| NSF Org: |
RISE Integrative and Collaborative Education and Research (ICER) |
| Recipient: |
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| Initial Amendment Date: | July 2, 2019 |
| Latest Amendment Date: | June 30, 2020 |
| Award Number: | 1854902 |
| Award Instrument: | Continuing Grant |
| Program Manager: |
Justin Lawrence
jlawrenc@nsf.gov (703)292-2425 RISE Integrative and Collaborative Education and Research (ICER) GEO Directorate for Geosciences |
| Start Date: | July 1, 2019 |
| End Date: | June 30, 2024 (Estimated) |
| Total Intended Award Amount: | $620,183.00 |
| Total Awarded Amount to Date: | $620,183.00 |
| Funds Obligated to Date: |
FY 2020 = $148,254.00 |
| History of Investigator: |
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| Recipient Sponsored Research Office: |
3400 N CHARLES ST BALTIMORE MD US 21218-2608 (443)997-1898 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
1101 E 33rd St Baltimore MD US 21218-2686 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | PREEVENTS - Prediction of and |
| Primary Program Source: |
01001920DB NSF RESEARCH & RELATED ACTIVIT |
| Program Reference Code(s): | |
| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.050 |
ABSTRACT
Drought is often thought of as a creeping disaster; one that emerges slowly over time. In contrast, "flash droughts" intensify dramatically in just a few weeks. A number of these events have struck the United States in recent years, leading to significant and unexpected damage to agriculture and the economy. Flash droughts are poorly represented in current forecast systems, hindering drought preparedness. This project is motivated by the need to advance understanding of flash droughts in order to improve our ability to predict them. To do this, we will focus on the critical role that plants play in the development of a flash drought. New satellite technologies and field measurement methods make it possible to detect water stress in plants weeks before that stress can be seen by eye. When plant stress increases rapidly there is a high risk of flash drought. Using this understanding, we will produce flash drought definitions and detection systems that cover the entire contiguous United States. We will then categorize flash droughts according to the ways in which weather and vegetation interact to cause the drought. These interactions can be very different for different regions or land uses, so identifying categories is an important step for improving prediction. Using these categories, we will apply recently developed statistical methods to combine plant stress observations with weather forecasts to predict flash drought risk from two weeks to three months in advance. Predictions at these time scales can inform planting decisions and relief efforts. Finally, highly damaging flash droughts will be selected for detailed study using advanced weather models, in order to understand how land management and climate contribute to particularly severe events.
This project will advance flash drought understanding and forecasting by targeting three known characteristics: (1) observations of vegetation and soil moisture can provide early indications of flash drought risk at significant lead times; (2) evaporative demand is a leading driver of flash drought onset, and it is amenable to skillful subseasonal-to-seasonal (S2S) forecasts; (3) vegetation plays a central role in flash drought development via soil moisture and turbulent heat fluxes. To leverage these features for prediction, we propose a new framework for defining flash droughts based on the understanding that a rapid increase in vegetation stress is the core defining flash drought characteristic. This framework makes use of advanced satellite and ground observations. We will classify historic flash drought events across the Contiguous United States on the basis of meteorological, hydrological, and ecological factors, allowing us to distinguish different types of event that have distinct processes and predictability characteristics. This classification will support probabilistic statistical and machine learning forecast models that combine information from recently developed observation datasets and global S2S forecasting systems. Analysis of drought classes and predictability will, in turn, be used to select cases for detailed dynamically-based simulation studies that isolate the role of vegetation and its contribution to predictability. Finally, the simulation infrastructure established during the project will be used to examine climate and land cover sensitivities of flash droughts, contributing to projections of future flash drought risk and assessment of land management options. Taken together, these activities will bring new tools to flash drought prediction, contribute to dynamically-based simulation of drought, and place both understanding and prediction of these extreme events into the broader context of climate trends and the terrestrial carbon balance.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
Rapid onset “flash droughts” have tremendous agricultural and ecological impacts in the United States and many other countries. These droughts are distinct in how quickly they develop: conditions can go from normal to severe drought in just a few weeks. They have also proven to be difficult to predict, and there is evidence that they are increasing as global temperatures rise. The goal of this project was to advance predictive understanding of flash droughts. To do this, we first had to develop an objective definition to identify and catalog flash drought events. We then tested the hypothesis that there are several different types of flash droughts, with different background weather conditions, by classifying all droughts in our catalog. In parallel, we developed statistical models to improve flash drought prediction and characterize the processes that drive them. These results informed physically-based simulations of seminal flash drought events. The simulations were designed to quantify the role that vegetation plays in flash drought onset and development. Finally, we undertook studies of the influence that droughts have on photosynthesis, in order to understand how trends in flash droughts might contribute to the carbon balance of affected areas.
Scientifically, we were able to test all of the key hypotheses that underlie the project. Analysis of our flash drought catalog and classification led us to conclude that there are three distinct classes of flash drought in the United States. One of these classes is associated with heatwave conditions, which is consistent with studies that have explored climate change impacts on flash droughts. Another class is defined by precursor conditions that are already on the cusp of drought. In these cases, vegetation might not have shown stress prior to the onset of the flash drough, but soil moisture was primed to fall into drought conditions, suggesting that close monitoring of soil moisture conditions could contribute to forecasting this type of event. The third class emerges rapidly from a combination of weather and soil conditions and has no obvious predictable precursor. This helps to explain the predictability challenge for at least some types of flash drought.
We have also been able to test our hypothesis that vegetation plays a role in flash drought onset. In studying this process, we focused on the first class of flash drought—those associated with high air temperature—because those were the events most strongly associated with high evaporation rates. In a modeling study of a major flash drought that struck Texas in 2011, our simulations that included vegetation feedbacks led to higher temperature and lower soil moisture compared to those that excluded feedbacks. This suggests that accounting for vegetation feedbacks is important for improved prediction of flash droughts in dynamical forecast systems.
Our investigation of photosynthesis and the gross primary productivity of vegetation under drought was designed to test the hypothesis that some droughts might actually increase ecological productivity, since warm, sunny conditions can boost plant growth even if water stress ultimately harms vegetation health. We found that in “energy-limited” ecosystems—that is, places where plant growth is primarily limited by availability of warmth and sunlight, rather than water—springtime drought can lead to increased photosynthesis, while the opposite was true in drier, water-limited ecosystems. This finding was based on our analysis of observations, and it reveals a pattern that is not successfully replicated by current biosphere models. This discrepancy suggests that the models are missing some processes or sensitivities that are relevant to predicting carbon fluxes under global change.
Finally, in the area of applied prediction, we successfully combined dynamical forecast model output with nonlinear machine learning methods to improve forecasts of the rapid changes in soil moisture and vegetation health. This indicates that operational flash drought forecasts can make use of both dynamical models and satellite-based observations to generate improved forecasts that better inform drought preparedness.
Last Modified: 08/08/2024
Modified by: Benjamin F Zaitchik
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