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The identification of homogeneous precipitation regions is essential in the planning, design and management of water resources systems. Regions are identified using a technique that partitions climate sites into groups based on the similarity of their attributes; the procedure is known as regionalization. In this paper the ability of four attribute sets to form large, coherent precipitation zones is assessed in terms of the regional homogeneity of precipitation statistics and computational efficiency. The outcomes provide guidance for effective attribute selection for future studies in Canada. The attributes under consideration include location parameters (latitude, longitude), distance to major water bodies, site elevation and atmospheric variables modelled at different pressure levels. The analysis is conducted in two diverse climate regions within Canada including the Prairie and the Great Lakes–St Lawrence lowlands regions. The method consists of four main steps: (i) formation of the attribute sets; (ii) determination of the preferred number of regions (selection of thec-value) into which the sites are partitioned; (iii) regionalization of climate sites using the fuzzyc-means clustering algorithm; and (iv) validation of regional homogeneity usingL-moment statistics. The results of the attribute formation,c-value selection, regionalization and validation processes are presented and discussed in a comparative analysis. Based on the results it is recommended for both regions to use location parameters including latitude, longitude and distance to water bodies (in the Great Lakes region) to form precipitation regions and to consider atmospheric variables for future (climate change) applications of the regionalization procedure. [ABSTRACT FROM PUBLISHER]

When it comes to projecting the potential effects of climate change on hydro-climatic variables using time-series models, the conventional approach has been to examine correlations with exogenous variables. Establishing correlations among endogenous and exogenous variables, however, cannot guarantee that there is a cause-effect relationship among the variables. This study, therefore, used Granger-causality for a more accurate alternative to the exogenous variables needed to expand time-series models. To demonstrate this, Maharlou Lake, Iran was selected for a case study not only because this inland water body has been exhibiting unprecedented depletion patterns recently, but also because studies are projecting that a changing local climate could add pressure to the region's water resources. Both restricted and extended models reveal that shrinkage observed in the lake's time-series data is expected to continue in the near future. This depletion, however, is projected to be more pronounced in August, September, and October, and milder in February, March, and April. Furthermore, the results from the extended model hint at a more severe pattern of shrinkage rooted in the adverse impacts of projected climate change. [ABSTRACT FROM AUTHOR]