In the heart of Beijing, researchers are pushing the boundaries of what’s possible in flood monitoring and water resource management. Hao-si Li, a scientist at the Key Laboratory of Computational Geodynamics, College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, is leading a groundbreaking study that could revolutionize how we track and predict floods, with significant implications for the energy sector.
Li and his team are harnessing the power of the Gravity Recovery and Climate Experiment Follow-On (GRACE-FO) mission, a pair of satellites orbiting Earth, to measure changes in water storage with unprecedented temporal resolution. Traditionally, such data has been available monthly, but Li’s innovative approach refines this to a mere few days. This leap in temporal resolution could be a game-changer for industries reliant on accurate water management, including energy production.
The energy sector, particularly hydropower and thermal power plants, is heavily dependent on water resources. Accurate and timely flood monitoring can help these industries optimize their operations, prevent damage, and ensure a stable power supply. “The energy sector stands to gain significantly from this research,” Li explains. “By providing high-temporal-resolution data, we can help energy companies better prepare for and respond to extreme climate events, ultimately leading to more efficient and resilient operations.”
The study, published in Water Resources Research (translated from English), focuses on two floods that occurred in July 2021—in Western Europe and Central China. Using an improved algorithm that accounts for peripheral signal sources and temporal correlations in mass variation, Li and his team were able to track the temporal progression of these floods with remarkable detail. They found that the water gain in Central China was much lower than expected, indicating a mass deficit during the flood. This discrepancy, they suggest, could be due to human activities manipulating water resources, highlighting the complex interplay between natural processes and human interventions.
This research opens up exciting possibilities for the future. As Li puts it, “Our approach refines the viability of applying GRACE-FO data to restore transient dynamics characterizing extreme climate events.” This could lead to more accurate flood predictions, better water resource management, and enhanced preparedness for extreme climate events. For the energy sector, this means improved risk management, optimized operations, and ultimately, a more sustainable and resilient energy infrastructure.
Moreover, this study underscores the non-negligible influence of human interventions on short-term hydrological dynamics. As we continue to grapple with climate change and its impacts, understanding and accounting for these human-induced changes will be crucial. This research is a significant step in that direction, paving the way for more integrated and holistic approaches to water resource management and flood monitoring.