Hydrology has a long history due to its early origin, but it is still considered young due to lack of a solid scientific foundation as a natural science. To lay a solid foundation of hydrology, field experimentation is crucial for investigating hydrological processes and revealing hydrological mechanisms. Professor Wei-Zu Gu (1932–2022) was an internationally renowned scientist in the field of hydrology and is recognized as the greatest pioneer of experimental hydrology and isotope hydrology in China. He created the Hydrohill experimental catchment, which serves as both a great public works for experimental hydrology and a valuable legacy for future researchers to conduct hydrological experiments. This legacy represents an innovative infrastructure that bridges the gap between natural watershed experiments and artificial physical models. The Hydrohill is an intensively-instrumented experimental catchment, allowing for comprehensive measurement of elements of the hydrologic cycle and their tracing indicators in a sophisticated manner. To provide an in-depth understanding of the Hydrohill, this paper presents its short history, experimental objectives, site description (including location, construction, and instrumentation), site conditions (such as soil, hydrological and meteorological properties), and contributions to hydrologic science. We pay our respects to Professor Gu for his hard work in creating the Hydrohill for experimental hydrology and enhancing our understanding of hydrological processes and mechanisms. Finally, we hope that with healthy operation at Chuzhou Scientific Hydrology Laboratory (CSHL) along with support from Professor Gu’s friends, CSHL will enable the continued growth of the Hydrohill so that it can address some unsolved problems in hydrology.

Knowledge of soil properties is essential for risk assessment of vapor intrusion (VI). Data assimilation (DA) provides a valuable means to characterize contaminated sites by fusing the information contained in the measurement data (such as concentrations of volatile organic chemicals). Nevertheless, the application of DA in risk assessment of VI is quite limited. Moreover, soil heterogeneity is often overlooked in VI-related research. To fill these knowledge gaps, we apply a state-of-the-art DA method based on deep learning (DL), that is, ES(DL), to better characterize the contaminated sites in VI risk assessment. The effectiveness of ES(DL) is well demonstrated by three representative scenarios with increasing soil heterogeneity. The results clearly show that ignoring soil heterogeneity will significantly undermine one’s ability to make reasonable decisions in VI risk assessment. As a preliminary attempt of applying an advanced DA method in VI research, this work provides implications for the potential of using DL and DA in complex problems that couple hydrological and environmental processes.