Faculty Spotlight: Viviana Maggioni
Viviana Maggioni, PhD. is an Associate Professor of Environmental and Water Resources Engineering at George Mason University. Dr. Maggioni received her B.S. and M.S. degrees in Environmental Engineering from the Politecnico of Milan, Italy, in 2003 and 2006, respectively, and her Ph.D. degree in Environmental Engineering from the University of Connecticut, Storrs, in 2012.
Her research interests lie at the intersection of hydrology and remote sensing. In particular, she is interested in the application of remote sensing techniques to estimate and monitor hydrological variables. Her work has direct applications in water resources management, weather and climate prediction, as well as agriculture and irrigation practices.
She currently serves as Chair of the AGU (American Geophysical Union) Precipitation Committee, co-Chair of the International Precipitation Working Group, Editor of the Journal of Hydrometeorology and Associate Editor of the Journal of Hydrology and Frontiers in Climate-Climate Services. Since 2010, she has published 55 peer-reviewed scientific articles, 4 book chapters, 3 scientific reports, and co-edited a book on Extreme Hydroclimatic Events and Multivariate Hazards in a Changing Climate (Elsevier, 2019). The Institute for Digital InnovAtion talked with Maggioni about her research. Responses have been edited.
Tell us about your research.
My research seeks to enhance the effective use of satellite products for hydrologic/water cycle research and application needs from the regional to the global scale. My approach has developed along two main trajectories, with two distinct yet intertwined goals:
- To improve the inherent coarse resolution of satellite-based observations down to finer scales. The gain of this shift is both practical and conceptual: not only the wealth of information generated at the finer scale vastly benefits decision-making processes, but it also allows for the study of physical processes that remain invisible at coarser scales. My team has developed novel approaches to downscale atmospheric and hydrological variables using a set of physically-based techniques and machine learning methods.
- To improve the accuracy and efficiency of satellite-based observations by merging them with model simulations through data assimilation. Albeit in different ways, satellite-based observations and model simulations are both inherently inaccurate: the combination of the two, along with an increasingly precise quantification of the uncertainty they carry, generates a measurement that is more accurate than either. My research team has been exploring the potential of assimilating satellite-based phenology, freeze/thaw state, snow cover, and soil moisture observations in land surface models to improve our estimation and understanding of terrestrial carbon-water-energy cycle processes.
What kinds of projects are funded?
My total funding in the past seven years at Mason has been approximately $2 million (that has been my share out of over $3 million total).
Most of our projects are funded by NASA. We have been working on a few projects to study the spatial and temporal variability of key water variables, such as rainfall, snow, and streamflow, but also hydroclimatic hazards in High Mountain Asia. Changes in High Mountain Asia water resources affect the livelihood of more than a billion people and have an impact on the biodiversity of the unique ecosystems of the region. Hydroclimatic extremes (such as floods and droughts) have significant impact on the quality and availability of water resources and the sustainability of the natural environment, with important consequences on the economic growth in the region. Thanks to these NASA-funded projects, we have combined modeling techniques with satellite observations to assess high resolution meteorological and hydrological processes and better understand their past and future variability. My team has developed frameworks to estimate and validate uncertainty estimates associated with fine resolution satellite precipitation products. The estimation of these errors is challenging because of the complex interactions of the satellite retrieval uncertainty with the natural space-time variability of precipitation, especially at fine temporal and spatial resolution. Addressing this issue has important practical implications concerning the use of current and future satellite data in hydrology, climate and weather studies. Our work contributed to this field by:
- developing a novel radar classification scheme for collision-coalescence dominant precipitation,
- comprehensively evaluating state-of-the-art satellite precipitation products globally (both over land and over ocean), and
- assessing the performance of these products for flood forecasting, debris flow prediction, and water quality monitoring.
As part of another NASA project, we have also been studying terrestrial carbon-water-energy cycles at the global scale. Specifically, we have been evaluating the potential of merging remotely sensed observations of vegetation condition into a land surface model. Within this context, we are developing a novel multi-model ensemble assimilation technique by including several land surface models that have a dynamic vegetation component. This multi-model approach allows the incorporation of uncertainty in the physical processes, which remains to this day an unresolved issue in land data assimilation systems.
Another research direction that I have been exploring deals with monitoring the freeze/thaw (FT) state of the landscape using remote sensing techniques from satellites and UAVs. As water in various components of the landscape freezes, its movement is largely curtailed with impacts on climate, hydrology, ecology, and biogeochemical processes. Freezing and thawing processes are also closely related to the development and ablation of snowpack. I am currently collaborating on a project funded by the NASA Terrestrial Hydrology Program aiming to comprehensively analyze current FT products and their limitations. Starting from these analyses, we aim to develop a higher resolution fractional FT product that captures intermediate phases between frozen and thawed states, thus moving beyond current methods of representation that are strictly binary (frozen/thawed).
At the regional scale, I have partnered with other scientists at Mason and with the Northern Virginia Regional Commission (NVR) in a project that is part of the AGU’s Thriving Earth Exchange program. Heavy precipitation and its effect on stormwater infrastructure have been identified as dominant among the environmental and climatic stressors in Northern Virginia. Thus, the analysis of past and future precipitation patterns together with projected population growth and land use change has become a top priority towards sustainable regional planning. However, obtaining past and future precipitation estimates at spatial resolutions that are useful for regional decision making is still a challenge.
Thanks to an IDIA-funded project, we are currently exploring new approaches of dynamic monitoring and adaptation of agricultural management to changing resource levels to achieve socio-ecosystem sustainability. To this end, we proposed an advanced stochastic system for adaptive precision agriculture in a nonstationary environment that not only monitors but also forecasts agricultural resource conditions to guide optimal management as well as to make actionable recommendations. Our goal is to develop a decision guidance system (DGS) to empower agriculturalists to adapt to changing environment, climate, and practice, impacting irrigation, water efficiency, planting, harvesting, pruning, weed control, fertilizers, pesticides, herbicides, crop production and quality, and disease control. Our research will progress agricultural decision support from classic “almanac” methodologies to project crop conditions and resource management dynamically, using computational Earth-system modeling techniques and machine learning approaches.
How does your project impact other disciplines?
I see interdisciplinarity as an essential trait of any research endeavor in engineering today, and particularly in environmental engineering, where the contribution of other disciplines (geography, environmental policy, climate studies, computer science) is of vital importance to the overarching mission of the field. The wide scope of my team’s research attests precisely to this open horizon of thinking and researching, with environmental engineering as the framework and catalyst: our work spans from the local scale (designing green infrastructure on the Mason main campus) to the regional scale (studying precipitation patterns in Northern Virginia), to the global scale (combining water resources engineering with hydrometeorology and remote sensing to evaluate conditions in regions where ground truthing is impossible, but where environmental and health consequences of mis-measurements can be devastating).
What projects would you like to be reached out to for potential collaborations?
The NASA Terrestrial Hydrology Program for multi-model data assimilation methods and the IDIA precision agriculture for novel AI/ML approaches.
What keywords describe your research?
Hydrology, remote sensing, land surface modeling, data assimilation, uncertainty analysis
How can you be reached?