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Application of non parametric innovative trend analysis of different time scale precipitation during (1951-2016) in Khyber Pakhtunkhwa, Pakistan

100%

Gul S. , Ren J.

tom Vol. 70, no 1

485--503

Reliable information on the frequency and duration of excessive precipitation in foods, droughts, earthquakes, coastal foods, and hill torrents is critical to natural disaster planning and disaster risk reduction strategies. The current study examined precipitation on a monthly, seasonal, and annual scale at varying amplitudes. Moreover, the Mann–Kendall and Sen Innovative trend analysis (ITA) approaches are used to examine precipitation variations. This study aims to evaluate the Mann–Kendall and Sen Innovative Trend Analysis techniques to understand better how they apply to the topic under consideration. Overall, 84.16% of testing months showed trendless precipitation based on the MK trend test. Comparatively, the ITA monthly analysis showed statistically significant variation in 80% months and 88% considerable rate in seasonal perspective over the entire study regions. The research recognized that the Sen Innovative trend test outperforms the Mann–Kendall analysis in a range of circumstances. First of all, Sen Approach has simple assumptions, and the study of skewed distributions with fewer data could apply. Another benefit of using the ITA was that all data sets could be viewed on a graph, making it easier to see pat terns and interpret the trends. Thus, the research recommends that the Sen Trend Method (ITA) analyze monthly, seasonal, and annual precipitation patterns to facilitate water resource scheduling and establish natural disaster strategies in the future.

Application of hierarchical cluster analysis to spatiotemporal variability of monthly precipitation over Khyber Pakhtunkhwa, Pakistan

80%

Tajbar S. , Tajbar A. , Pashaie Z. , Khorshiddoust A. M. , Rafiq L.

tom Vol. 72, no. 2

1159--1174

Understanding the long-term spatiotemporal variability of precipitation at the regional scale is critical for developing flood and drought control strategies and water resource management. This study assessed the spatiotemporal variability of monthly precipitation over the Khyber Pakhtunkhwa province of Pakistan for 1998-2019 using hierarchical cluster analysis to cluster 156 Tropical Rainfall Measuring Mission grids. Statistical properties of clusters were calculated and the relationship of geographical features such as latitude, longitude, and altitude and statistical variables including standard deviation, maximum and minimum precipitation, and coefficient of variation (CV) with average precipitation was assessed. Findings showed that northeast parts received maximum precipitation while north and southern regions received less precipitation. Temporal analysis showed two clusters of rainy months (February, March, April, May, July, and August) and dry months (January, June, September, October, November, and December). The region was divided into two homogeneous precipitation regions. From January to April and November to December, cluster 1 occupied northern parts with maximum average precipitation while cluster 2 southern parts. From June to September, cluster 2 covered the northeast and southern parts with the highest average precipitation. During May, cluster 2 received the highest average precipitation in the northeast and southeast parts, whereas cluster 1 covered the northwest and southwest. In October, cluster 2 received maximum average precipitation covering the northeast. CV suggested higher temporal variability in cluster 2 (67.75-102.36)% than cluster 1 (65.82-99.55)%. Precipitation correlation showed that CV opposed the longitude and averages, whereas latitude and altitude demonstrated minimal correlations. These insights can assist decision-makers in devising suitable strategies to plan and control unexpected volumes of precipitation.