Professor Leila Farhadi – Remote Sensing & Computer Modelling: Understanding the Dynamic Water Cycle

Dec 4, 2019 | Earth and Environment

The Earth’s water cycle is an incredibly complex system, and is closely coupled to the planet’s energy and carbon cycles. One of the biggest challenges for hydrologists is to accurately model the components of this system and begin to understand how human-induced changes to the climate and landscape will affect it. Combining computer modelling with observational data, Professor Leila Farhadi and her team at the George Washington University created a novel approach to mapping two critical components of the water cycle: evapotranspiration from the landscape and recharge to aquifers. Their work has implications for predicting and responding to water shortages, towards ensuring global water and food security.

A Dynamic Planet

The Earth is a dynamic planet. The relationships between the atmosphere, land, ice and oceans are complex and constantly varying through a series of cycles and feedback loops. For example, a small change in the atmosphere will impact other environmental variables, leading to further changes in the atmosphere. Earth system science is the study of how these different environmental variables behave as a whole, linked by nutrient, carbon, water and energy cycles that are themselves extremely complex.

Professor Leila Farhadi, a hydrologist at the George Washington University, is particularly interested in the land component of the water cycle, known as the terrestrial water cycle. The terrestrial water cycle describes the continuous movement of water on, above and below the terrestrial landscape.

Water deposits on land in the form of rain and snow through the process of condensation (changing from vapour to liquid), flows on land as runoff, infiltrates into the soil and recharges to groundwater. It then moves back into the atmosphere through the process of evaporation (turning from liquid to vapour through the Sun’s heat) and transpiration (moving from the soil to plant leaves to the atmosphere), or becomes stored in soil, lakes, streams, groundwater, and polar and glacial ice. This gigantic system, powered by energy from the Sun, comprises a continuous exchange of moisture between the land and the atmosphere.

The combination of evaporation and transpiration is known as evapotranspiration, and this is how water moves from the surface of the land to the atmosphere. Recharge flux is the primary process through which water enters subsurface aquifers and occurs below plant roots. Evapotranspiration flux, which links the surface and atmospheric system, and recharge flux, which links the surface and subsurface systems, are two key components of the terrestrial water cycle that are not well understood and are poorly monitored.

Evapotranspiration and recharge also play major roles in the energy and nutrient cycles on Earth. They are vital for the health of ecosystems – not just for the natural environment but also for human crop production. They are important for the sustainability of water sources, such as aquifers, and are strongly affected by climate. The effects of even small changes in climate are amplified due to subsequent changes in evapotranspiration and recharge rates. Professor Farhadi and her team aim to characterise and map the patterns and complex dynamics of these two processes, enabling us to better predict and prepare for water shortages, towards ensuring food security in the face of climate change.

The Challenge of Measuring Fluxes

Accurately estimating evapotranspiration and recharge is a challenge for hydrologists, as they change through time and under varying climate conditions. Complexities and interrelation of land surface processes, along with many unknown parameters of land surface models, make it very difficult to realistically map these fluxes using physical computer models. Direct measurements of these fluxes are difficult, costly and labour-intensive. Moreover, measurement of fluxes at a single point cannot be used to estimate or map these processes over a larger area.

A potential answer to this problem involves remote sensing technology, in which satellites collect scientific data from orbit over wide areas of the Earth throughout the year. Professor Farhadi has recently secured a NASA New Investigator Program grant to use observational satellite data combined with computer modelling to map evapotranspiration and recharge processes.

How to Model A Complex System

Land surface fluxes, such as evapotranspiration and recharge, cannot be measured directly through remote sensing, as they do not have a specific signature that can be detected directly by current remote sensing technologies. Instead, satellites gather observational surface data.

Soil moisture and land surface temperature data are a key focus for Professor Farhadi, as they contain hidden information about evapotranspiration and recharge flux. By assimilating such data into a computer model, her team can begin to estimate these fluxes. ‘We are developing robust, efficient and reliable data assimilation techniques that enable us to map regional fluxes of evapotranspiration and recharge,’ explains Professor Farhadi.

The team is developing a variational data assimilation (VDA) model to map evapotranspiration and recharge flux. Combining the outputs of physical computer models with environmental observations, VDAs are sophisticated mathematical techniques that produce states and parameters that most accurately approximate the current and future states of a true system.

In a study published this year, the researchers tested their method of estimating evapotranspiration flux. The team found that their technique is more accurate than previous attempts at mapping the evapotranspiration flux from observational surface data.

The team’s novel model uses soil moisture measurements, as well as land surface temperature data. It then integrates these environmental observations into a system of water and energy balance model that are coupled through the flux of evapotranspiration. The model uses moisture and heat diffusion equations to define the transfer of water and heat between land and atmosphere. It then uses uncertainty analysis techniques to evaluate the accuracy of estimation.

The Geostationary Operational Environmental Satellite. CREDIT: NASA

The First Test

Professor Farhadi and her team initially tested the performance of their technique using a synthetic dataset. Using a combination of data from the simultaneous heat and water (SHAW) model and meteorological data (such as precipitation and solar radiation), they hoped to test the performance of their VDA model to indicate its feasibility for larger-scale studies using satellite data.

The team completed a set of tests on their VDA model to simulate different data scenarios. For example, they used the model to calculate land surface fluxes when there was a limited amount of soil moisture and land surface temperature data available to represent time periods when cloudy conditions reduced the number of measurements that a satellite could make.

The study successfully showed that the inclusion of soil moisture data along with land surface measurements improved the overall estimation and mapping of evapotranspiration rates. The team discovered that by using the moisture diffusion equation within the model, the parameters of the model were better constrained than in previous studies. The study was vital in proving that the team’s proposed method could be applied to larger scale studies, and that space-based satellite data could be integrated into the model to create accurate estimates of land surface fluxes.

Integrating Satellite Data

In their most recent work, Professor Farhadi and her team used their VDA model with satellite data from a variety of sources. The study focused on a specific 31,500 square kilometre area of the Southern Great Plains, with data covering a nine-month period. Not only is this a large, regional-scale area of land, analysing data over a large time period confirms how well the mapping technique performs under changing seasonal conditions.

The team used the Geostationary Operational Environmental Satellite (GOES) operated by the National Oceanic and Atmospheric Administration (NOAA) as a source of land surface temperature data. This high-resolution dataset comprises hourly calculations of land surface temperature at a spatial resolution of five kilometres. The researchers obtained soil moisture data from the Soil Moisture Active Passive (SMAP) mission operated by National Aeronautics and Space Administration (NASA), which makes measurements every two to three days, with a spatial resolution of nine kilometres. The team then integrated these data into their VDA model.

When used with real remote sensing data, the coupled VDA modelling technique was a success. ‘The developed computational tools have been successful in mapping regional fluxes of evapotranspiration in US southern great plains,’ describes Professor Farhadi. Her team was able to evaluate its performance through a series of tests, which showed that the model performed consistently. They also compared the results from the model with real surface heat and evaporative flux measurements that were recorded in the study area. These measurements were made by the Atmospheric Radiation Measurement network and when compared to the model results, were an indication of how accurate the estimated fluxes were.

The research team also completed an uncertainty analysis to show that integrating the remotely sensed land surface temperature and soil moisture data helped to reduce uncertainties within the model parameters. Overall, they were able to prove that assimilating remotely sensed soil moisture and land surface temperature data into a coupled model of water and energy balance improved the accuracy of land surface flux estimations.

Earth System Future

The evaporation and transpiration maps that Professor Farhadi has produced have already been used to study a variety of other relationships. For example, the model has been used to look at how evapotranspiration is dependent on several factors, such as vegetation and solar radiation. It has also been used to look at microclimate and biogeographical controls on water limitation within the study area.

So far, Professor Farhadi has been focused on accurately estimating and mapping evapotranspiration using the coupled water-energy balance model. Her team’s future research will also include studying the recharge flux using similar techniques. Looking further ahead, future models could couple the water and energy cycles with the carbon cycle, or include additional types of remote sensing data to improve understanding and accuracy of land surface fluxes.

Evapotranspiration and recharge maps have a wide range of applications for scientists, especially hydrologists working to understand this complex and dynamic system. Water availability and food security depend on accurate estimations of these fluxes. The recharge rate is exactly the rate of the sustainable use of subsurface aquifers. Evapotranspiration is the link between the water, energy and carbon cycles and therefore ecosystem productivity; biomass accumulation and net carbon exchange is dependent on this flux.

Moreover, variations in the land branch of these fundamental cycles would be essentially independent if it were not for the evapotranspiration flux that links the three. In order to understand how the Earth system works and how it will evolve in a changing climate, the scientific community requires accurate estimation of evapotranspiration flux, and Professor Farhadi’s methods are bringing us significantly closer to achieving this goal.

Reference
https://doi.org/10.33548/SCIENTIA450

Meet the researcher


Professor Leila Farhadi
Department of Civil and Environmental Engineering
The George Washington University
Washington DC
USA

Professor Leila Farhadi is a hydrologist at the George Washington University in the US. After completing her PhD at the Massachusetts Institute of Technology, she spent some time working at the NASA Goddard Space Flight Centre as a research scientist in the Global Modelling and Assimilation Office (GMAO). Her particular research interest lies in the area of hydrology and land-atmosphere interaction. She is interested in understanding and modelling land surface and land-atmosphere interaction and exchange processes by utilising innovative remote sensing, optimisation and numerical modelling techniques. In 2018, she was awarded a prestigious NASA Early Career (New Investigator) Award in Earth Sciences, and is the Primary Investigator on multiple other research grants. Professor Farhadi has also supervised Masters and PhD students who themselves have moved forward with exciting research careers. She continues to teach alongside her research, and received the SEAS Outstanding Junior Teaching Award in 2018.

CONTACT

E: lfarhadi@gwu.edu

W: https://www.cee.seas.gwu.edu/leila-farhadi

FUNDING

NASA, USGS

KEY COLLABORATORS

Rolf H. Reichle, Research Physical Scientist, Global Modeling and Assimilation Office, NASA Goddard

Dara Entekhabi, Professor, Massachusetts Institute of Technology

Sayed M. Bateni, Associate Professor, University of Hawai’i at Manoa

Umar M. Altaf, Research Scientist, King Abdullah University of Science and Technology

FURTHER READING

A Abdolghafoorian & L Farhadi, Estimation of Surface Turbulent Fluxes from Land Surface

Moisture and Temperature via a Variational Data Assimilation Framework, Water Resources Research, 2019, 55, 4648.

A Abdolghafoorian, L Farhadi, SM Bateni, SA Margulis & T Xu, Characterizing the Effect of

Vegetation Dynamics on the Bulk Heat Transfer Coefficient to Improve Variational Estimation of Surface Turbulent Fluxes, Journal of Hydrometeorology, 2017, 18, 321.

A Abdolghafoorian & L Farhadi, Uncertainty Quantification in Land Surface Hydrologic Modeling:

Toward an Integrated Variational Data Assimilation Framework, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016, 69, 2628.

L Farhadi, D Entekhabi and G Salvucci, Mapping Land Water and Energy Balance Relations through Conditional Sampling of Remote Sensing Estimates of Atmospheric Forcing and Surface States, Water Resources Research, 2016, 52, 2732.

 

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