Surface deformation along the Nile Delta, Egypt: From monitoring towards prediction

The Nile Delta represents the most critical part of Egypt, hosting more than 50% of the population and approximately two-thirds of the nations agricultural lands. During the last decades, the Nile Delta has suffered from significant surface deformation that has led to damage to transport networks and infrastructures, thus becoming a significant risk in the area. This deformation is mainly due to environmental changes and anthropogenic activities such as the over-extraction of groundwater for different purposes and the building of dams inside and outside Egypt along the river Nile. These activities have led to shortage of water inflow, changing the discharge rates, reduced sedimentation in the delta and changes in the water recharge rates of the Nile, the primary source of water for the Nile Delta aquifers. Regarding the environmental changes, the shifts in climate patterns, including variations in precipitation, rising temperatures, and rising sea levels, have all altered the hydrological balance of the Nile Delta, consequently leading to variations in surface deformation patterns. Many studies have studied the rate and patterns of land deformations in the Nile Delta based on geodetic tools such as Global Positioning System (GPS) and Interferometric Synthetic Aperture Radar (InSAR). But still, there is a gap in understanding the relationship between surface deformation rates and the causative factors. Such understanding could potentially enable the estimation of system response for future scenarios. In this research, we present the results of a system that uses Sentinel-1 SAR data characterized by VV polarization, with ascending and descending orbital directions, acquired between 2015 and 2023 along the Nile Delta. We utilized open-source LiCSBAS tools to analyze the surface deformation rates from InSAR Sentinel-1 data. Then, we calculated the vertical deformation velocity over time by decomposing the ascending and descending LOS data. Then, we analyzed the surface deformation results obtained with the present methodology against the freely available geospatial data, which represents the possible causative factors (such as rainfall, water body change, total terrestrial water storage, land use-landcover, temperature, etc.), to understand their relations and their impact. By linking the surface deformation to its causative factors through machine learning techniques such as Random Forest, our research aims to provide a better understanding of the system dynamics and an appropriate model for prediction. This model can be utilized by decision-makers to consider and manage risks associated with severe surface deformation for possible future scenarios. The results should enable the design of future mitigation actions to protect Egyptian society and make it more resilient to the consequences of land deformation over the Nile Delta.