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

Sara Hashempour Motlagh Shirazi (), Davar Khalili (), Shahrokh Zand-Parsa () and Amin Shirvani ()
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Sara Hashempour Motlagh Shirazi: Shiraz University
Davar Khalili: Shiraz University
Shahrokh Zand-Parsa: Shiraz University
Amin Shirvani: Shiraz University

Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, 2022, vol. 24, issue 9, No 35, 11348-11368

Abstract: Abstract 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.

Keywords: Extreme precipitation characteristics; Spatio-temporal analysis; Clustering; Monotonic temporal trends; Short-term temporal trends (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s10668-021-01969-x

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