A new method for modelling precipitation variability in relation to climate change. Issue 1 (14th December 2022)

• Record Type: Journal Article
• Authors: Qamar, Sadia; Ali, Zulfiqar; Sammen, Saad Shauket
• Is Part Of: Journal of water and climate change. Volume 14:Issue 1(2023)
• Page Start: 289
• Page End: 304
• Publication Date: 2022-12-14
• Subjects: auxiliary information -- precipitation concentration -- predictive maps -- regional patterns -- spatial correlation; Water -- Periodicals; Hydrology -- Periodicals; Climatic changes -- Periodicals; Climatic changes; Hydrology; Water; Electronic journals; Periodicals; 333.9116
• DOI: 10.2166/wcc.2022.340
• Languages: English
• ISSNs: 2040-2244

Abstract

Abstract: Climate change is one of the main consequences of anthropogenic activities. Since the 1950s, gradual changes and an increase in climate warming have been observed. Previous research has been indicating potential associations between climate warming and spatiotemporal changes in precipitation. Moreover, the regional patterns of precipitation have a key role in the continuous monitoring of climate characteristics and natural hazards such as floods and droughts. Therefore, precise and accurate measurements of precipitation concentration and spatiotemporal variability in their patterns are very crucial. In this study, a new method for measuring precipitation concentration is developed and applied to 54 meteorological stations in Pakistan. Furthermore, to assess the precipitation patterns, the proposed method provides solid evidence for considering the effect of temperatures under climate warming. Furthermore, using the spatial correlation between the proposed method and its competitor, a comparative analysis is made to evaluate the performance of the proposed method. Moreover, the spatial variability structure in various precipitation patterns is assessed and compared using spatial predictive maps. Outcomes associated with this research show significant deviations between the proposed method and the existing one. In this paper, regression analysis revealed that the additional input can potentially improve the precipitation estimates under the appropriate sampling estimator. This is the first study that has documented the impact of climate warming on measuring precipitation concentration. These findings can contribute to a better understanding of precipitation concentration in relation to climate warming. HIGHLIGHTS: Develop a new method (RCTIPC) for precipitation modelling. Comparison between PCI and RCTIPC for precipitation modelling. Analyzing regional precipitation variability with climate change impact. Spatial variability structure in various precipitation patterns is assessed and compared using spatial predictive maps.