Gowhar Meraj

Flood susceptibility maps identify potentially affected land but provide limited guidance on the form of adaptation. We examined how susceptibility can inform urban retrofit, landscape retention and protection priorities in Sharjah, United Arab Emirates (UAE). Nine environmental predictors supported a Random Forest model trained on a synthetic mask derived from terrain, wetness and urban‐runoff rules. Land context and allocation rules defined 104.5 km ² of principal candidates and 128.8 km ² of advisory support. Urban and peri‐urban land contained 99.3% of retrofit and 99.5% of protection core, whereas 64.5% of retention lay in the wider region. Varying synthetic‐mask thresholds by ±20% changed principal area from 19.5 to 112.5 km ² , indicating substantial dependence on modelling assumptions. April 2024 reports provided contextual plausibility evidence, not independent validation against a systematic flood inventory. The framework connects relative susceptibility with differentiated planning responses and identifies where hydraulic, land‐ownership and implementation appraisal should begin.

The difficulty in modeling the long-term climatic variability in narrow marine corridors is that the strong air-sea coupling, large environmental gradients, and non-stationary behavior resulting in a non-stationary temporal behavior make this task difficult. The Strait of Hormuz is a dynamically controlled system in which seasonal forcing, variability in circulation and atmospheric structure interact in various time scales. Traditional statistical and deep learning methods tend to have difficulty in producing consistent climatic tracks using dissimilar remote-sensing variables in such circumstances. In this study, Strait-ClimateGradientSynthNet is presented as a gradient-aware deep learning model which is applied to the generation of long-horizon climatic trajectories based on assimilated multi-source reanalysis observations. This architecture makes use of hierarchical temporal coding which is based on recurrent neural networks to learn long-term climatic persistence along with regime switches based on gradient-sensitive features. The assessment of the framework is conducted with the help of multi-decadal ERA5 reanalysis data (including atmospheric, oceanic, radiative, and vertical-structure variables) over the Strait of Hormuz. The outcomes indicate that the suggested methodology has consistent and plausible trajectory reconstructions, and the prediction error is RMSE = 0.3973 °C, MAE = 0.3156 °C, and R ² = 0.7390 to the monthly near-surface air temperature anomalies. Comparison analysis suggests better generalization compared to more complicated architectures when there is limited data. The results not only show that the proposed framework is effective in terms of parameter efficiency in synthesizing regional climate trajectories, but also drive the relevance of the proposed framework to climate risk assessment and long-term environmental surveillance in the dynamically constrained marine environment.

Stubble burning in the Trans Indo-Gangetic Plain (IGP) significantly contributes to air pollution, posing severe threats to human health and the environment. This study investigates the interactions between stubble burning, vegetation productivity, atmospheric loading, and wind anomalies across the Trans Indo-Gangetic Plain using MODIS burned-area and gross primary productivity products, TerraClimate wind fields, MERRA-2 aerosol reanalysis, and AIRS carbon monoxide retrievals. Burned area, vegetation productivity, aerosol loading, and wind variability were analysed for the period 2001–2020, whereas AIRS-derived CO was analysed for 2002 to 2020. Our analysis reveals a 107% increase in Kharif burned area, from 11,630 km ² in 2002 to 24,053 km ² in 2015. Aerosol loading reached peak values of 8.68 in 2002 and 8.66 in 2018, indicating periods of elevated atmospheric particulate burden. Carbon monoxide levels also exhibited distinct seasonal variations, peaking at 137.22 parts per billion in 2009 during the Zaid season. The study highlights marked seasonal variations in wind speed, particularly in winter and spring, which influence pollutant dispersion and accumulation and may worsen air quality in urban centres such as Delhi. These findings underscore the need for targeted policy interventions that incorporate meteorological dynamics into air quality models. Promoting sustainable farming practices, such as crop rotation, zero-tillage farming, and bioenergy generation from agricultural waste, is crucial for mitigating the environmental impacts of stubble burning. This research provides critical insights for developing data-driven strategies to improve air quality and promote environmental sustainability in the Trans IGP region.