海河流域作物水分敏感系数空间分布

作物水分敏感系数是指导有效灌溉和优化配水的关键参数,研究其空间分布格局对流域水资源优化配置具有重要作用。该文通过空间统计建模,利用探索性空间数据分析技术,分别基于Moran′I统计量、Moran散点图以及空间关联局域指标(local indicators of spatial association,LISA)对作物水分敏感系数(Ky)进行全局、局部空间自相关分析,探索Ky在流域内的空间分布格局。结果表明,Ky在流域空间上呈现出东部平原地区较大,西部及北部山地较小的趋势,其值在0.749～1.668变化。Ky总体上存在显著的空间正相关关系(Moran′I为0.6009～0.6077,且p<0.0001),而且呈现明显的空间集聚特征;高-高集聚区位于北京、天津等东部平原地区,低-低集聚区位于承德、秦皇岛和大同等北部和西部山地,高-高和低-低集聚类型区占据整个流域的80%,其中约有一半表现显著(p<0.05),而低-高和高-低集聚区仅有少数;Ky空间自相关程度随距离的增加而减弱,在距离为240～280km时自相关系数接近于0。总之,Ky形成以东部平原地区为"高敏感核心区",逐渐向西部及北部山地发散并降低的核心-边缘空间分布格局。研究结果可为该地区节水灌溉和水资源优化配置提供指导。

Spatial pattern of crop water sensitive coefficient in Haihe Basin

Abstract: The crop water production function is of importance to water resource planners. The crop water sensitivity coefficient in this study is derived from the seasonal empirical model presented by Doorenbos and Kassam (1979). The coefficient is also known as yield response factor (Ky), which is an important basis for implementing efficient irrigation and optimal water allocation. Significant disparities in Ky are well documented on spatial scales. Although many related results for Ky under the condition of specific water management have been reported in previous literature, most of the studies have been focused on the value of Ky at an individual site, and few on its spatial variation or spatial patterns. This paper begins to study based on above problem. Therefore, this research has important theoretical significance and practical value.After determining the Ky values at the municipal level in the Haihe Basin, spatial statistical methods and exploratory spatial data analysis (ESDA) were implemented to examine the spatial pattern of Ky for winter wheat. Moran's I coefficient was used to study the global spatial autocorrelation, while Moran scatterplots and local indicators of spatial association (LISA) maps were used to study the local spatial autocorrelation of Ky. In addition, Moran's I values under different spatial directions were calculated to analyze spatial autocorrelation features in various directions including east-west (E-W), northeast-southwest (NE-SW), south-north (S-N) and southeast-northwest (SE-NW).Results showed that the Ky of winter wheat indicated an increasing trend from the western and northern mountainous region to the eastern plain in the basin, with values in the range 0.749~1.668. The global Moran's I values for dry, average, and wet typical growing seasons were 0.6009, 0.6058, 0.6077, respectively, and all with statistically significant differences (p<0.0001). This presents strong evidence of generalized, spatial autocorrelation between Ky at the global scale. The results of local spatial autocorrelation analysis revealed that Ky had a high-high (H-H) cluster in the eastern plain area including Beijing, Tianjin, etc., versus a low-low (L-L) cluster in the northern and western regions such as Chengde, Qinhuangdao, and Datong. Low-high (L-H) and high-low (H-L) clusters appeared to be rare. In addition, the total area in H-H and L-L clusters accounted for 80% of Haihe Basin, half of which exhibited statistical significance (p<0.05). Moreover, the degree of spatial autocorrelation of Ky diminished with the increasing of distance at each and every direction was accordance in trend, and the autocorrelation coefficient approached zero at a distance of 240~280 km. The NE-SW direction played a dominant role in spatial autocorrelation.In summary, the eastern plain area represents a "highly sensitive core region", diverging and decreasing gradually towards the western and northern mountains, forming a "core-periphery" spatial pattern. The research results could present some references valuable for water-saving irrigation and water resources optimal allocation in the Haihe Basin and provide effective clues for further study in other areas.