USDM and IDI
Multivariate-based integrated drought monitoring systems within the research focus on drought modeling and probabilistic drought monitoring.
Overview
The USDM and IDI are presented as multivariate-based integrated systems for drought monitoring. They fit within Dr. Moradkhani’s broader work on hydrologic science, water resources system analysis, uncertainty analysis, and computational modeling.
The research team’s technical focus includes predictive science, machine learning, data analytics, and high-performance computing—methods used to examine complex hydrologic conditions.
Monitoring Framework
| System | Role | Research context |
|---|---|---|
| USDM | Drought monitoring within the integrated framework. | Multivariate drought analysis and probabilistic monitoring. |
| IDI | Drought monitoring within the integrated framework. | Multivariate drought analysis and predictive assessment. |
The evidence available for this page identifies the systems and their multivariate-based monitoring purpose, but does not specify individual input variables, temporal resolution, spatial scale, or downloadable datasets.
Research Context
- Drought monitoring
- Assessing drought conditions through multivariate information rather than a single indicator.
- Uncertainty analysis
- Quantifying uncertainty in hydrologic predictions and monitoring results.
- Machine learning
- Using data-driven methods to support predictive drought science.
- Hydrologic modeling
- Applying computational models to complex water-resources systems.
For related research themes, see the sections on Drought Modeling and Probabilistic Drought Monitoring and Forecasting.