Water scarcity in arid and semiarid regions has resulted in using of low-quality waters for crop irrigation. This study aims to investigate the spatial relationship of low-quality irrigation water and waterlogging in arid and semiarid environments. The multi-decadal (1990–2020) time series Landsat images and hadrochemical water analysis were employed within geographical information system mapping (GIS) to understand the relationship between irrigation water, soil salinity, and waterlogging in the western Nile Delta, Egypt. The normalized difference water index (NDWI) and the normalized difference vegetation index (NDVI) from the Landsat images were combined to quantify the Spatiotemporal changes in the croplands and waterlogging from 1990 to 2020. ArcGIS inverse distance weighted (IDW) interpolation was used to create spatial layers of irrigation water salinity from electrical conductivity (EC), sodium adsorption ratio (SAR), and soluble sodium percentage (Na%). The results demonstrated a significant spatial relationship between waterlogging and EC, SAR, and Na% in irrigation groundwater. Long-term irrigation with high salinity groundwater led to increased soil salinity, low soil permeability, and waterlogging. This study offers a time- and cost-efficient geospatial method for regional monitoring of surface waterlogging and mitigation strategies for cropland degradation and agricultural drainage water recycling that would benefit stakeholders and decision-makers.

This study presents an integrated approach for the identification of groundwater occurrences in shallow fracture zone (SFZ) aquifers using remote-sensing, geological, and geophysical data. The Central Eastern Desert of Egypt was selected as a test site for the present study. The distribution of major faults and shear zones was extracted from a fusion image generated by injecting high-spatial resolution phased array L-band synthetic aperture radar (PALSAR) images into Landsat Enhanced Thematic Mapper images. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) digital elevation model was processed to extract the drainage systems, slope, and topographic wetness index (TWI). The multidate PALSAR imagery acquired during rainy and dry seasons was used to estimate the relative soil moisture content. The lithology, fractures, drainage density, slope, TWI, and soil moisture content were used as thematic layers for groundwater occurrence in the SFZ aquifers. A GIS model of groundwater potential was developed by selecting the most probable locations for groundwater in each layer. The results indicate that the spatial distribution of the existing water wells is in agreement with the model where all wells fall in the regions of high groundwater potential zones. A geophysical survey was conducted using ground penetrating radar (GPR), indicating that the high groundwater potential zones are promising for drilling shallow wells. The adopted approach can be used as a cost-effective tool for groundwater exploration in the SFZ aquifers in the study area and in areas of similar geologic and hydrogeologic settings elsewhere.

Accurate and timely flood mapping is critical for informing emergency response and risk mitigation during extreme weather events. This study presents a synthetic aperture radar (SAR)-based approach for rapid flood extent mapping using Sentinel-1 imagery, demonstrated for Tropical Storm Imelda (17–21 September 2019) in southeastern Texas. Dual-polarization Sentinel-1 SAR data (VH and VV) were processed by computing the VH/VV backscatter ratio, and the resulting ratio image was classified using a supervised Random Forest classifier to delineate water and land. All Sentinel-1 images underwent radiometric calibration, speckle noise filtering, and terrain correction to ensure precision in flood delineation. The Random Forest classifier achieved an overall flood mapping accuracy exceeding 94%, with Cohen’s kappa coefficients of approximately 0.75–0.80, demonstrating the approach’s reliability in distinguishing transient floodwaters from permanent water bodies. The spatial distribution of flooding was strongly influenced by topography and land cover. Analysis of Shuttle Radar Topography Mission (SRTM) digital elevation data revealed that low-lying, flat terrain was most vulnerable to inundation; correspondingly, the land cover types most affected were hay/pasture, cultivated land, and emergent wetlands. Additionally, urban areas with low-intensity development experienced extensive flooding, attributed to impervious surfaces exacerbating runoff. A strong, statistically significant correlation (R² = 0.87, p < 0.01) was observed between precipitation and flood extent, indicating that heavier rainfall led to greater inundation; accordingly, the areas with the highest rainfall totals (e.g., Jefferson and Chambers counties) experienced the most extensive flooding, as confirmed by SAR-based change detection. The proposed approach eliminates the need for manual threshold selection, thereby reducing misclassification errors due to speckle noise and land cover heterogeneity. Harnessing globally available Sentinel-1 data with near-real-time processing and a robust classifier, this approach provides a scalable solution for rapid flood monitoring. These findings underscore the potential of SAR-based flood mapping under adverse weather conditions, thereby contributing to improved disaster preparedness and resilience in flood-prone regions.

Climate change and the associated sea level rise (SLR) are presenting newfound challenges to the port systems and coastal transportation infrastructure of southeast Texas. This paper introduces a Geographic Information Systems (GIS) based model designed to simulate inundation scenarios under various sea-level projections, aiming to assess the vulnerabilities of both port facilities and road networks. The study area encompasses a specific region within Jefferson County, southeast Texas, encompassing three major ports: Port Arthur, Beaumont, and Orange. Utilizing a high-resolution (1-m) Digital Elevation Model (DEM) derived from the 2017 LiDAR dataset, this model is integrated with NASA’s sea-level rise projections to compute the extent and volume of inundation across low, medium, and high SLR scenarios. Drawing from monthly mean sea level data spanning from 1958 to 2020, the lowest SLR projections, derived from the relative sea-level trend measured at the Sabine Pass, TX gauge station, indicate a yearly increase of 6.16 mm, with a 95% confidence interval of +/- 0.74 mm. Projections for 2050 and 2,100 show the lowest SLR at 0.17 m and 0.48 m, respectively. In contrast, the medium to high RSLR projections under the IPCC SSP3-7.0 scenario for 2050 and 2,100 stand at 0.54 m and 1.34 m, respectively. The findings reveal that, under medium to high SLR scenarios, the extent of inundated areas in the study region is expected to expand by 12.4% in 2050 and 19.9% in 2,100, compared to the lowest SLR projection. Additionally, the length of submerged roadways is predicted to increase by 6.9% in 2050 and 13.3% in 2,100, in comparison to the lowest SLR projection. It is worth noting that some margin of error may be introduced due to factors such as the width of the port area and access roads, the high-resolution DEM, and the alignment of computed inundated areas with the existing topography. Overall, the manuscript highlights the urgency of proactive planning and underscores the importance of safeguarding critical infrastructure in the context of climate change and SLR.