型的滞后时间随着被预报土层深度的增加而增加,预报模型相对误差不超过6%,最大相对误差不超过10%。10 cm土层含水量分别联合20 cm,30 cm,40 cm和60 cm土层含水量,利用自回归分布滞后模型对相应各土层含水量模拟预报,缩短了滞后时间,模型表达式更简洁,精度仍然较高。

Study on Predicting Lower Layer Soil Water Content Using Surface Soil Moisture Series

Prediction of soil profile water content is important in irrigation, soil and water conservation, and environment improvement, etc. In this paper, we measured daily soil water content at different depths in a red soils field, and estimated the moisture at depth of 20 cm, 30 cm, 40 cm and 60 cm by the time series analysis methods using 10cm measured data . The results showed that there were significant relationships between soil water content at different depths. Based on the water content at depth of 10cm, soil water content at lower layers were predicted by the distributed lag models. With the increase of soil depth to be forecasted, the lag time in the model increased. The average relative error of soil water content between measured and estimated was less than 6 %, and the maximum relative error was not more than 10%. By combining soil water content in 10cm depth and soil water content in 20 cm, 30 cm, 40 em and 60 cm respectively, we simplified the models to autoregressive distributed lag models. With shorter lag times, the models also got acceptable precisions.