Latif et al. (2025) Transition of the Karakoram anomaly under emerging hydroclimatic trends

Identification

• Journal: The Science of The Total Environment
• Year: 2025
• Date: 2025-11-13
• Authors: Yasir Latif, S. C. Pryor, Sardar Ateeq-Ur-Rehman, Sher Muhammad, Muhammad Yaseen, Muhammad Atif Wazir
• DOI: 10.1016/j.scitotenv.2025.180678

Research Groups

• Department of Earth and Atmospheric Sciences, Cornell University, USA
• Department of Complex Systems, Institute of Computer Science of the Czech Academy of Sciences, Czech Republic
• Antea Group Belgium, Belgium
• International Center for Integrated Mountain Development (ICIMOD), Nepal
• Center for Integrated Mountain Research, University of the Punjab, Pakistan
• Pakistan Meteorological Department, Pakistan

Short Summary

This study examines the amplitude and temporal evolution of the Karakoram Anomaly (KA) by analyzing hydroclimatic trends in the Hunza River Basin (HRB) and develops an Artificial Neural Network for runoff simulation.

Objective

• To examine the amplitude and temporal evolution of the Karakoram Anomaly (KA) by observing hydroclimatic (temperature, precipitation, snow, and streamflow) trends in the extensive snow/glacier-fed Hunza River Basin (HRB), and to establish a hydroclimatic relationship for runoff simulation.

Study Configuration

• Spatial Scale: Hunza River Basin (HRB), Karakoram region.
• Temporal Scale: 1995–2021 for in situ hydroclimatic data and reanalysis/satellite products; 2001–2020 for MODIS Snow Covered Area (SCA).

Methodology and Data

• Models used: Artificial Neural Networks (ANNs) for runoff simulation; Wavelet Transfer Function (WTF), Innovative Trend Analysis (ITA), and Mann-Kendall (MK) tests for trend validation.
• Data sources: Daily in situ hydroclimatic data (temperature, precipitation, snow, streamflow); reanalysis/satellite products; MODIS Snow Covered Area (SCA).

Main Results

• Quantified the persistence of the Karakoram Anomaly (KA) and investigated its magnitude and temporal evolution.
• Validated the direction and extent of secular hydroclimatic trends (temperature, precipitation, snow, and streamflow) using WTF, ITA, and MK tests.
• Established a hydroclimatic relationship for the Hunza River Basin using Artificial Neural Networks to simulate runoff.

Contributions

• Provides a comprehensive analysis of the amplitude and temporal evolution of the Karakoram Anomaly, addressing recent challenges to its persistence.
• Applies multiple advanced statistical methods (Wavelet Transfer Function, Innovative Trend Analysis, Mann-Kendall tests) to validate hydroclimatic trends in a critical glacier-fed basin.
• Develops an Artificial Neural Network model for runoff simulation in the Hunza River Basin, enhancing understanding of regional hydroclimatic relationships.

Funding

• Not specified in the provided text.

Citation

@article{Latif2025Transition,
  author = {Latif, Yasir and Pryor, S. C. and Ateeq-Ur-Rehman, Sardar and Muhammad, Sher and Yaseen, Muhammad and Wazir, Muhammad Atif},
  title = {Transition of the Karakoram anomaly under emerging hydroclimatic trends},
  journal = {The Science of The Total Environment},
  year = {2025},
  doi = {10.1016/j.scitotenv.2025.180678},
  url = {https://doi.org/10.1016/j.scitotenv.2025.180678}
}