GovTwin / Institution

Lalmonirhat District

Local Gov

A small northern border district on the Teesta and Dharla rivers, abutting India and historically shaped by enclave geography and a flood-and-erosion-prone floodplain. Its economy rests on smallholder agriculture, and it shows some of the weakest economic dynamism in the country.

Wealth rank 14/64 (1 = poorest district) Warming +0.32°C (1980s–2020s) Air NO₂ #50/64 (1 = most polluted) Night-lights +38% (2014–23 activity) Built-up 21 km² Forest loss 110 ha (2001–23) Rainfall 2,942 mm/yr

Indicators: Meta RWI (HDX); ERA5-Land; MODIS; Sentinel-5P; VIIRS night-lights; GHSL; Hansen v1.11; CHIRPS v2.0. Exposure: GloFAS v2.1, FABDEM, MODIS LST, ACAG PM2.5, WorldPop 2020.

Problems and issues

  1. economy Economic activity is nearly stagnant: nightlights grew only 38% and the district ranks 63rd of 64 in nightlights growth, the slowest among these four and near the bottom nationally. So what: Stalled economic growth signals scant job creation, leaving a poor population reliant on agriculture and outmigration. Source: VIIRS nighttime lights (annual radiance) via Google Earth Engine
  2. poverty The district is among the poorest nationally, ranking 14th of 64 by mean Relative Wealth Index. So what: Deep poverty alongside stagnant growth means few local pathways out of deprivation, entrenching reliance on remittances and seasonal labour. Source: Meta Data for Good Relative Wealth Index (HDX), ~2.4 km grid
  3. climate disaster Very high monsoon rainfall (2,942 mm/yr) drives flash flooding and bank erosion along the Teesta and Dharla, which cut across the district. So what: Sudden Teesta floods and erosion repeatedly damage crops and homes in a district with little fiscal room to recover. Source: CHIRPS v2.0 precipitation (UCSB Climate Hazards Group) via Google Earth Engine
  4. environment Forest loss of about 110 ha over 2001-23 is high relative to the district's modest tree cover (112.5 km2), the smallest among these four districts. So what: Continued tree-cover loss on an already thin base erodes shade, fuelwood and windbreaks that buffer smallholders against heat and storms. Source: Hansen Global Forest Change v1.11 (UMD) via Google Earth Engine
  5. air quality Aerosol loading is notably high, ranking 14th-worst of 64 districts in aerosol optical depth (0.709), pointing to elevated seasonal particulate haze. So what: High aerosol pollution carries respiratory-health costs and signals dry-season air burdens in a district with weak health infrastructure. Source: MODIS MAIAC aerosol optical depth (550 nm) via Google Earth Engine

Probable solutions

Upazilas (5)

Lalmanirhat Sadar Aditmari Kaliganj Hatibandha Patgram