A workflow to study land subsidence based on InSAR analysis and hydro-mechanical modeling: A case study of Hanoi, Vietnam

In recent years, land subsidence has been intensively studied due to its severe impacts on urban communities and the environment. Amongst others, groundwater withdrawal is suspected to be the main trigger for such land subsidence. A prominent example is the Red River Delta in Northeastern Vietnam, where Hanoi is located. Radar remote sensing for mapping ground movement has been successfully applied for Hanoi City to quantify the land subsidence. Specifically, SAR data at the X, C, and L bands have been used, mainly based on the small baseline subset (SBAS) and Persistent Scatterer InSAR (PSInSAR) methods for extracting deformation in the urban setting of Hanoi from 1995 to the present. In these previous studies, line-of-sight land deformation was converted into the vertical direction with the assumption that horizontal movement is insignificant. However, a detailed analysis of the hydro-mechanical processes triggering the subsidence would strongly benefit from more complete InSAR deformation data, accounting also for horizontal movement. Therefore, the study applies PSInSAR to process both ascending and descending Sentinel-1 data acquired from 2017 to the end of 2019 to extract both vertical and horizontal (along the east-west direction) deformation in the study area. Our results show that in some areas, total displacement adds up to 32 mm/y in the vertical (subsidence) and 17 mm/y in the horizontal direction, indicating that horizontal movements are not negligible when it comes to interpreting deformation and relating it to hydro-mechanical processes in a heterogeneous subsurface. An interdisciplinary workflow is introduced that illustrates how remotely sensed subsidence data can be interpreted with the help of a geological subsurface model and coupled hydro-mechanical simulations conducted with numerical multi-physics software. We present the data basis and model set-up for the planned modeling study with the open-source software platform OpenGeoSys.