Counting snowflakes for better water resource management
Mostafa Zaky has built an award-winning model that helps estimate the amount of water stored in snowpacks, which could improve climate change and flood forecasting, as well as overall water resource management.
Water is one of the most important and powerful resources on Earth, and a lot of it resides in snowpacks. To help determine how much water is stored in snow, known as the Snow-Water Equivalent (SWE), PhD student Mostafa Zaky has built a new computational model that better estimates the electromagnetic backscattering from snowpacks. This information could improve climate change and flood forecasts, as well as overall water resource management.
Snowmelt provides freshwater to the western states, and the great polar ice caps are key regulators of ocean levels and currents. As glaciers melt, sea levels rise, threatening to flood cities and coasts. This influx of freshwater can affect ocean currents such as the Gulf Stream, which would significantly impact the climates of the northeastern U.S. and northwestern Europe.
Therefore, an accurate estimate of the SWE would significantly improve our estimations of the global impact of a warmer world where the snowpacks continue to shrink. However, it is difficult to discern a highly accurate SWE due to the inverse scattering problem – it’s difficult to measure snow because of how it scatters radiation and sensors.
To solve the inverse scattering problem and improve SWE estimation, Zaky has built a new computational model that provides an accurate physical model of the snowpack and a forward electromagnetic scattering solution.
To build this model, Zaky relied on two fundamental steps: the generation of computer samples for the snow medium and the EM scattering computation from the computer-generated samples of the snow through a numerical solver. He reconstructed the snow samples by considering the snow as a matrix of air and ice particles.
Zaky presented his paper, “Physics-Based Modeling and Electromagnetic Scattering Computation for Snow-Packs,” at the 2019 IEEE MTT-S International Conference on Numerical Electromagnetic and Multiphysics Modeling and Optimization where he won First Prize for Best Student Paper Award. He is advised by Kamal Sarabandi, the Rufus S. Teesdale Professor of Engineering and Director of the Radiation Laboratory.