Mapping the Distribution of Shallow Groundwater Occurrences Using Remote Sensing-Based Statistical Modeling over Southwest Saudi Arabia

Abstract

Identifying shallow (near-surface) groundwater in arid and hyper-arid areas has significant societal benefits, yet it is a costly operation when traditional methods (geophysics and drilling) are applied over large domains. In this study, we developed and successfully applied methodologies that rely heavily on readily available temporal, visible, and near-infrared radar and thermal remote sensing data sets and field data, as well as statistical approaches to map the distribution of shallow (1–5 m deep) groundwater occurrences in Al Qunfudah Province, Saudi Arabia, and to identify the factors controlling their development. A four-fold approach was adopted: (1) constructing a digital database to host relevant geologic, hydrogeologic, topographic, land use, climatic, and remote sensing data sets, (2) identifying the distribution of areas characterized by shallow groundwater levels, (3) developing conceptual and statistical models to map the distribution of shallow groundwater occurrences, and (4) constructing an artificial neural network (ANN) and multivariate regression (MR) models to map the distribution of shallow groundwater, test the models over areas of known depth to groundwater (area of Al Qunfudah city and surroundings: 294 km2), and apply the better of the two models to map the shallow groundwater occurrences across the entire Al Qunfudah Province (area: 4680 km2). Findings include: (1) high performance for the ANN (92%) and MR (88%) models in predicting the distribution of shallow groundwater using temporal-derived remote sensing products (e.g., normalized difference vegetation index (NDVI), radar backscatter coefficient, precipitation, and brightness temperature) and field data (depth to water table), (2) areas witnessing shallow groundwater levels show high NDVI (mean and standard deviation (STD)), radar backscatter coefficient values (mean and STD), and low brightness temperature (mean and STD) compared to their surroundings, (3) correlations of temporal groundwater levels and satellite-based precipitation suggest that the observed (2017–2019) rise in groundwater levels is related to an increase in precipitation in these years compared to the previous three years (2014–2016), and (4) the adopted methodologies are reliable, cost-effective, and could potentially be applied to identify shallow groundwater along the Red Sea Hills and in similar settings worldwide.

Use of Geophysical and Radar Interferometric Techniques to Monitor Land Deformation Associated with the Jazan Salt Diapir, Jazan city, Saudi Arabia

“…The alluvial aquifer is the primary source of groundwater in the coastal plain where the watersheds draining the Red Sea Hills collect precipitation and channel the accumulated runoff toward the Red Sea coastal plain aquifers as surface runoff and/or groundwater flow. The precipitation over the coastal plain and the surface runoff infiltrate and recharge the shallow alluvial aquifer, as does the groundwater flow from the fractured basement aquifer (Sultan et al 2019;Alshehri et al 2020).…”

Adoption of Sustainable Water Management Practices among Farmers in Saudi Arabia

“…Of all the water used for agricultural purposes, 90% is supplied by groundwater aquifers [42]. Different key challenges are facing Saudi Arabia's water sector, including balancing food security and water security [43]; the increasing demand for water from the agricultural sector [44]; low irrigation efficiency [45]; population growth and the high consumption of water [46]; the scarcity of reliable data about ground water resources [47]; climate change [39]; water losses through leakage [43,48]; and the environmental consequences of desalination plants [49].…”

An Assessment of Water Sources for Heritage Listed Organic Mound Springs in NW Australia Using Airborne Geophysical (Electromagnetics and Magnetics) and Satellite Remote Sensing Methods

“…A normalized difference vegetation index (NDVI) has proven effective in monitoring the decline in vegetation coverage associated with drying climates across large catchments and climatic zones in Asia and northern hemisphere permafrost regions [18,19]. In sparsely vegetated semi-arid to arid regions, surface water forms a mappable contrast with surface soils, which provides the opportunity to map the changing extents of lakes and springs [20,21], while the monthly frequency of satellite data acquisition allows for seasonal interrogation of vegetation at a global scale (e.g., [22]). The authors of [2] illustrate how temporal multispectral remote sensing can contribute to understanding spring discharge and the extent of vegetation associated with these systems.…”