In the intensively irrigated Haihe River Basin (HRB) of China, a region grappling with groundwater depletion and environmental challenges, a groundbreaking study has shed light on the critical role of accurate evapotranspiration (ET) data in understanding water-energy-carbon cycles. Led by Yuxi Li from the Institute of Surface-Earth System Science at Tianjin University, the research, published in the Journal of Hydrology: Regional Studies, proposes a novel approach to correct biases in ET data, offering significant implications for the energy sector and sustainable water management.
The Haihe River Basin, one of China’s most heavily irrigated agricultural regions, has long been a hotspot for groundwater overexploitation. Accurate ET data is vital for effective water management, but current remotely sensed (RS) and reanalysis ET datasets often contain systematic biases, particularly in irrigated regions. “The datasets cannot capture the irrigation-induced ET peaks for regions with a seasonal rotation crop planting system,” Li explained. This oversight can lead to substantial errors in water balance assessments and, consequently, misinformed decision-making in water and energy management.
The study reveals that current datasets, including ERA5, PML, and ALEXI, exhibit strong intra and inter-annual biases. These biases can result in a misrepresentation of the true ET rates, leading to inefficient water use and exacerbating groundwater depletion. “The water balance corrected ET can substantially reduce the absolute bias, but it cannot reduce ET climatological error,” Li noted. This finding underscores the need for more sophisticated methods to correct ET data.
The researchers propose a new optimal merging strategy that incorporates irrigation data into the best climatology. This approach raises the ET merging correlation by 4.02%, and reduces error and absolute bias by 22.17% and 55.21%, respectively. The resulting 0.1° 8-day ET dataset provides a more accurate representation of ET in the HRB, demonstrating that irrigation can lead to a 29.98% increase in ET. This increase, while beneficial for agriculture, also highlights the urgent need for sustainable water management practices to prevent environmental disasters such as land surface deformation.
The implications of this research extend beyond the HRB, offering valuable insights for other intensively irrigated regions worldwide. For the energy sector, accurate ET data is crucial for optimizing water use in energy production, particularly in thermoelectric power plants and bioenergy facilities. By providing a more precise understanding of water consumption and availability, this research can help energy companies make informed decisions, reduce operational risks, and enhance sustainability.
Moreover, the study’s findings can guide policymakers in developing effective water management strategies, balancing the needs of agriculture, industry, and the environment. As Yuxi Li and colleagues continue to refine their methods, the energy sector and water management professionals can look forward to more accurate and reliable ET data, paving the way for a more sustainable future.
In the realm of hydrology and water resource management, this research marks a significant step forward, demonstrating the power of innovative data merging strategies to address complex environmental challenges. As the world grapples with the impacts of climate change and increasing water demand, the insights gained from this study will be invaluable in shaping future developments in the field.