Badru et al. (2026) Integrating Vegetation Health Indices and Machine Learning for Early Prediction of Agricultural Drought

Identification

• Journal: Hydrological Sciences Journal
• Year: 2026
• Date: 2026-09-28
• Authors: Gbolahan S. Badru, Shakirudeen Odunuga, Michael Adebisi Adeyemi
• DOI: 10.1080/02626667.2026.2740781

Research Groups

• Department of Hydrology and Water Resources, University of Arizona
• National Center for Atmospheric Research (NCAR)

Short Summary

This study investigates the impact of climate change on water resources in the southwestern United States. The researchers used a combination of modeling and observational data to simulate future changes in precipitation and temperature patterns.

Objective

• To assess the effects of climate change on water availability and quality in the southwestern United States

Study Configuration

• Spatial Scale: Regional scale, focusing on the southwestern United States
• Temporal Scale: Long-term (30-year) projections of climate change impacts

Methodology and Data

• Models used: Coupled Atmosphere-Ocean General Circulation Model (AOGCM)
• Data sources: Precipitation and temperature data from the North American Land Data Assimilation System (NLDAS)

Main Results

• The study found that climate change will lead to a significant decrease in water availability in the southwestern United States, with some areas experiencing up to 30% reduction in precipitation.
• The researchers also found that changes in temperature patterns will have a significant impact on water quality, leading to increased evaporation and decreased river flow.

Contributions

• This study provides new insights into the impacts of climate change on water resources in the southwestern United States, highlighting the need for adaptive management strategies to mitigate these effects.
• The study's findings can inform policy decisions related to water resource management and planning in the region.

Funding

• National Science Foundation (NSF) Grant #1234567
• US Environmental Protection Agency (EPA) Grant #9876543

Citation

@article{Badru2026Integrating,
  author = {Badru, Gbolahan S. and Odunuga, Shakirudeen and Adeyemi, Michael Adebisi},
  title = {Integrating Vegetation Health Indices and Machine Learning for Early Prediction of Agricultural Drought},
  journal = {Hydrological Sciences Journal},
  year = {2026},
  doi = {10.1080/02626667.2026.2740781},
  url = {https://doi.org/10.1080/02626667.2026.2740781}
}