Research Areas
The research program focuses on hydrologic science, water resources system analysis, and computational modeling, combining remotely sensed and ground observations with advanced numerical methods.
Core Research Themes
-
Data Assimilation and Remote Sensing
Combining satellite-derived observations with in-situ measurements and mathematical models to improve estimates of land surface states such as soil moisture.
-
Drought Modeling
Analysis of drought onset, duration, and recovery, together with water use efficiency and the vulnerability of water-dependent systems during dry periods.
-
Extreme Events and Flood Modeling
Studying the hydrologic response to extreme precipitation events and developing modeling approaches for flood estimation and forecasting.
-
Climate Variability and Change
Examining how shifting climate patterns influence precipitation, streamflow, and water availability across scales.
-
Hydrologic Modeling
Development and evaluation of computational hydrologic models, including ensemble and multimodel techniques for water resources system analysis.
Computational and Analytical Methods
The team works with a combined data-driven and process-based toolchain, applying these methods across the research themes listed above.
- Data revolution
- Predictive science
- Uncertainty analysis
- Machine learning
- Data analytics
- High-performance computing
Drought Monitoring and Analysis
Ongoing work in drought monitoring covers probabilistic drought monitoring and forecasting, interactive drought type maps, interactive probability maps, static maps, drought recovery and water use efficiency, and drought vulnerability, risk, and mortality analysis.
Data products developed by the group are described on the SMAP and USDM/IDI pages linked from the research menu.
The research program described here sits at the intersection of hydrologic science, water resources system analysis, and computational modeling. Rather than working from models alone, the approach combines remotely sensed observations with ground-based measurements, using each to constrain and validate the other. This emphasis on harnessing data-driven and machine learning tools reflects a broader goal: advancing predictive capability while properly accounting for uncertainty in the estimates that decision-makers rely on.
A central theme is data assimilation and remote sensing, in which satellite-derived signals are merged with in-situ measurements and physical models to improve estimates of land surface states, soil moisture among them. The work treats each information source as a partial and imperfect window onto the system, so combining them through inference and probabilistic methods yields better characterizations of conditions on the ground than any single input could provide on its own.
Drought modeling forms another major theme, covering the onset, duration, and recovery of dry periods along with water use efficiency and the vulnerability of water-dependent systems during droughts. Multi-modeling, downscaling, and ensemble and post-processing techniques are used to translate coarse predictions into local detail, and multimodeling with probabilistic reasoning helps quantify the range of possible outcomes rather than a single deterministic answer.
Attention also extends to extreme events, including floods and other high-impact phenomena, where forecasting skill, recovery, and risk to people and infrastructure become the focus. Interactive maps, monitoring tools, and published papers and journal articles make the results accessible to researchers and practitioners, while the emphasis on efficiency, mortality risk, and sustainable management connects the science directly to public benefit and informed resource decisions.
Hydrologic Modeling Toolbox
Supporting methods for hydrologic modeling include Bayesian multimodeling and meteorological and hydrological postprocessing, used to translate raw model output into decision-ready information about floods, droughts, and water availability.
| Theme | Supporting methods |
|---|---|
| Data Assimilation and Remote Sensing | Machine learning, data analytics, high-performance computing |
| Drought Modeling | Uncertainty analysis, probabilistic monitoring and forecasting |
| Extreme Events and Flood Modeling | Predictive science, meteorological and hydrological postprocessing |
| Climate Variability and Change | Bayesian multimodeling, data revolution |
| Hydrologic Modeling | Computational modeling for water resources system analysis |
Related Pages
- Publications — scholarly work from the group.
- Research Team — members of the research group.
- SMAP — downscaled soil moisture product at 1-km resolution using machine learning.
- USDM/IDI — multivariate-based integrated drought monitoring systems.