基于有效含水量的土壤水分监测点布设的空间分层采样方法  

Spatial stratified sampling strategy for soil moisture based on available water capacity

作　　者：金建华 张宝忠[1,4] 刘钰[1,4] 毛晓敏[2] Jin Jianhua;Zhang Baozhong;Liu Yu;Mao Xiaomin(State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin,China Institute of Water Resources and Hydropower Research,Beijing 100038,China;College of Water Conservancy and Civil Engineering,China Agricultural University,Beijing 100083,China;College of Water Conservancy Engineering,Tianjin Agricultural University,Tianjin 300384,China;National Center of Efficient Irrigation Engineering and Technology Research-Beijing,Beijing 100048,China)

机构地区：[1]中国水利水电科学研究院流域水循环模拟与调控国家重点实验室,北京100038 [2]中国农业大学水利与土木工程学院,北京100083 [3]天津农学院水利工程学院,天津300384 [4]国家节水灌溉北京工程技术研究中心,北京100048

出　　处：《农业工程学报》2021年第21期100-107,共8页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家自然科学基金项目(51822907,51979287);流域水循环模拟与调控国家重点实验室自主研究项目(SKL2020TS08);天津市教委科研计划项目(2018KJ189)。

摘　　要：为了优化灌溉实践,构建准确估计平均土壤水分的监测点布设准则,该研究引入有效含水量(Available Water Capacity,AWC)作为辅助变量,结合经典统计学和地统计学构建了一种基于辅助变量空间自相关的分层采样方法(Stratified Sampling method based on spatial autocorrelation of Auxiliary Variables,SSAV),克服直接以土壤水分为变量时受其强时空变异影响的弊端,并在田块尺度进行试验。结果表明:0~40和0~80 cm土层的AWC服从正态分布;在90%置信区间,采样误差为10%时研究区内0~40和0~80 cm土层的监测点数目分别为7个和6个;基于SSAV布点法估计土壤水分的相对误差变化范围为–23.23%~35.15%,较简单随机布点(Simple Random Sampling,SRS)法减小了26.48%。标准差的平均值为4.78%,较SRS降低了17.30%。基于SSAV的0~40和0~80 cm^(2)个土层的估计值和观测值之间的平均均方根误差RMSE为0.0104 cm^(3)/cm^(3),基于SRS的RMSE为0.0120 cm^(3)/cm^(3),显著性检验P<0.001,SSAV显著提高了对土壤水分的估计精度和准度。SSAV为获得区域平均土壤水分提供了省时、省力、低成本的监测点布设方案,为农业水资源管理和提升农业用水效率提供了保障。Soil moisture has been a key limiting factor for crop growth during the surface process in many lands.It is very necessary to establish the placement criteria of monitoring sites for the soil moisture in optimum irrigation.The spatial and temporal distribution of Available Water Capacity(AWC)was strongly correlated with soil moisture.The AWC spatial distribution pattern was also related to soil characteristics,but it can be more stable than that of soil moisture.In this study,a spatially stratified sampling was proposed to relieve the strong temporal and spatial variability,when the soil moisture was used as a variable.The Stratified Sampling method based on spatial autocorrelation of Auxiliary Variables(SSAV)was also used to combine the classical statistics and geo-statistics,where the AWC was introduced as an auxiliary variable.The experiments were then carried out to verify at a field scale.The results showed that the AWC in the 0-40 and 0-80 cm soil layers followed the normal distribution,indicating a moderate variation.In the 90%confidence interval,the number of monitoring points in the 0-40 and 0-80 cm soil layers in the study area was 7 and 6,respectively,where the sampling error was 10%,indicating that the reducing number of monitoring points,and cost-saving monitoring of soil moisture.The geostatistical analysis demonstrated that the range of two soil layers(0-40 and 0-80 cm)was both 366 m in the semi-variance function of AWC.The relative errors of soil moisture estimated by the Simple Random Sampling(SRS)and SSAV were–27.03%-52.38%,and–23.23%-35.15%,respectively.The relative error of soil moisture estimated by the SSAV was reduced by 26.48%,compared with the SRS.The mean standard deviation was 4.78%,17.30%lower than that of SRS.A paired t-test indicated that the relative error and the mean standard deviation of soil moisture were 9 times in the two soil layers under two monitoring during 2016-2018.Thus,there were significant differences between the relative error range and the mean standard deviatio

关 键 词：土壤 水分 采样 地统计学 有效含水量 空间相关性 空间变异性 

分 类 号：S127[农业科学—农业基础科学]