Din et al. (2026) Assessing Nonstationary Hydroclimatic Impacts on Streamflow in the Soan River Basin, Pakistan, Using Mann–Kendall Test and Artificial Neural Network Technique

Identification

• Journal: Hydrology
• Year: 2026
• Date: 2026-04-01
• Authors: Rafi Ul Din, Saddam Hussain, Adeel Ahmad Khan, Muhammad Naveed Anjum, A. T. M. Sakiur Rahman, Shahid Ullah
• DOI: 10.3390/hydrology13040106

Research Groups

• Department of Land and Water Conservation Engineering, PMAS-Arid Agriculture University, Rawalpindi, Pakistan
• Department of Agricultural and Biological Engineering, Tropical Research and Education Center (TREC), University of Florida, Homestead, FL, USA
• State Key Laboratory of Cryosphere Sciences, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, China
• Department of Environmental Sciences, University of California, Riverside, CA, USA
• Agricultural Engineering Department, Balochistan, Quetta, Pakistan

Short Summary

This study assessed long-term nonstationary hydroclimatic impacts on streamflow in the Soan River Basin, Pakistan, revealing a warming trend, decreasing precipitation, and a significant decline in streamflow, with streamflow patterns being highly responsive to these changes.

Objective

• To determine long-term variations and nonstationary impacts of precipitation, temperature, and streamflow in the Soan River Basin of Pakistan, using three decades of in situ records (1991–2020), integrating both linear and non-linear trend analysis.

Study Configuration

• Spatial Scale: Soan River Basin, Pakistan
• Temporal Scale: 30 years (1991–2020)

Methodology and Data

• Models used: Mann–Kendall (non-parametric trend test), Artificial Neural Network (ANN)
• Data sources: Three decades of in situ records for precipitation, temperature, and streamflow

Main Results

• The Soan River Basin is experiencing a consistent warming trend.
• The annual average precipitation over the entire basin is decreasing at a rate of -7.33 mm/year.
• The trend of monsoon precipitation was less certain compared to the westerly season.
• Annual average streamflow is decreasing at a rate of -0.47 m³/year according to the Mann–Kendall test, and -1.30 m³/year according to the ANN approach.
• A moderate positive correlation exists between precipitation and streamflow, indicating precipitation as a primary driver of flows.
• The ANN approach more precisely demonstrates the non-linear behavior of hydroclimatic variables compared to the Mann–Kendall test, although both showed similar decreasing tendencies.
• Streamflow patterns are considerably responsive to the warming of the basin and changing precipitation behavior.

Contributions

• Integrates traditional statistical (Mann–Kendall) and advanced machine learning (Artificial Neural Network) approaches to assess hydroclimatic trends, addressing the limitations of traditional methods in capturing non-linear behaviors.
• Provides a more precise understanding of non-linear hydroclimatic variability and its impacts on streamflow in a complex topographic and climatic regime.
• Offers critical findings for sustainable water resource management and planning in the Soan River Basin, Pakistan, by highlighting the responsiveness of streamflow to warming and changing precipitation.

Funding

Not specified in the provided text.

Citation

@article{Din2026Assessing,
  author = {Din, Rafi Ul and Hussain, Saddam and Khan, Adeel Ahmad and Anjum, Muhammad Naveed and Rahman, A. T. M. Sakiur and Ullah, Shahid},
  title = {Assessing Nonstationary Hydroclimatic Impacts on Streamflow in the Soan River Basin, Pakistan, Using Mann–Kendall Test and Artificial Neural Network Technique},
  journal = {Hydrology},
  year = {2026},
  doi = {10.3390/hydrology13040106},
  url = {https://doi.org/10.3390/hydrology13040106}
}