The Environmental Impact of Dams in Saudi Arabia

Abstract

Understanding vegetation cover dynamics and their relationship with environmental variables is critical for maintaining ecosystem functionality, particularly in arid regions like Saudi Arabia. Dams play a vital role in water resource management, capturing runoff for domestic and agricultural use. However, their construction and operation can lead to significant environmental impacts, both upstream and downstream. Downstream regions are most affected by these impacts, spanning biological, hydrological, chemical, and geophysical domains. Although dams provide water, recharge groundwater, and increase agricultural productivity, they can also aggravate environmental degradation, particularly in arid or semi-arid areas, where water availability changes, natural flow regimes are reduced, and soil salinization is increased. Recent scientific interest has focused on the environmental consequences of dams, yet existing studies often rely on ground-based measurements without integrating advanced technologies like remote sensing and land hydrology models. This limitation hinders a comprehensive assessment of dam impacts on downstream vegetation and associated environmental factors. Key variables such as Land Surface Temperature (LST), soil moisture, soil salinity, precipitation, temperature, stream runoff, and groundwater levels remain underexplored in their relationship to vegetation dynamics. While some studies have employed basic statistical methods like linear regression and principal component analysis (PCA) to explore the dam’s impacts on such variables, there is a pressing need for more advanced analytical approaches to better evaluate these complex relationships. Furthermore, research on ephemeral streams in arid regions is scarce, despite their critical role in understanding downstream environmental impacts. This study fills these gaps by combining remote sensing technology with outputs of land hydrology models to provide an integrated assessment of downstream environmental effects of dam development in arid countries, with Saudi Arabia as the main case study. Specifically, the study investigates the effects of various environmental factors on downstream vegetation, with a focus on ephemeral streams. Advanced methodologies—including satellite remote sensing, reanalyzed hydrological data, and machine learning techniques—are used to monitor changes before and after dam construction. Analytical software includes supervised vegetation classification, moderated mediation analysis for direct and indirect effects, PCA and Two-Step Clustering for investigating environmental groupings, Dynamic Time Warping (DTW) for identifying spatiotemporal changes, and Vector Autoregression (VAR) modeling for quantifying time-lagged vegetation response to hydroclimatic variables. A space-for-time substitution approach is also applied to generalize findings across different spatial and temporal contexts. By integrating these advanced techniques, this study establishes a comprehensive framework for assessing dam-induced environmental impacts on downstream ecosystems in arid regions. These results provide important guidance for decision-makers and those overseeing environmental management, supporting sustainable development and effective water resource management in dam-affected regions.