The Innovative Polygon Trend Analysis (IPTA) as a Simple Qualitative Method to Detect Changes in Environment—Example Detecting Trends of the Total Monthly Precipitation in Semiarid Area

Abstract

Precipitation is a crucial component of the water cycle, and its unpredictability may dramatically influence agriculture, ecosystems, and water resource management. On the other hand, climate variability has caused water scarcity in many countries in recent years. Therefore, it is extremely important to analyze future changes of precipitation data in countries facing climate change. In this study, the Innovative Polygon Trend Analysis (IPTA) method was applied for precipitation trend detection at seven stations located in the Wadi Sly basin, in Algeria, during a 50-year period (1968–2018). In particular, the IPTA method was applied separately for both arithmetic mean and standard deviation. Additionally, results from the IPTA method were compared to the results of trend analysis based on the Mann–Kendall test and the Sen’s slope estimator. For the different stations, the first results showed that there is no regular polygon in the IPTA graphics, thus indicating that precipitation data varies by years. As an example, IPTA result plots of both the arithmetic mean and standard deviation data for the Saadia station consist of many polygons. This result means that the monthly total precipitation data is not constant and the data is unstable. In any case, the application of the IPTA method showed different trend behaviors, with a precipitation increase in some stations and decrease in others. This increasing and decreasing variability emerges from climate change. IPTA results point to a greater focus on flood risk management in severe seasons and drought risk management in transitional seasons across the Wadi Sly basin. When comparing the results of trend analysis from the IPTA method and the rest of the analyzed tests, good agreement was shown between all methods. This shows that the IPTA method can be used for preliminary analysis trends of monthly precipitation.