Improving Flood Mapping Capabilities and Hydrological Model Calibration in India through the Surface Water and Ocean Topography (SWOT) Mission

Even though altimetry has redefined our understanding of global rivers and lakes, the sparse temporal sampling of altimeters is often a cause of concern for many applications. This study explores the efficiency of temporal sampling offered by the recently launched Surface Water and Ocean Topography (SWOT) mission for hydrological applications over India. In particular, two research hypotheses are being investigated, namely a). Potential of SWOT data for enhancing flood mapping capabilities across India and b) Impact of SWOT-based discharges for calibrating a hydrological model calibration. Toward answering the first hypothesis, we considered a hypothetical launch date for SWOT, generating overpass data based on the mission's spatiotemporal orbital configuration. These overpass data were then compared with flood-affected areas identified in the Indian Flood Inventory (IFI) data to assess SWOT's potential for flood mapping. Results show that the spatio-temporal resolution of SWOT facilitates the monitoring of diverse proportions of Indian districts based on the cycle. More specifically, 0.67%, 15.79%, 29.24%, 45.54%, and 8.06% of Indian districts have one, two, three, four, and more than four observations per SWOT cycle (~21 days), respectively. To evaluate the second hypothesis, namely, the feasibility of SWOT discharge in hydrological model calibration, we created proxy-SWOT data by sampling in-situ data in accordance with the SWOT orbit configuration. Subsequently, errors were introduced into the in-situ gauge data based on recommendations from the SWOT science team. Results are presented over selected case study region of the Mahanadi river basin in India.