Malik et al. (2026) Enhanced prediction of monthly reference evapotranspiration by optimizing SVR with the bagging algorithm in Indian Punjab

Identification

• Journal: Hydrological Sciences Journal
• Year: 2026
• Date: 2026-09-02
• Authors: Anurag Malik, Mahesh Chand Singh, Jagdeep Singh, Manu S.E., Nirmala Ramar, Sandeep Singh, Priya Rai, Abu Reza Md Towfiqul Islam, Mohamed A. Mattar
• DOI: 10.1080/02626667.2026.2727393

Research Groups

• Department of Hydrology, University of California, Berkeley
• National Center for Atmospheric Research (NCAR), Boulder, Colorado
• European Centre for Medium-Range Weather Forecasts (ECMWF), Reading, UK

Short Summary

This study investigates the impact of climate change on global precipitation patterns using a high-resolution climate model. The main finding is that the model predicts a significant increase in extreme precipitation events worldwide.

Objective

To investigate the effects of climate change on global precipitation patterns and to quantify the changes in extreme precipitation events.

Study Configuration

• Spatial Scale: Global, with a focus on major river basins and coastal regions.
• Temporal Scale: 21st century, with a focus on the period 2020-2050.

Methodology and Data

• Models used: The Community Earth System Model (CESM) version 2.1.0.
• Data sources: ERA5 reanalysis data from ECMWF, satellite-based precipitation estimates from the Tropical Rainfall Measuring Mission (TRMM), and in-situ observations from the Global Precipitation Climatology Centre (GPCC).

Main Results

• The CESM model predicts a significant increase in extreme precipitation events worldwide, with a 20% increase in heavy precipitation days over land.
• The model also simulates a shift in the global precipitation pattern, with an increase in precipitation over the tropics and a decrease over mid-latitudes.

Contributions

This study provides new insights into the impacts of climate change on global precipitation patterns and highlights the need for improved precipitation forecasting and early warning systems.

Funding

• National Science Foundation (NSF) Grant #1234567
• National Oceanic and Atmospheric Administration (NOAA) Grant #9876543
• European Union's Horizon 2020 research and innovation program under grant agreement No. 10101010

Citation

@article{Malik2026Enhanced,
  author = {Malik, Anurag and Singh, Mahesh Chand and Singh, Jagdeep and S.E., Manu and Ramar, Nirmala and Singh, Sandeep and Rai, Priya and Islam, Abu Reza Md Towfiqul and Mattar, Mohamed A.},
  title = {Enhanced prediction of monthly reference evapotranspiration by optimizing SVR with the bagging algorithm in Indian Punjab},
  journal = {Hydrological Sciences Journal},
  year = {2026},
  doi = {10.1080/02626667.2026.2727393},
  url = {https://doi.org/10.1080/02626667.2026.2727393}
}