Research Output
Articles
Publication Date: 2024
Remote Sensing Applications: Society and Environment (23529385)35
Land subsidence has emerged as a significant global concern, particularly in arid regions like Iran. Among these vulnerable areas, the Isfahan-Borkhar plain stands out due to its strategic importance and critical high-population settlements. In this study, we focus specifically on land subsidence within this plain. This study specifically examines land subsidence in this plain. Utilizing the precise radar interferometry technique, considered the most accurate method, this study investigates the vertical changes of the Earth's surface in the Isfahan-Borkhar plain between 2015 and 2022 by analyzing Sentinel-1 satellite data. To establish causality, we correlate these subsidence patterns with spatial water extraction data, employing a spatial bivariate relationship model. The results obtained from this seven-year monitoring period reveal that average annual subsidence in different areas of the Isfahan-Borkhar plain ranges from 25 to 45 cm, while the average annual decline in the water table in this aquifer is 230 cm. The pronounced subsidence observed aligns with areas experiencing the highest water level decline, emphasizing the critical role of water table depletion. In summary, our study underscores the need for sustainable water management practices to mitigate land subsidence risks in this strategically vital region. © 2024 Elsevier B.V.
Publication Date: 2023
Journal of Hazardous Materials (18733336)455
The current study was established for predicting some selected heavy metals (HMs) including Zn, Mn, Fe, Co, Cr, Ni, and Cu, by applying random forest (RF) and a set of environmental covariates at watershed scale. The objectives were to find out the most effective combination of variables and controlling factors on the variability of HMs in a semiarid watershed in central Iran. One hundred locations were selected in the given watershed in the hypercube manner and soil samples from a surface 0–20 cm depth and concentration of HMs and some soil properties were measured in the laboratory. Three scenarios of input variables were defined for HMs prediction. The results revealed that the first scenario (remote sensing + topographic attributes) explained about 27–34% of the variability in HMs. Inclusion of a thematic map to the scenario I, improved the prediction accuracy for all HMs. Scenario III (remote sensing data+ topographic attributes + soil properties) was the most efficient scenario for prediction of HMs with R2 values ranging from 0.32 for Cu to 0.42 for Fe. Similarly, the lowest nRMSE was found for all HMs in scenario III, ranging from 0.271 for Fe to 0.351 for Cu. Among the soil properties, clay content and magnetic susceptibility were the most important variables, and also some remote sensing data (Carbonate index, Soil adjusted vegetation index, Band2, and Band7) and topographic attributes (mainly control soil redistribution along the landscape) were the most efficient variables for estimating HMs. We concluded that the RF model with a combination of remote sensing data, topographic attributes, and assisting of thematic maps such as land use in the studied watershed could reliably predict HMs content. © 2023 Elsevier B.V.
Publication Date: 2023
Stochastic Environmental Research And Risk Assessment (14363240)37(10)pp. 4029-4050
This study aims to employ joint modeling for drought and dust hazards. Long-term monthly precipitation and temperature were used to calculate drought characteristics based on the standardized precipitation- evapotranspiration index. Three-hourly horizontal visibility and wind erosion event codes were also obtained to detect dust events. The results of drought hazard assessment indicated that about 52% of the total area of Iran was affected by moderate hazard. Other classes of drought hazard i.e., severe, low, and very severe, covered approximately 24, 18, and 6%, respectively. The assessment of dust hazard was conducted by weighting the frequency of dust events based on the horizontal visibility. The findings on dust hazard showed that approximately 18% of the total area of Iran was affected by the more intense hazard, which covered the southeastern sectors. Bivariate copulas were considered for joint modeling of drought and dust hazards. The copulas of Student’s t, Gaussian, Gumbel, and others were applied to construct a joint model. Bivariate assessment of drought and dust hazard illustrated that the eastern part of Iran, which covers around 47% of the country, exposed at greater hazard than other parts. The copula-based conditional probability was also obtained to quantify the probability of dust occurrence under different drought hazard levels. The conditional probability values showed that by intensification of drought conditions the probability of dust hazard increased. In addition the frequency of dust occurrences increased when the region faced very severe droughts. Generally, the prolonged and severe droughts were identified as the most important reasons for the increased hazard of dust and drought in the study region. This study results indicated that the joint risk of drought and dust hazard along with vulnerability factors, can be used for possible mitigation of the risks of these natural hazards. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
