Factorial inferential grid grouping and representativeness analysis for a systematic selection of representative grids

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.

Multifactorial Principal‐Monotonicity Inference for Macro‐Scale Distributed Hydrologic Modeling

“…The climate classification is achieved by recursive cutting and merging based on objective statistical inferences. Due to this and according to integrative analyses of multiple metrics characterizing classification effectiveness, both intra-zone similarity and inter-zone dissimilarity of the climate variables in the classification method are, on average, higher than those in other methods relying on subjective classifications (Figure S2 in Supporting Information S1) (Cheng, Huang, Dong, Xu, & Yao, 2017). This makes MFPMI be skilled in classifying regional climates of tempo-spatial heterogeneities.…”