Particularly now, the severe hazards that present high risks to humans and the ecosystems are extreme climatic patterns [77]. Hence, with evidence showing that the severity of extreme climatic patterns has increased since the 1990s, these events are under serious consideration and gaining attention. As part of a time series analysis, the recognition of a monotonic trend has prime importance. For applying statistical methods, a speculative composition requires the detection of trends. Due to the repercussions of climate variability, the non-homogeneity in the trends of hydrological series was shown. Statistical tests assist in identifying trends in stochastic or non-random arrangements. Understanding their potential or size permits us to make an accurate decision regarding already present trends [78]. The **data** was analyzed through various procedures, which involved performing tests to detect trends, occurrences, and stationarity of the **data** for the whole Punjab region, the results of which are shown in Table 4. The main concern during trend detection is finding out whether the values of a time series dataset have a familiar fluctuation with the increase of time; to this end, the ADF test aims to evaluate whether the average values or variances of a series change with the time. Almost all analysis techniques must suppose amorphous stationarity in the datasets whether the methods are linear or not. Hence, it is compulsory to check the stationarity using some standard and finite modules. At the same time, the examination of non-stationarity may lead to some awareness of the actual physical implementations.