Assessing Nonstationary Hydroclimatic Impacts on Streamflow in the Soan River Basin, Pakistan, Using Mann–Kendall Test and Artificial Neural Network Technique

Analysis of the hydroclimatic variations in complex topographic and climatic regimes is important in determining the freshwater availability and its response. Although several previous studies have assessed the changing patterns of hydroclimatic variables in South Asian River basins, most of them have considered traditional statistical methods, which may inadequately reflect potential non-linear hydroclimatic trends. This study determines long-term variations in precipitation, temperature, and streamflow in the Soan River Basin of Pakistan, using three decades of in situ records (1991–2020). A non-parametric (Mann–Kendall) trend test along with an artificial neural network (ANN) approach was used to check the linear and non-linear trends. The results exhibited that the basin was getting warmer at a consistent rate, although the amount of precipitation varied significantly with location and season. The annual average amount of precipitation over the entire basin was decreasing at the rate of −7.33 mm/year. As compared to the westerly season, the trend of monsoon precipitation was less certain. Changes in streamflow patterns generally demonstrated the consequences of changing precipitation and rising temperature patterns. The annual average streamflow was decreasing at the rate of −0.47 (−1.30) m3/year, as per the results of MK (ANN). A moderate positive correlation between precipitation and streamflow indicates that precipitation mainly governed the flows in the basin. The results of the MK test and the machine-learning approach demonstrated the similar decreasing tendencies of hydroclimatic variables. However, the ANN approach more precisely demonstrates the non-linear behavior of hydroclimatic variables. It was concluded that the streamflow patterns were considerably responsive to the warming of the Soan River Basin, as well as to the changing behavior of precipitation. These findings emphasized the significance of integrating statistical and machine-learning approaches to enhance the comprehension of hydroclimatic trends. Results of this research could be applicable in sustainable management and planning of the water resources within the basin.