Persistent scatterer interferometric analyses were conducted on a stack of 84 Environmental Satellite's Advanced Synthetic Aperture Radar scenes spanning 7 years (2004 to 2010) over the entire Nile Delta of Egypt and surroundings (area: 40,416 km2) to monitor the ongoing spatial and temporal land deformation, identify the factors controlling the deformation, and model the interplay between sea level rise and land subsidence to identify areas and populations threatened by sea encroachment by the end of the 21st century. Findings include the following: (1) general patterns of subsidence (average rate: −2.4 mm/year) in the northern delta, near‐steady to slight subsidence in the southern delta (average rate: 0.4 mm/year), separated by a previously mapped flexure zone (minimum width: 20–40 km) undergoing uplift (average rate: 2.5 mm/year); (2) high subsidence rates (up to −8.9 mm/year) over the north central and northeastern delta (area: ~4,815 km2), possibly due to compaction of recent (<3,500 years old), thick (>5 m) silt and clay‐rich Holocene sediments; (3) high subsidence rates (up to −9.7 mm/year) in areas where the highest groundwater extraction rates were reported in southern delta (Menoufia governorate) and in reclaimed desert land in the western delta (Beheira governorate); (4) high subsidence rates (up to −9.7 mm/year) over onshore gas fields, notably the Abu Madi gas field, where high gas extraction rates have been recorded; and (5) using extracted deformation rates, high‐resolution TanDEM‐X digital elevation model, a eustatic sea level rise of 0.44 m, and applying a bathtub inundation model, an estimated 2,660 km2 in northern delta will be inundated by year 2100.

The Gravity Recovery and Climate Experiment (GRACE) has been successfully used to monitor variations in terrestrial water storage (GRACETWS) and groundwater storage (GRACEGWS) across the globe, yet such applications are hindered on local scales by the limited spatial resolution of GRACE data. Using the Lower Peninsula of Michigan as a test site, we developed optimum procedures to downscale GRACE Release-06 monthly mascon solutions. A four-fold exercise was conducted. Cluster analysis was performed to identify the optimum number and distribution of clusters (areas) of contiguous pixels of similar geophysical signals (GRACETWS time series); three clusters were identified (cluster 1: 13,700 km2; cluster 2: 59,200 km2; cluster 3: 33,100 km2; Step I). Variables (total precipitation, normalized difference vegetation index (NDVI), snow cover, streamflow, Lake Michigan level, Lake Huron level, land surface temperature, soil moisture, air temperature, and evapotranspiration (ET)), which could potentially contribute to, or correlate with, GRACETWS over the test site were identified, and the dataset was randomly partitioned into training (80%) and testing (20%) datasets (Step II). Multivariate regression, artificial neural network, and extreme gradient boosting techniques were applied on the training dataset for each of the identified clusters to extract relationships between the identified hydro-climatic variables and GRACETWS solutions on a coarser scale (13,700–33,100 km2), and were used to estimate GRACETWS at a spatial resolution matching that of the fine-scale (0.125° × 0.125° or 120 km2) inputs. The statistical models were evaluated by comparing the observed and modeled GRACETWS values using the R-squared, the Nash–Sutcliffe model efficiency coefficient (NSE), and the normalized root-mean-square error (NRMSE; Step III). Lastly, temporal variations in GRACEGWS were extracted using outputs of land surface models and those of the optimum downscaling methodology (downscaled GRACETWS) (Step IV). Findings demonstrate that (1) consideration should be given to the cluster-based extreme gradient boosting technique in downscaling GRACETWS for local applications given their apparent enhanced performance (average value: R-squared: 0.86; NRMSE 0.37; NSE 0.86) over the multivariate regression (R-squared: 0.74; NRMSE 0.56; NSE 0.64) and artificial neural network (R-squared: 0.76; NRMSE 0.5; NSE 0.37) methods; and (2) identifying local hydrologic variables and the optimum downscaling approach for individual clusters is critical to implementing this method. The adopted method could potentially be used for groundwater management purposes on local scales in the study area and in similar settings elsewhere.

An integrated approach [field, Interferometric Synthetic Aperture Radar (InSAR), hydrogeology, geodesy, and spatial analysis] was adopted to identify the nature, intensity, and spatial distribution of deformational features (sinkholes, fissures, differential settling) reported over fossil aquifers in arid lands, their controlling factors, and possible remedies. The Lower Mega Aquifer System (area 2 9 10 6 km 2 ) in central and northern Arabia was used as a test site. Findings suggest that excessive groundwater extraction from the fossil aquifer is the main cause of deformation: (1) deformational features correlated spatially and/or temporally with increased agricultural development and groundwater extraction, and with a decline in water levels and groundwater storage (-3.7 ± 0.6 km . Results indicate that faults played a role in localizing deformation given that deformational sites and InSAR-based high subsidence rates (-4 to -15 mm/year) were largely found within, but not outside of, NW-SE-trending grabens bound by the Kahf fault system. Findings from the analysis of Gravity Recovery and Climate Experiment solutions indicate that sustainable extraction could be attained if groundwater extraction was reduced by 3.5-4 km 3 /year. This study provides replicable and cost-effective methodologies for optimum utilization of fossil aquifers and for minimizing deformation associated with their use.

The normalized difference vegetation index (NDVI) has been frequently used to map hail damage to vegetation, especially in agricultural areas, but observations can be blocked by cloud cover during the growing season. Here, the European Space Agency’s Sentinel-1A/ 1B C-band synthetic aperture radar (SAR) imagery in co- and cross polarization is used to identify changes in backscatter of corn and soybeans damaged by hail during intense thunderstorm events in the early and late growing season. Following a June event, hail-damaged areas produced a lower mean backscatter when compared with surrounding, unaffected pixels [vertical–vertical (VV): −1.1 dB; vertical–horizontal (VH): −1.5 dB]. Later, another event in August produced an increase in co- and cross-polarized backscatter (VV: 0.7 dB; VH: 1.7 dB) that is hypothesized to result from the combined effects of crop growth, change in structure of damaged crops, and soil moisture conditions. Hail damage regions inferred from changes in backscatter were further assessed through coherence change detections to support changes in the structure of crops damaged within the hail swath. While studies using NDVI have routinely concluded a decrease in NDVI is associated with damage, the cause of change with respect to the damaged areas in SAR backscatter values is more complex. Influences of environmental variables, such as vegetation structure, vegetation maturity, and soil moisture conditions, need to be considered when interpreting SAR backscatter and will vary throughout the growing season.

The Greater Houston metropolitan area has experienced recurring flooding events in the past two decades related to tropical cyclones and heavy inland rainfall. With the projected recurrence of severe weather events, an approach that outlines the susceptibility of different localities within the study area to potential floods based on analyses of the impacts from earlier events would be beneficial. We applied a novel C-band Sentinel-1 Synthetic Aperture Radar (SAR)-based flood detection method to map floodwater distribution following three recent severe weather events with the goal of identifying areas that are prone to future flood hazards. Attempts were made to calibrate and validate the C-band-based results and analyses to compensate for possible sources of error. These included qualitative and quantitative assessments on L-band aerial SAR data, as well as aerial imagery acquired after one of the events. The findings included the following: (1) most urban centers of Harris county, with few exceptions, are not believed to be prone to flooding hazards in contrast to the densely populated areas on the outskirts of Harris county; (2) nearly 44% of the mapped flood-prone areas lie within a 1 km distance of major drainage networks; (3) areas experiencing high subsidence rates have persistently experienced flooding, possibly exacerbated by morphological changes to the land surface induced by subsidence.