The present study explored the changes in monthly stream ow in the Hindukush-Karakoram-Himalaya (HKH) region within Pakistan using the recently developed trend analysis method known as Innovative Polygon Trend Analysis (IPTA). The monthly stream ow data of 34 gauging stations installed in the HKH region was analyzed, and the Pettitt test was applied to check the homogeneity of the time series. The entire study area was divided into 13 sub-basins, and then changes in monthly stream ow of each subbasin were explored using the IPTA method. The stream ow of high elevated glaciated and snow/icecovered sub-basins (e.g., Shyok, Astore, and Chitral) have increased in high ow months (June-August), where there is a downward trend for the Hunza sub-basin in these months. In the Gilgit sub-basin, a transition occurred from no trend in May to a decreasing trend from June to August. The upstream gauges of Swat and Kabul sub-basins showed an increasing trend throughout the year, while downstream gauging stations in the same sub-basins exhibited a strong decreasing trend only in high ow months (June-August). The upper reaches of the Indus part in the Upper Indus Basin (UIB) showed increasing trends in high ow months (June-August), however the downstream gauges of UIB showed decreasing trends throughout the year. Overall, only the glaciated and snow/ice-covered sub-basins experienced increasing trends, while most other sub-basins faced decreasing trends in high ow months and increasing trends in low ow months (October -March). The enhancement of the seasonal pattern of stream ow in the upper reaches of UIB is possibly due to the melting of snow and ice that potentially in uence oods and hydropower generation. The results of this study can result in a better understanding of the hydrology of the HKH region and support sustainable water management.

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.

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.

The effects of climate change caused by global warming can be seen in changes of climate variables such as precipitation, humidity, and temperatures. These effects of global climate change can be interpreted as a result of the examination of meteorological parameters. One of the most effective methods to investigate these effects is trend analysis methods. Innovative Polygon Trend Analysis (IPTA) method is a trend analysis method that has emerged in recent years. The distinctive features of this method compared with other trend methods are that it depends on time series and can compare data series among themselves. Therefore, in this study, IPTA method was applied to total monthly precipitation data of Susurluk Basin, one of Turkey's important basins. Data of ten precipitation observation stations in Susurluk Basin were used. Data were provided by General Directorate of State Meteorology Affairs. The length of this data series was 12 years (2006–2017). As a result of the study, since there is no regular polygon in IPTA graphics of each station, it is seen that precipitation data varies by years. While this change is seen increasingly at some stations, it is seen decreasingly at other stations.

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