Analysis of Precipitation Data Using Innovative Trend Pivot Analysis Method and Trend Polygon Star Concept: A Case Study of Soan River Basin, Potohar Pakistan

Abstract

The trend analysis approach is adopted for the prediction of future climatological behavior and climate change impact on agriculture, environment, and water resources. In this study, the Innovative Trend Pivot Analysis Method (ITPAM) and Trend Polygon Star Concept Method were applied for precipitation trend detection at eleven stations located in Soan River Basin (SRB), Potohar region Pakistan. Polygon graphics of total monthly precipitation data were created and trends length and slope were calculated separately for arithmetic mean and standard deviation. As a result, the innovative methods produced useful scientific information and helped in identifying, interpreting and calculating monthly shifts under different trend behaviors i.e. increase in some stations and decrease in others of precipitation data. This increasing and decreasing variability emerges from climate change. The risk graphs of the total monthly precipitation and monthly polygonal trends appear to show changes in the trend of meteorological data in the Potohar region of Pakistan. The monsoonal rainfall of all stations shows complex nature of behaviour and monthly distribution is uneven. There is a decreasing trend of rainfall in high land stations of SRB with a significant change between the first data set and the second data set in July and August. It was examined that monsoon rainfall is increasing in lowland stations indicating a shifting pattern of monsoonal rainfall from highland to lowland areas of SRB. The increasing and decreasing trends in different periods with evidence of seasonal variations may cause irregular behaviour in the water resources and agricultural sectors.

Analysis of Temperature Data Using the Innovative Trend Pivot Analysis Method and Trend Polygon Star Concept: A Case Study of Soan River Basin, Potohar, Pakistan

“…Overall, the northern part of the basin is dominated by humid and sub-humid climates, while the central and southern parts are dominated by arid and semi-arid climates, respectively. The northern boundary of SRB is surrounded by the Margalla Hills and the Murree Hills, while the southern boundary is covered by a salt range (Hussain et al, 2022). The climate is continental and subtropical, with hot summers and relatively cold winters.…”

“…The Innovative Trend Pivot Analysis Method (ITPAM) and Trend Polygon Star Concept Method are new trend tests, and only three studies have been reported that used both methods for temperature and precipitation trend analysis (Ceribasi et al, 2021a(Ceribasi et al, , 2021bHussain et al, 2022). The ITPAM determines risk classes by establishing a relationship between data (Ceribasi et al, 2021a).…”

“…Ceribasi et al (2021a) analyzed Susurluk Basin's total monthly precipitation data (2006-2017) using ITPAM and determined the degree of trend risk. Hussain et al (2022) analyzed precipitation data from SRB, Potohar, Pakistan, using ITPAM and the Trend Polygon Star Concept Method.…”

“…In the present study, the same methodology of ITPAM and Trend Polygon Star Concept Method has been applied to analyze SRB average monthly temperature data and further strengthen the adaptability and applicability of both newest methods in academics. Hussain et al (2022) analyzed the qualitative trends of rainfall data using ITPAM and trend polygon star concept method in the selected study area of SRB Potohar region in Pakistan, and in the present study, we further want to analyze the degree of trend risk and observe climate change using ITPAM based on average monthly temperature data.…”

“…According to our understanding, all the parametric and non-parametric methods of trend analysis establish a relationship between data and made definitions such as increasing, decreasing, low, medium, and high, while ITPAM categorizes the risk classes showing changes between available data sets. ITPAM is a modified version of the IPTA method used to determine five risk classes of average monthly temperature data of 11 stations of SRB (Hussain et al, 2022). Moreover, the increasing and decreasing trend regions are divided into five classes for a clear understanding of this method.…”