麦田土壤水分时空变异特性及CA-Markov模型模拟预报

为揭示农田土壤水分时空变异特征，精准预测土壤含水量，该研究以河北省太行山山前平原井灌区典型麦田为例，在监测土壤水分的基础上，采用时间稳定性指数法、空间自相关性评价法研究土壤水分时空分布规律，构建了适用于模拟预报田间水分时空变化的CA-Markov 模型，并将该模型的模拟预报效果与HYDRUS 模型进行比较。结果表明：随着土层深度的增加，土壤水分等值线由密变疏，变异系数逐渐减小。随着小麦生育期的推移，前期监测的土壤水分稳定性高于后期；在土壤较湿润的情况下，土壤水分空间相关性较强，土壤水分全局Moran’s I 指数随小麦生育期的推移呈现先增大后变小的规律。CA-Markov 模型模拟预报的各土壤相对湿度等级面积误差的平均值为1.61%，比HYDRUS 模型模拟预报的面积误差平均值（10.86%）小9.25个百分点； CA-Markov 模型对研究区4月下旬、5月上旬的土壤水分干旱等级预测的空间分布Kappa 系数分别为 89.31%、91.46%。该模型可综合考虑麦田墒情的时空变化及随机特性，模拟预测土壤墒情的精度较高、效果良好，可以作为麦田水分管理的重要工具。

Spatial-temporal variation of soil moisture in wheat field and simulation prediction using CA-Markov model

Soil moisture can be a very important indicator to monitor agricultural drought. The dynamics of water distribution in the soil can greatly contribute to the decision-making on the soil and water resources. This study aims to reveal the spatial and temporal variation characteristics of soil moisture in the farmland, and then accurately predict the soil moisture. The soil information fixed-point monitoring was carried out in a typical Wheat Grains field of Taihang mountain front and plain region in Hebei province, China. A total of 150 sampling points were designed from April 19th to May 9th 2017. A wheat field with a width of 100 m and a length of 50 m was meshed by 10 m for the soil sampling in each mesh grid. Soil samples were collected from the depth points at 0-20, >20-40, >40-60, and >60-80 cm six times during the sampling. The temporal stability index and spatial autocorrelation evaluation were then used to determine the spatial and temporal distribution of soil moisture. The CA-Markov model was constructed to predict the spatial and temporal variation of soil moisture in the field. A comparison was made on the prediction with the HYDRUS model. A field test of data detection was finally conducted to verify the accuracy of the simulation. The results showed that the isoline map of soil moisture was changed from dense to sparse with the increase in soil depth. There was the largest variation in the surface soil moisture. The variation coefficient of soil moisture also decreased gradually, as the soil depth increased. Specifically, the proportions of temporal stability index for the soil moisture less than 10% accounted for 52%, 58%, 60%, and 74% in the soil layers of 0-20, >20-40, >40-60, and >60-80 cm, respectively. Correspondingly, there was high temporal stability with the increase in soil depth. Furthermore, irrigation was an important factor with a strong spatial correlation under humid conditions, thus influencing the spatial distribution pattern of soil moisture. A spatial analysis was also conducted using the Moran''s I statistic. It was found that the global Moran''s I index increased firstly and then decreased with the growth period of wheat. Particularly, the global Moran''s I index of relative humidity in the last three times was 0.064-0.142 smaller than that in the first three times, indicating the weak autocorrelation caused by the soil structure. Once the degree of drought reached a certain level, there was a decreasing trend in the spatial autocorrelation of soil moisture and relative humidity. A CA-Markov model was constructed to simulate the change of drought grade, according to the characteristics of soil relative moisture. The average area error was 1.61% for each grade of soil relative moisture, which was 9.25% smaller than that (10.86%) by the HYDRUS model. At the same time, the CA-Markov model was used to simulate the drought grade of soil moisture in late April and early May. The Kappa coefficients of predicted spatial distribution were 89.31% and 91.46%, respectively. Both the Kappa coefficients were higher than 75%, indicating an excellent performance of the improved model on the prediction of soil moisture distribution. The findings can provide a strong reference for crop growth and irrigation water management.