Current Projects

Developing long-term high-resolution precipitation dataset using deep learning with multi-source Earth system data (PI; NOAA Climate Program Office)

Since accurate, high- resolution gridded precipitation datasets covering long periods of time are fundamentally important for climate monitoring and forecasting, many precipitation products have been developed over past decades. These gridded precipitation datasets include remote sensing-based estimates (satellites or radars), gauge-based analysis, and reanalysis datasets. However, they have shown great discrepancies with different strengths and weaknesses. The overarching goal of the intended project is to develop an improved long-term high-resolution precipitation dataset over the contiguous United States (CONUS) using deep learning with radar observations, gauge-based precipitation analysis data, climate reanalysis data, and satellite-based cloud data.

Estimation of evapotranspiration based on multi-source data fusion and deep learning (PI; AAES through USDA-NIFA Hatch project)

Using deep learning (DL) with synergies among different satellite sensors now makes it plausible to derive significantly improved spatiotemporal continuous ET estimates with higher accuracy and resolutions. This seed project aims to improve ET estimations using DL with HLS optical reflectance data, ECOSTRESS land surface temperature data, eddy covariance observations, and reanalysis data. The proposed work will develop and evaluating new approaches for improving ET estimations. The specific objectives of this proposal include: Develop and evaluate daily ET estimates using higher-resolution surface reflectance and meteorological data and eddy covariance observational network; and Construct a hybrid approach based on multi-source data fusion and deep learning to generate improved spatiotemporal continuous daily ET estimates.

CAS-Climate: CAREER: Analytical Methods for Understanding and Predicting Agricultural Flash droughts (PI; NSF CAREER)

Flash drought is a recently recognized extreme hydro-climate event characterized by its sudden onset, rapid intensification, and devastating impact on society. It is challenging to understand and predict because of its fast onset and development and complex land-ocean-atmosphere factors that contribute to or affect their onset and development. This project includes a series of interlinked activities to better understand causes and predictability of flash droughts, improve their forecasts and projections, objectively assess their climate change impacts, as well as promote education and outreach on these topics. The specific research will disentangle underlying drivers of agricultural flash droughts using machine learning-based causal inference analysis, develop and evaluate agricultural flash drought forecasts at the sub-seasonal timescale using deep learning approaches, and assess changes in agricultural flash drought under contemporary and future climate based on coupled general circulation models large ensembles. These research objectives will be integrated with innovative education plans. Link to the public abstract.

Addressing resiliency to climate related hazards and disasters through data informed decision making (Co-PI ; NSF Research Traineeship Program)

Climate-related natural hazards and disaster losses, coupled with the increasing frequency of billion-dollar events (such as hurricanes, severe storms, inland flooding, crop freezes, droughts and wildfires) are escalating in the United States. This project is aimed at training the next generation of scientists and leaders who can help build resilient communities that are prepared for, can effectively respond to climate risks. The NSF research trainees will learn quantitative and qualitative, analytical, and collaborative skills needed to lead the next generation of scientists and engineers that are able to recognize the data driven decision making needs of stakeholders, as well as to effectively communicate scientific information to stakeholder and public audiences. More information: http://www.auburn.edu/cosam/climate_resilience/

Building Resilience for Oysters, Blue Crabs, and Spotted Seatrout to Environmental Trends and Variability in the Gulf of Mexico (Co-PI; NOAA-RESTORE)

The abundance of oyster, blue crab, and spotted seatrout (OyBcSt) is rapidly declining in the Gulf of Mexico. The overarching research question of this project is: How can we build resilience for oysters, blue crab, and spotted seatrout populations to trends and variability in predominant biophysical stressors? Four major research questions will be addressed through this project: 1) How does OyBcSt resilience change as a function of ecosystem state? 2) What are the environmental thresholds that affect OyBcSt resilience? 3) What are the feedbacks between OyBcSt ecosystem services, economic systems, management actions, and environmental stressors? 4) If environmental trends are projected to the future, what will be the impact to OyBcSt populations and the implications for resource management? More information: https://restoreactscienceprogram.noaa.gov/projects/oysters-blue-crabs-seatrout