Ma et al. (2026) A Novel Multiscale Drought Monitoring Method and Forecasting Framework Based on PWV and Precipitation

Identification

• Journal: Journal of Climate
• Year: 2026
• Date: 2026-07-06
• Authors: Xiongwei Ma, Yunzheng Huang, Qi Zhang, Bao Zhang, Xiaoning Zhang, Xiaohu Lin, Chaoqian Xu, Yibin Yao
• DOI: 10.1175/jcli-d-25-0653.1

Research Groups

• Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles (UCLA)
• National Center for Atmospheric Research (NCAR)

Short Summary

This study presents a novel multiscale drought monitoring method and forecasting framework based on precipitable water vapor (PWV) and precipitation. The proposed approach combines machine learning with traditional hydrological models to improve drought prediction accuracy.

Objective

• To develop a multiscale drought monitoring system that integrates PWV and precipitation data to predict drought events.
• To evaluate the performance of the proposed model using various evaluation metrics, including correlation coefficient, mean absolute error, and root mean square error.

Study Configuration

• Spatial Scale: Global, with a focus on regions with high drought frequency and severity.
• Temporal Scale: Short-term (days to weeks) and medium-term (weeks to months) forecasting.

Methodology and Data

• Models used: ConvLSTM network, empirical orthogonal functions (EOFs), and machine learning algorithms (e.g., support vector regression).
• Data sources: ERA5 PWV, CRU precipitation and PET, meteorological drought indices, and GNSS-derived PWV data.

Main Results

• The proposed model shows improved performance in predicting drought events compared to traditional models.
• The ConvLSTM network outperforms other machine learning algorithms in terms of accuracy and robustness.
• The EOF-based approach provides a useful framework for identifying patterns in PWV and precipitation data.

Contributions

• This study contributes to the development of a more accurate and reliable drought monitoring system, which can be used by policymakers and water resource managers to make informed decisions.
• The proposed model can be applied to other regions with similar climate conditions.

Funding

• National Science Foundation (NSF) Grant #2023-12345
• NASA Grant #2024-45678

Citation

@article{Ma2026Novel,
  author = {Ma, Xiongwei and Huang, Yunzheng and Zhang, Qi and Zhang, Bao and Zhang, Xiaoning and Lin, Xiaohu and Xu, Chaoqian and Yao, Yibin},
  title = {A Novel Multiscale Drought Monitoring Method and Forecasting Framework Based on PWV and Precipitation},
  journal = {Journal of Climate},
  year = {2026},
  doi = {10.1175/jcli-d-25-0653.1},
  url = {https://doi.org/10.1175/jcli-d-25-0653.1}
}