ESSG develops and deploys innovative models critical for cyclone risk analysis in Bangladesh. Our original modeling includes cyclone downscaling and Dn-SPAL methods for extreme rainfall, using 21large-ensemble approaches to quantify uncertainty. We employ state-of-the-art 2D and 3D flood models and have developed new coupled inundation-salinity models for inland (pluvial and fluvial) flooding. In Year 3, we integrated improved wind modeling into coastal flood simulations. Our dynamic observatory utilizes informative observation strategies to optimally and densely gather adaptive soil-salinity data. Additionally, we introduced novel “informative learning” methods in machine learning to improve model accuracy and interpretability. We developed new satellite-driven methods to classify polder-level structures, particularly ghers, and piloted agent-based, agricultural-yield modeling in collaboration with IIT Bombay. Our original computational games engage communities in decision-making, forming a basis for livelihood-resilience strategies. Overall, ESSG’s modeling advanced hazard assessments at national and sub-national scales, mapping and observations at sub-national and local scales, and resilience-driven decision-making at the local level.
The outcomes include cyclone-induced wind hazards, cyclone and seasonal rain hazards, and flood- hazard assessments in a large ensemble uncertainty quantifying setting. The storm-tide assessment is the first such result for Bangladesh for a changing climate under a wide variety of scenarios and models. The dynamic observatory generates high-resolution (30-m) root-zone, soil-salinity maps, and is poised to enter into a self-sustaining operational model. It informatively gathers new salinity samples and processes them at ESSG’s soil laboratory currently housed at KUET. The new PASO system will offer a permanent predictive solution to soil -salinity monitoring in southwest Bangladesh, and our work also directly informs YRISE’s efforts with water entrepreneurship.