Girish Patidar

The Surface Water and Ocean Topographic (SWOT) mission is aimed to improve flood mapping by providing high-resolution data of water surface elevation, slope and water extent for rivers wider than 100 m and water bodies as small as 62500 m2. Although, SWOT’s data latency and huge data size of rawest point cloud data make it impracticable for real or near-real-time flood forecasting but the SWOT data will provide new insight in the first-hand information of flood mapping. In this study, we explore the potential of SWOT mission for flood mapping over India. It is observed that the spatio-temporal resolution of SWOT allows the observation of 0.67%, 15.79%, 29.24%, 45.54%, and 8.06% of Indian districts with one, two, three, four and more than four observations per cycle of SWOT respectively. It is observed that SWOT would have observed 49.6% of flood events at least once if the mission were launched earlier. The SWOT could also observe 30.6% of small flood events with flood duration <4 at least once. Using the information, this study proposes a flood severity map by combining geomorphological and physical flood parameters for a reach of Brahmaputra river. From the map different flood severity zones can be observed.

An important goal of the Surface Water and Ocean Topography (SWOT) mission is to provide river width, height, slope to estimate global river discharges. To appraise the feasibility of SWOT data over India, the present study utilizes the SWOT simulator from Centre National D’Etudes Spatiales (CNES) to generate a long time-series (three years) of SWOT observations over the Mahanadi River basin in India. Two independent sources of river geometry are used in this study, namely satellite-based and hydrodynamic model-based estimates. Satellite-based river geometry is derived using a web-based User Interface (UI) developed in-house to generate time series of accurate river width shapefiles from Sentinel-1 satellite imagery. To compare these results, a 1D unsteady flow analysis is carried out over the Mahanadi River to simulate channel water depth and inundation extent using the Hydrologic Engineering Center's River Analysis System (HEC-RAS) model. The simulated observations are used in this study to assess the potential error in SWOT channel water depth measurements. Results indicate a good spatial correlation between the water surface area derived from Sentinel and HEC-RAS models with a correlation of 0.74. The simulated data shows a bias of 20 cm compared to gauge observations. This study is conducted as a proof of concept in demonstrating the ability of simulated SWOT data in capturing river water surface elevation with respect to the conventional HEC-RAS model. It also implements a new tool in google earth engine (GEE) to generate input to SWOT simulator.

ISRO’s Scatsat-1 scatterometer, launched on 26th September 2016, supports the OSCAT-2 scatterometer as a continuation and enhancement of the OSCAT onboard the OceanSat-2. This paper carries out a detailed inter-comparison between the swath-wise level 2B (L2B) data from Scatsat-1 with in situ wind velocity data derived from three different moored buoy arrays: the Global Tropical Moored Buoy Array (GTMBA,) National Data Buoy Center (NDBC), and the Ocean Moored buoy Network for northern Indian Ocean (OMNI.) While the three arrays collectively span most of the ocean basins, they are primarily concentrated in the tropical regions and the coast of North America; therefore, to facilitate an assessment of the agreement between the satellite and in situ data in different regions, the comparison is split between tropical, extra-tropical and coastal regions for each ocean basin. The statistics thus derived are however biased towards buoys with a greater number of collocated observations and therefore the agreement between the two wind vector datasets are also estimated at each buoy location.As wind direction is a circular variable, circular/directional statistics has been used to compute the relevant parameters for wind direction. A temporal analysis of the agreement between WS and WD has also been carried out and it indicates that months with high WD bias contain a relatively greater number of observations with errors close to 180°.

Monitoring of total suspended matter (TSM) concentration in the coastal waters is vital for water quality monitoring and coastal management. In this study, TSM over the highly dynamic Hooghly estuary region is derived using MODIS surface reflectances at 645 nm and in-situ TSM observations. MODIS TSM products show a correlation of 0.95, root mean square error of 24.72 g/m3 and mean absolute & percentage errors of 18.25 g/m3 and 23.2%, respectively when compared with in-situ measurements. Subsequently, TSM variability in the Hooghly estuary from the derived TSM maps were analyzed during the period 2003-2018 on monthly and seasonal time scales. Annual cycle of TSM showed peak concentration (> 250 g/m3) during southwest monsoon season which could be attributed to large-scale river discharge as compared to the northeast and inter-monsoon seasons (~100150 g/m3). Inter-annual variability showed higher TSM during the years 2004, 2012, 2013 and low during 2005 and 2015. It could be concluded that the fine-tuning of existing TSM retrieval algorithm is essential based on long term earth observation data for monitoring the sediment distribution in the coastal and estuarine regions utilizing available satellite observations, particularly in the highly turbid estuaries like the Hooghly estuary.

Lockdown seems the most effective way to prevent the spread of Coronavirus disease (COVID-19) as no vaccine is currently available in the market to cure it. Thus, India has enforced nation-wide lockdown from 25th March to lower the spread of this contagious virus and associated illness. This study aims to quantify the changes in pollution levels as well as meteorology during the 6-weeks COVID-19 lockdown over 17 cities of India for 5 major criteria pollutants using publicly available air quality data. Hourly averaged data is accessed from the air quality monitoring stations during the lockdown and immediate pre-lockdown periods and also corresponding data from the previous year (2019). During the lockdown, PM2.5, PM10, NO2, and CO reduced significantly with relatively small changes in meteorological conditions compared to the pre-lockdown period. The highest decline is observed over Ahmedabad (68%), Delhi (71%), Bangalore (87%), and Nagpur (63%) for PM2.5, PM10, NO2, and CO, respectively. The Northern region shows the highest decline for all the pollutants with most days below NAAQS during lockdown—86%, 68%, and 100% compared to 18%, 0%, and 38% in 2019 for PM2.5, PM10, and NO2, respectively. The smaller cities Dewas and Jorapokhar show lesser improvement with only 3% and 16% improvement in days under NAAQS for PM2.5. SO2 is the least affected pollutant with little improvement. The major decline is observed during 7–10 am and 7–10 pm hours of the day for PM2.5, PM10, NO2, and CO with more than 40% reduction. The meteorological changes are very small and heterogeneous over India showing a similar extent of changes compared to the previous year but the pollution levels have reduced significantly. Thus, the sharp decline in pollutant concentration during the ~6 weeks period national lockdown can be attributed to the reduced economic and transport activities.