Our research group studies the relationships between water, plants, and people in a changing climate. Ongoing questions include:
How will changes in soil moisture affect future yields?
Changes in root-zone soil moisture will have pronounced influences on the future of agriculture. We aim to reduce the adverse effects of climate change by answering the following questions: What is the role of root-zone soil moisture in controlling past yield variability? How will anthropogenic climate change alter root-zone soil moisture, and what are the implications for crops? How do different climate variables, such as temperature, vapor pressure deficit, and sunlight, interact and compound with water stress to control yield variability?
Using remotely sensed soil moisture, we have made significant progress answering these questions regionally for different crops: maize in the U.S. Midwest, tea in Kenya, and rice in Madagascar.
How should we isolate crop signals from satellite data?
Satellite remote sensing provides a tool to resolve historical relationships between weather and yield at global scales. However, the agricultural signal needs to be extracted from the remotely sensed observations, i.e. we need to know where and when crops are grown.
We are currently working to map critical growing season periods using a range of satellite observations and agricultural statistics across the globe, paying close attention to regions that are historically understudied.
How will climate change affect transpiration?
Transpiration parameterizations fail over large spatial scales, as exemplified by the vastly different transpiration rates estimated by state-of-the-art global climate models. This situation is largely born of insufficient measurements of photosynthesis, transpiration, and root-zone soil moisture at scales relevant to estimate plant stress in land surface models. New remote sensing observations of surface temperature, water availability (soil moisture), and productivity (vegetation optical depth, solar induced fluorescence) have the potential to greatly improve our ability to estimate vegetation stress, especially when coupled with on the ground eddy covariance flux measurements and data-driven methods
We are currently working to understand how water stress will shift with climate change at diurnal, seasonal, and annual time scales using new data sources and innovative statistical techniques.