Spatio-temporal variability of extreme precipitation characteristics under different climatic conditions in Fars province, Iran

• Published in: Environment, development and sustainability development and sustainability, 2022-09, Vol.24 (9), p.11348-11368
• Main Authors: Hashempour Motlagh Shirazi, Sara, Khalili, Davar, Zand-Parsa, Shahrokh, Shirvani, Amin
• Format: Article
• Language: English
• Subjects: Algorithms; Changes; Climatic conditions; Cluster analysis; Clustering; Earth and Environmental Science; Ecology; Economic Geology; Economic Growth; Environment; Environmental Economics; Environmental Management; Extreme weather; Mitigation; Original Paper; Precipitation; Regional planning; Short term; Sustainable Development; Trends; Variability
• ISSN: 1387-585X; 1573-2975
• DOI: 10.1007/s10668-021-01969-x

Spatio-temporal variability of extreme precipitation characteristics (EPCs) were analyzed using clustering techniques to establish homogeneous sub-regions (clusters), the nonparametric Mann–Kendall (M–K) test to detect significant monotonic temporal trend and the nonparametric Lepage (LP) test to detect change-points (jumps) representing significant short-term temporal trend. The study area is Fars province (southern Iran), exhibiting diverse climatic conditions within relatively small area. Detailed clustering analysis involved utilization of eight algorithms and four validation indices. Consequently, one algorithm was selected, suggesting seven clusters (C1 to C7) for the study area. EPCs were identified by eight variables, which were used for temporal analysis. The M–K test utilizing within cluster EPC data did not detect any significant trend (5% or 1% levels), for the study period (1976–2013). However, the LP test conducted at 10- and 5-year time-steps showed significant change-points (5% level) in temporal behavior of EPCs in every cluster. Furthermore, “between-cluster variability” was strong as shown in the number of EPCs with significant change-point. For 10-year time-step, C1 and C2, respectively, had two and eight EPCs with significant change-points, representing minimum and maximum number of EPCs. Remaining clusters had between three and seven EPCs with significant change-point. The 5-year time-step also showed strong EPC variability (within and between clusters). According to results for regions with diverse climatic conditions, detailed spatio-temporal analyses of EPCs should include proper identification of homogenous sub-regions (clusters) and also detection of both long-term and short-term (change-point) temporal trends by tests such as the M–K and LP. Results from this type of research can be used as part of the information, which is necessary for flood mitigation/prevention planning at the regional scale.