Integrating Remotely Sensed Datasets to Investigate Hydrological Processes and Hydroclimatic Extreme Risks in Data-Scarce and Vulnerable Regions of Asia

The frequency and intensity of extreme weather events, such as floods and droughts, have risen due to global warming and climate change. These events disproportionately impact resource-scarce regions with poor infrastructure, leading to unprecedented suffering and significant loss of lives and property, especially in low- and middle-income countries of Asia. This dissertation advances the understanding of climate-induced hydrological vulnerabilities and improves disaster response strategies through integrated assessments and innovative methodologies. A Climate Risk and Vulnerability Assessment (CRVA) framework evaluated Georgia’s regional vulnerability to climate change by analyzing geographic and socio-economic sensitivity alongside climate exposure using indices such as maximum temperature, total precipitation, heavy rainfall events, and drought duration under RCP 4.5 and 8.5 scenarios. The findings highlight critical hotspots where climate change significantly threatens water resources. Further, satellite-derived precipitation products (SPPs) were validated against gauge data in Nepal’s Myagdi Khola watershed to address hydrological modelling challenges in data-scarce regions. Performance evaluations of SPPs, for instance, SM2RAIN-ASCAT, GPM IMERGF, MSWEP, and CHIRPS, using the Soil and Water Assessment Tool (SWAT) indicated that SM2RAIN-ASCAT and GPM IMERGF outperformed others, providing higher Nash-Sutcliffe Efficiency (NSE) values after calibration. These results demonstrate the potential of SPPs to improve hydrological modeling accuracy in mountainous watersheds. In Pakistan’s Indus River Basin, the extreme flooding of 2022 was analyzed by investigating the interplay of hydroclimatic factors, including land surface temperature anomalies, snow cover reduction, saturated antecedent soil moisture, and extreme monsoon precipitation. The study revealed a >25% decline in snow cover and a ~300% increase in monsoon rainfall in July and August, highlighting the compounded impacts of hydroclimatic extremes. Lastly, an innovative remote sensing-based approach was applied to the 2008 Koshi River flood in the alluvial plains of Bihar, India, caused by an embankment breach. Using the Modified Normalized Difference Water Index (MNDWI) combined with the Normalized Difference Vegetation Index (NDVI), permanent water bodies, and Height Above the Nearest Drainage (HAND) data, the improvement in flood inundation map was achieved with improved accuracy. It validated results with HEC-RAS 2D hydrodynamic modeling and binary classification assessment metrics. The accuracy of the remotely sensed flood inundation map using the 0.3 MNDWI threshold was 81%, indicating good predictability capability of the Landsat satellite data to map flooding events with the improvements in mapping techniques outlined earlier. This research underscores the critical role of remote sensing, hydrological and hydrodynamic modeling, and vulnerability assessment in addressing the multifaceted impacts of climate change on regional hydrology and flood management in data-scarce regions.