No abstract

The present study have used space observations of NO emission (OMI) and aerosol optical depth (AOD) (MODIS) from the last couple of years (2015)(2016)(2017)(2018)(2019) to investigate the changes in air pollution in response to COVID-19 pandemic during the lockdown period (Mar-May, 2020) over the Indo-Pak region. Result of this study reveals a huge drop in air pollution that accounted for 40-50% reduction in NO emissions and 45% in AOD over the whole Indo-Pak region. The major metropolitan areas (cities) of the region showed a remarkable decrease in NO emissions, whereas the calculated rate of reduction was found highest for the city of Lahore (Pak) ranges between 29-52% followed by Ahmadabad (Chennai) as 27-52% (32-42%) respectively. The geospatial analysis revealed the existence of positive correlation (range from 0.23-0.50) between AOD and NO emissions, which further implies that a decrease in AOD may be attributed to reduction in NO to some extents.

Understanding the pixel-scale hydrology and the spatiotemporal distribution of regional precipitation requires high precision and high-resolution precipitation data. Satellite-based precipitation products have coarse spatial resolutions (~10 km–75 km), rendering them incapable of translating high-resolution precipitation variability induced by dynamic interactions between climatic forcing, ground cover, and altitude variations. This study investigates the performance of a downscaled-calibration procedure to generate fine-scale (1 km × 1 km) gridded precipitation estimates from the coarser resolution of TRMM data (~25 km) in the Indus Basin. The mixed geographically weighted regression (MGWR) and random forest (RF) models were utilized to spatially downscale the TRMM precipitation data using high-resolution (1 km × 1 km) explanatory variables. Downscaled precipitation estimates were combined with APHRODITE rain gauge-based data using the calibration procedure (geographical ratio analysis (GRA)). Results indicated that the MGWR model performed better on fit and accuracy than the RF model to predict the precipitation. Annual TRMM estimates after downscaling and calibration not only translate the spatial heterogeneity of precipitation but also improved the agreement with rain gauge observations with a reduction in RMSE and bias of ~88 mm/year and 27%, respectively. Significant improvement was also observed in monthly (and daily) precipitation estimates with a higher reduction in RMSE and bias of ~30 mm mm/month (0.92 mm/day) and 10.57% (3.93%), respectively, after downscaling and calibration procedures. In general, the higher reduction in bias values after downscaling and calibration procedures was noted across the downstream low elevation zones (e.g., zone 1 correspond to elevation changes from 0 to 500 m). The low performance of precipitation products across the elevation zone 3 (>1000 m) might be associated with the fact that satellite observations at high-altitude regions with glacier coverage are most likely subjected to higher uncertainties. The high-resolution grided precipitation data generated by the MGWR-based proposed framework can facilitate the characterization of distributed hydrology in the Indus Basin. The method may have strong adoptability in the other catchments of the world, with varying climates and topography conditions.