Objective/Mission
This study aims to assess the potential impacts of climate change on heat stress and water scarcity in Bangladesh over the next three decades (2021–2050). Specifically, it seeks to elucidate the intensification of heat-stress and hydrological imbalances under future climate scenarios based on dynamically downscaled projections from CMIP5 and CMIP6 global climate models using the MITRegional Climate Model (MRCM). By providing reliable, high-resolution climate information, the study intends to support policymakers and local stakeholders in formulating targeted adaptation strategies,thereby enhancing the resilience of vulnerable communities and promoting sustainable development in the face of escalating climate risks.
Approach in Year 3
To support the Jameel Observatory CREWSnet flagship areas, this study provides detailed projections of key climate variables—including dry-bulb temperature, wet-bulb temperature, precipitation, humidity,wind, surface pressure, shortwave radiation, and longwave radiation — under various climate-change scenarios (Historical, SSP1-2.6, SSP2-4.5, and SSP5-8.5), at temporal resolutions ranging from daily to monthly. These data serve as foundational input for the decision-support tool and as boundary conditions for other climate-impact models, including the Community Terrestrial Systems Model(CTSM), the MIT Economic Projection and Policy Analysis (EPPA) model, a Salinity Model, and anAgent-Based Water Scarcity Model.
Progress in Year 3
In Year 3, we additionally generated the intermediate greenhouse-gas emissions scenario (SSP2-4.5)by dynamically downscaling a CMIP6 global-climate simulation (EC-Earth3) using MRCM.In addition, we developed an initial version of agent-based, water-scarcity model to identify water scarcity hotspots in the southwestern corner of Bangladesh (Figure 12). For the development of the agent-based model, we collected various socioeconomic data (number of households from BBS,household size from BBS, average roof size from Karim 2010, average water tank size from Karim 2010, and estimated income distribution from HIES) and water resource–related data (availability of various water sources from BBS, groundwater level from BWDB, groundwater salinity level from BWDB, groundwater arsenic level from BWDB, rainfall from TRMM and MRCM, and water price fromY-RISE’s KII survey and BRAC’s KOBO survey) with support from Jakir in Eltahir Research Group atMIT, Y-RISE in Yale university, and BRAC.
At a seminar on heat waves organized by BRAC on May 23, 2024, in Dhaka, we presented the underlying causes and future projections of heatwaves in Bangladesh. The seminar brought together a diverse group of stakeholders to engage in meaningful and action-oriented dialogue on strategies for addressing the emerging challenges.
Foundational/internal modeling
The initial version of the agent-based, water-scarcity model was developed to identify water-scarcity hotspots in the southwestern corner of Bangladesh. The agent-based model integrates water-resource information (groundwater availability, groundwater salinity level, groundwater arsenic level, harvested rainwater, water from RO plants, and tap water) along with socioeconomic information (household size,household income, primary water sources, water demand, and water prices) to derive the WPI. TheWPI is defined as the ratio of expenditures on purchasing fresh water to household monthly income.Three high-performing Global Climate Models (GCMs) from the CMIP6 archive—EC-Earth3,HadGEM3-GC31-MM, and UKESM1-0-LL—were dynamically downscaled using the MRCM over the last three years. This downscaling produced high-resolution (10 km) climate scenarios for 1975–2050 under three Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5). The resulting climate projections served as key inputs for the agent-based, water-scarcity model. In particular,precipitation data were used to estimate the volume of rainwater that could be collected through RWH systems in the southwestern corner of Bangladesh.
Data Hub and Decision-Support Tool
Dynamically downscaled daily and monthly data for dry-bulb temperature, wet-bulb temperature, and precipitation were uploaded to the Data Hub for 1976–2050 under various climate change scenarios:Historical, SSP1-2.6, SSP2-4.5, and SSP5-8.5.We contributed significantly to the Housing and Shelter Resilience Flagship by identifying areas most vulnerable to extreme heat and supporting the selection of optimal locations for AFs (heatwave shelters). In addition, high-resolution (10 km) climate-change projections generated by MRCM were used to drive the Community Terrestrial Systems Model (CTSM) and the MIT Economic Projection and Policy Analysis (EPPA) model, enabling projections of agricultural and economic impacts of climate change, respectively. The MRCM projections can also be integrated with climate-impact models specific to the water sector—such as a the salinity model and the agent-based, water-scarcity model—to enhance the understanding of water-related local vulnerabilities and to inform the development of resilience strategies.