NorESM climate scenario data of 4.5 and 8.5 W/m2 of daily rainfall Tmin and Tmax.

Daily NEX-GDDP data (Tmin, Tmax and Precipitation) for different scenario (reference period, future projection), RCP 4.5 and RCP 8.5. These following models are included in data; ACCESS1-0,bcc-csm1-1,BNU-ESM,CanESM2,CCSM4,CESM1-BGC,CNRM-CM5,CSIRO-Mk3-6-0,GFDL-CM3,GFDL-ESM2G,GFDL-ESM2M,inmcm4,IPSL-CM5A-LR,IPSL-CM5A-MR,MIROC5,MIROC-ESM,MIROC-ESM-CHEM,MPI-ESM-LR,MPI-ESM-MR,MRI-CGCM3,NorESM1-M are used for simulating the global climate change between 1950- 2100.

RCM Climate data for different scenario (reference period, future projection), RCP 4.5 and RCP 8.5. These following models are included in data;GISS-E2-R-r4i1p1, IPSL-CM5A-LR-r4i1p1,IPSL-CM5A-LR-r3i1p1, CanESM2-r4i1p1, GFDL-ESM2G-r1i1p1, IPSL-CM5A-LR-r4i1p1, CSIRO-Mk3-6-0-r3i1p1, CanESM2-r4i1p1 are used for simulating the global climate change between 1998- 2050.

Precipitaton(%) and temperature(delta T(K)) for RCP4.5 and RCP 8.5 for 4 GCM, monthly change grids at a resolution of 25km covering the whole model domain, grids showing the average monthly value during 2021-2050 relative to the average monthly value during 1961-1990.

Bangladesh is one of the most flood-affected countries in the world. In the last few decades, flood frequency, intensity, duration, and devastation have increased in Bangladesh. Identifying flood-damaged areas is highly essential for an effective flood response. This study aimed at developing an operational methodology for rapid flood inundation and potential flood damaged area mapping to support a quick and effective event response. Sentinel-1 images from March, April, June, and August 2017 were used to generate inundation extents of the corresponding months. The 2017 pre-flood land cover maps were prepared using Landsat-8 images to identify major land cover on the ground before flooding. The overall accuracy of flood inundation mapping was 96.44% and the accuracy of the land cover map was 87.51%. The total flood inundated area corresponded to 2.01%, 4.53%, and 7.01% for the months April, June, and August 2017, respectively. Based on the Landsat-8 derived land cover information, the study determined that cropland damaged by floods was 1.51% in April, 3.46% in June, 5.30% in August, located mostly in the Sylhet and Rangpur divisions. Finally, flood inundation maps were distributed to the broader user community to aid in hazard response. The data and methodology of the study can be replicated for every year to map flooding in Bangladesh.

Bangladesh is one of the most flood-affected countries in the world. In the last few decades, flood frequency, intensity, duration, and devastation have increased in Bangladesh. Identifying flood-damaged areas is highly essential for an effective flood response. This study aimed at developing an operational methodology for rapid flood inundation and potential flood damaged area mapping to support a quick and effective event response. Sentinel-1 images from March, April, June, and August 2017 were used to generate inundation extents of the corresponding months. The 2017 pre-flood land cover maps were prepared using Landsat-8 images to identify major land cover on the ground before flooding. The overall accuracy of flood inundation mapping was 96.44% and the accuracy of the land cover map was 87.51%. The total flood inundated area corresponded to 2.01%, 4.53%, and 7.01% for the months April, June, and August 2017, respectively. Based on the Landsat-8 derived land cover information, the study determined that cropland damaged by floods was 1.51% in April, 3.46% in June, 5.30% in August, located mostly in the Sylhet and Rangpur divisions. Finally, flood inundation maps were distributed to the broader user community to aid in hazard response. The data and methodology of the study can be replicated for every year to map flooding in Bangladesh.

Bangladesh is one of the most flood-affected countries in the world. In the last few decades, flood frequency, intensity, duration, and devastation have increased in Bangladesh. Identifying flood-damaged areas is highly essential for an effective flood response. This study aimed at developing an operational methodology for rapid flood inundation and potential flood damaged area mapping to support a quick and effective event response. Sentinel-1 images from March, April, June, and August 2017 were used to generate inundation extents of the corresponding months. The 2017 pre-flood land cover maps were prepared using Landsat-8 images to identify major land cover on the ground before flooding. The overall accuracy of flood inundation mapping was 96.44% and the accuracy of the land cover map was 87.51%. The total flood inundated area corresponded to 2.01%, 4.53%, and 7.01% for the months April, June, and August 2017, respectively. Based on the Landsat-8 derived land cover information, the study determined that cropland damaged by floods was 1.51% in April, 3.46% in June, 5.30% in August, located mostly in the Sylhet and Rangpur divisions. Finally, flood inundation maps were distributed to the broader user community to aid in hazard response. The data and methodology of the study can be replicated for every year to map flooding in Bangladesh.

Average delta change for monthly mean air temperature (Tavg), monthly maximum air temperature (Tmax), monthly temperature (Tmin) and monthly precipitation (P). This dataset has a monthly temporal resolution spanning 42 years from 1 January 2008 to 31 December 2050 and a spatial resolution of 25x25 km. RCP 4.5(model available BNU-ESM_r1i1p1, inmcm4_r1i1p1, CMCC-CMS_r1i1p1, CSIRO-Mk3-6-0_r4i1p1) ; RCP 8.5(model available inmcm4_r1i1p1, CMCC-CMS_r1i1p1, bcc-csm1-1_r1i1p1, CanESM2_r3i1p1).

Poverty and Vulnerability Assessment (PVA) is a district level household survey to understand vulnerability of households to environmental and socioeconomic changes with focus on mountain specificity as well as their coping strategies and adaptive capacity. Under the Adaptation to Change program, the Himalaya Climate Change Adaptation Programme (HICAP) initiative have conducted the survey among 2647 households across 7 districts in states of Assam and Arunanchal Pradesh in the Eastern Brahmaputra sub-basin in India. The households were selected following a probability based multi-stage cluster sampling approach.

The comprehensive baseline information on the glaciers of the HKH region was generated semi-automatically using more than 200 Landsat 7 ETM+ images of 2005 ± 3 years with minimum cloud and snow coverage. The glacier outlines were derived by using object-based image classification method separately for clean-ice and debris-covered glaciers with some manual intervention. The attribute data were assigned to each glacier using 90m resolution SRTM DEM. This data does not cover the China part.