The spatial structure of the variance related to hydroclimatic datasets over multi-decadal timescales was studied for Mexico. The changes of variance at different spatial scales were investigated for precipitation and temperature normals by variographic analysis. The objective of the study was to determine the proper spatial scales for studying key climatic elements. Isotropy, anisotropy, 12 different models, and 50 distances from 0.1 to 100% of the study area diameter were tested for raw temperature and precipitation normals, as well as for the residuals resulting from polynomial detrending. Each variogram was tested, and the suitable ones were used to describe and understand the spatial variation of the phenomena. The variance structure in the data of each climatological element varies as a function of the scale, relief, and the application of detrending because the spatial evolution of the phenomena is complex. The active lag distances related to structures that best fit the data belong to meso-β scale and are shorter than the study area radius. Moreover, various mesoscale distances highlight different structures that have physical meanings.

在墨西哥研究了十年来与水文气候数据集有关的方差的空间结构。通过方差分析研究了降水和温度常态在不同空间尺度上的方差变化。该研究的目的是确定适当的空间尺度，以研究关键的气候要素。测试了各向同性，各向异性，12种不同模型以及研究区域直径的0.1％到100％的50个距离，以测试原始温度和降水法线以及多项式去趋势导致的残差。测试了每个变异函数，并使用合适的变异函数来描述和理解现象的空间变化。每个气候要素数据中的方差结构随尺度，起伏，以及去趋势的应用，因为现象的空间演化是复杂的。与最适合数据的结构相关的活动滞后距离属于meso-β尺度，并且比研究区域半径短。此外，各种中尺度距离突出了具有物理意义的不同结构。