Open AccessFactorial inferential grid grouping and representativeness analysis for a systematic selection of representative grids
Author(s) -
Cheng Guanhui,
Huang Guohe,
Dong Cong,
Xu Ye,
Yao Yao
Publication year - 2017
Publication title -
earth and space science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.843
H-Index - 23
ISSN - 2333-5084
DOI - 10.1002/2017ea000297
Subject(s) - representativeness heuristic , grid , statistics , selection (genetic algorithm) , normalization (sociology) , loess plateau , environmental science , computer science , econometrics , mathematics , data mining , geography , artificial intelligence , geodesy , soil science , sociology , anthropology
Abstract A factorial inferential grid grouping and representativeness analysis ( FIGGRA ) approach is developed to achieve a systematic selection of representative grids in large‐scale climate change impact assessment and adaptation ( LSCCIAA ) studies and other fields of Earth and space sciences. FIGGRA is applied to representative‐grid selection for temperature ( Tas ) and precipitation ( Pr ) over the Loess Plateau ( LP ) to verify methodological effectiveness. FIGGRA is effective at and outperforms existing grid‐selection approaches (e.g., self‐organizing maps) in multiple aspects such as clustering similar grids, differentiating dissimilar grids, and identifying representative grids for both Tas and Pr over LP . In comparison with Pr , the lower spatial heterogeneity and higher spatial discontinuity of Tas over LP lead to higher within‐group similarity, lower between‐group dissimilarity, lower grid grouping effectiveness, and higher grid representativeness; the lower interannual variability of the spatial distributions of Tas results in lower impacts of the interannual variability on the effectiveness of FIGGRA . For LP , the spatial climatic heterogeneity is the highest in January for Pr and in October for Tas ; it decreases from spring, autumn, summer to winter for Tas and from summer, spring, autumn to winter for Pr . Two parameters, i.e., the statistical significance level ( α ) and the minimum number of grids in every climate zone ( Nmin ), and their joint effects are significant for the effectiveness of FIGGRA ; normalization of a nonnormal climate‐variable distribution is helpful for the effectiveness only for Pr . For FIGGRA ‐based LSCCIAA studies, a low value of Nmin is recommended for both Pr and Tas , and a high and medium value of α for Pr and Tas , respectively.