A Numerical Method for InSAR-Based Estimation of Head Changes using Storativity Parameters

Significant groundwater (GW) head decline due to excessive withdrawal is an essential hydrological concern in several major plains of Iran. The capacity of an aquifer to retain GW can be described through the storativity parameters. Traditional methods to define these parameters are costly, time-consuming, and sometimes ineffective.

The storativity of an aquifer, irrespective of its confinement type, is defined as the ratio of land surface deformation caused by GW withdrawal to the corresponding changes in GW head during a specified period. Interferometric Synthetic Aperture Radar (InSAR) is an effective tool to measure the gradual land surface deformation through backscattered radar signals. Additionally, the GW head changes can be monitored using available piezometric wells within the area. Depending on the hydrogeological properties of the aquifer, the GW head changes can lag the deformation by a few days to several years.

Previous studies aimed at deriving the aquifer’s storativity parameters by focusing on extracting the storativity coefficient of the confined aquifer based on analyzing the seasonal components of both deformation and GW head signals. In this study, three parameters have been considered as representative indicators of the storativity for each target aquifer, independent of its type and complexity arising from multi-layered structures. These parameters encompass the lag time between the GW head change and induced land surface deformation, which is calculated through cross-correlation analysis. The other two parameters, seasonal and long-term skeletal storage coefficients, are estimated through a joint analysis of the head signal and the deformation signal shifted by the lag-time value. By estimating these parameters at each piezometric well location, a simulation of the GW head signal is feasible using InSAR data. The final year of both signals is isolated to evaluate the method's efficiency for predicting head changes.

Our method was implemented on random observation wells across three areas encompassing different aquifer types and geological settings in order to evaluate its performance. The model demonstrated satisfactory performance in simulating and predicting the GW head, as evidenced by the average R-squared values of 0.77 and 0.54, respectively.