Climate change and overpopulation have led to an increase in water demands worldwide. As a result, land subsidence due to groundwater extraction and water level decline is causing damage to communities in arid and semiarid regions. The agricultural plain of Samalghan in Iran has recently experienced wide areas of land subsidence, which is hypothesized to be caused by groundwater overexploitation. This hypothesis was assessed by estimating the amount of subsidence that occurred in the Samalghan plain using DInSAR based on an analysis of 25 Sentinel-1 descending SAR images over 6 years. To assess the influence of water level changes on this phenomenon, groundwater level maps were produced, and their relationship with land subsidence was evaluated. Results showed that one major cause of the subsidence in the Samalghan plain was groundwater overexploitation, with the highest average land subsidence occurring in 2019 (34 cm) and the lowest in 2015 and 2018 (18 cm). Twelve Sentinel-1 ascending images were used for relative validation of the DInSAR processing. The correlation value varied from 0.69 to 0.89 (an acceptable range). Finally, the aquifer behavior was studied, and changes in cultivation patterns and optimal utilization of groundwater resources were suggested as practical strategies to control the current situation.
Abstract. Differential Synthetic Aperture Radar Interferometry (DInSAR) allows displacements to be detected with millimeter accuracy as well as more advanced methods, such as Persistent Scatterer Interferometry (PSI). Sentinel-1 data have been collected systematically under the COPERNICUS program at a high temporal resolution with global coverage, helping us to build a wide user community and develop miscellaneous SAR-based applications. In the Garmsar alluvial fan, the long-term groundwater overexploitation due to agricultural and urban demands, the utilization of urban space, and erosion have led to land deformation. In this study, the analysis of land subsidence in Garmsar fan was assessed by using the Differential Interferometric Synthetic Aperture Radar (DInSAR) technique based on 20 Sentinel-1 SAR images from January 2019 to June 2022. Distinct variations of land subsidence were found in the study regions however, it can be seen in most land use types. The maximum annual land subsidence rate has occurred in urban areas with an average rate of 95.2 mm/year from 2019 to 2022. Analysis showed that serious land subsidence mainly occurred in the following land use types: urban areas, agricultural lands, and bare lands.
Water balance is one of the most important issues in water resources management and water consumption planning. ABCD water balance conceptual model is a very suitable simulation tool due to its simplicity, low requirement of input data, and providing various components of the water balance. As the lack of data is always a major challenge in many developing countries, remote sensing technology was used to collect the required data for the Zarandeh sub-basin in Neyshabur in the Northeastern Iran. To do so, IMERG precipitation satellite products and ERA5 temperature reanalysis data were used. Outputs were evaluated using five statistical indices including Pearson Correlation Coefficient (R), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Nash-Sutcliffe Efficiency Coefficient (NSE), and BIAS Coefficient. The Results indicated that products are accurate, and there is a high correlation between outputs and observed data. To calculate the water balance, remote sensing products were applied in the form of the ABCD model. The uncertainties in the model parameters were assessed through Fuzzy numbers. In addition, the Monte Carlo method was employed to calibrate them with two different objective functions including NSE and Coefficient of Determination (R2). The results showed that the ABCD water balance model is an accurate tool for simulating the surface runoff of the Zarandeh sub-basin. Finally, the model was applied to the Sebi sub-basin located in Torbat-e-Heydariyeh, which had a moderate performance showing that the model parameters should be recalibrated in each region.
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