Climate classification through recursive multivariate statistical inferences: a case study of the Athabasca River Basin, Canada

Abstract

In this study, a recursive dissimilarity and similarity inferential climate classification (ReDSICC) approach is developed to provide an alternative tool for climate classification. Based on incorporation of a discrete distribution transformation (DDT) method and integration of advanced statistical inferential methods, a recursive framework of dissimilarity and similarity inferences is proposed for stepwise grouping multi‐dimensional climate‐variable observations. ReDSICC is capable of eliminating the restriction of samples being normally distributed, enabling classification of regional climates under data uncertainties and multivariate dependencies, identifying the most desired climate classification result, and avoiding subjective judgments in the classification process. To verify methodological effectiveness and facilitate related studies, ReDSICC is applied to climate classification in the Athabasca River Basin (ARB), Canada. It is revealed that the complicated dissimilarities and similarities of climatic conditions among all grids over the ARB are effectively reflected in the results of ReDSICC. A reversible transformation between an abnormal distribution and a normal distribution is achieved by DDT. The effectiveness of climate classification which is represented as the Nash coefficient for climatic features over any grid and the corresponding climate class is decreased if DDT is not employed. In comparison with daily minimum temperature, the spatial heterogeneity of daily maximum temperature is higher while that of daily cumulative precipitation is lower over the ARB. The classification result of ReDSICC varies with changes of representative climate variables and parameter values. These advantages and revelations are helpful for enhancing the reliability of climate classification results, improving the effectiveness of existing climate classification methods, and providing scientific support for the related studies in the ARB or neighbouring regions.

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

“…For these groups, the climate variables are significantly different between any two and insignificantly different within any one [ Cheng et al ., ], which is the most representative advantage of ReDSICC in comparison with other existing approaches. This is mainly achieved through a recursive classification/dissimilarity and clustering/similarity process based on statistical inferences [ Cheng et al ., ].…”

“…In the first module of the FIGGRA approach, the grouping is enabled through a systematic analysis of the spatial heterogeneity and homogeneity among these grids based on advanced statistical inferences. [Shapiro and Wilk, 1965;Royston, 1995]; DDT: the discrete distribution transformation approach [Cheng et al, 2016a[Cheng et al, , 2016b; MNV test: the modified Nel and van der Merwe test [Krishnamoorthy and Yu, 2004]; α: statistical significance level; Nmin: minimum row number; ReDSICC: recursive dissimilarity and similarity inferential climate classification [Cheng et al, 2017].…”

Recursive multivariate principal‐monotonicity inferential climate downscaling

“…There are significant temporal and spatial differences among the local climatic conditions over the whole region, which was elaborated in Cheng et al . () and the supporting information of this article (Figures S1 to S5 in File S1). However, the spatial resolution of these differences is finer than that of CMIP5 GCMs .…”

“…On the contrary, the ARB includes diverse hydro-climatic regimes due to physiographical heterogeneity; snow-capped mountains, coniferous forest, mixed wood and deciduous forest are found in the uplands, whereas willow brush, shrubs, black spruce and sphagnum moss dominate the lowlands (Kerkhoven and Gan, 2006). The region can be divided into nine sub-catchments (Figure 2(b)) and 20 climate zones (Figure 2(d)) (Cheng et al, 2017a). There are significant temporal and spatial differences among the local climatic conditions over the whole region, which was elaborated in Cheng et al (2017a) and the supporting information of this article (Figures S1 to S5 in File S1).…”

Hydraulic correction method (HCM) to enhance the efficiency of SRTM DEM in flood modeling

“…GR model, Perrin et al, 2003) deal with the system as a whole and do not consider the spatial heterogeneity of the problem domain (Khakbaz et al, 2012). However, flooding may present clear spatial variations as influenced by localized weather and topographic conditions (Chen et al, 2017a;Cheng et al, 2017c;Cheng et al, 2017d). Distributed hydrological models (e.g.…”