新疆和田植被水分利用效率的SEM-ANN模型构建

A SEM-ANN model of vegetation water use efficiency in Hotan, Xinjiang

水分利用效率(WUE)反映了消耗单位质量水所制造的干物质量,是评估植被生长适宜性的综合指标,但是WUE的复杂影响机制使得区域多种因素的驱动贡献不甚明晰。为了探究多种因素对WUE的直接或间接影响关系,以求得到更好的WUE模拟效果,本研究构建了结构方程模型(SEM)与人工神经网络(ANN)相耦合的SEM-ANN模型。由SEM确定WUE影响因素之间的结构关系及影响程度,然后转化为ANN的拓扑结构。结果表明:在新疆和田地区,不同植被类型WUE的影响因素和影响层次有所不同;温度、降水、饱和水汽压差、风速均对植被WUE有着不同程度的直接影响;在草地及耕地中,增强植被指数(EVI)可体现为为间接影响WUE的中间变量;而在灌木及常绿针叶林中,标准化降水蒸散发指数(SPEI)将作为中间变量。经过SEM优化后的ANN结构,拟合效果更好,SEM-ANN模型对于生态系统的环境控制和WUE的模拟有着很高的解释性和精确性,这为提高新疆生态系统的高效用水能力以及预测未来气候变化下WUE的响应状况提供了理论依据和模拟途径。

Water use efficiency(WUE)of vegetation reflects the amount of its dry matter through consuming per unit amount of water,a comprehensive indicator for assessing its growth conditions.However,contributions of multiple forcing factors to WUE are unclear due to the complicated influencing mechanism.Combining a structural equation model(SEM)with the artificial neural network(ANN),this paper develops a hybrid SEM-ANN model for analysis of the direct and indirect influences of WUE multiple factors to achieve an improvement on the simulations.It determines the structural relationship among the factors and their degrees of influence by using SEM,and then constructs the topology of ANN.The results show that in the Hotan region,various vegetation types have different WUE factors at different levels.We divide them into direct factors and intermediate variables that impact WUE indirectly-with the former including temperature(T),precipitation(P),vapor pressure deficit(VPD),and wind speed(WS);the latter including an enhanced vegetation index(EVI)for grassland and cropland and a standardized precipitation evapotranspiration index(SPEI)for shrub land and evergreen needle leaved forest.The SEM-optimized structure of ANN fits better,and the SEM-ANN model has high explanatory capacity and higher accuracy in the ecosystem’s environmental control and simulations of WUE,thus providing a theoretical basis and simulation method that can improve efficient water use and predict future WUE responses to climate changes in Xinjiang.