Geomorphic classification of large rivers identifies morphological patterns, as a foundation for estimating biogeochemical and ecological processes. In order to support the modelling of in-channel nutrient retention or export, the classification of geomorphic units (GUs) was done in the Padma River, Bangladesh, a large and geomorphically-complex lowland river. GUs were classified using the normalized difference vegetation index (NDVI) four times over a year, so as to cover the seasonal variation of water flows. GUs were categorized as primary and secondary channels (C & S); longitudinal bar (L); transverse bar (T); side bar (SB); unvegetated bank (EK); dry channel (ED); island (VI); and water depression (WD). All types of GUs were observed over the four distinct annual seasons, except ED, which was absent during the high flow, monsoon season. Seasonal variation of the surface area of GUs and discharge showed an inverse relation between discharge and exposed surface areas of VI, L, T, and SB. Nutrients mainly enter the river system through water and sediments, and during monsoon, the maximum portion of emergent GUs were submerged. Based on the assumption that nutrient retention is enhanced in the seasonally inundated portions of GUs, nutrient retention-/export-relevant geomorphic units (NREGUs) were identified. Seasonal variation in the area of NREGUs was similar to that of GUs. The mean NDVI values of the main identified NREGUs were different. The variation of NDVI values among seasons in these NREGUs resulted from changes of vegetation cover and type. The variation also occurred due to alteration of the surface area of GUs in different seasons. The changes of vegetation cover indicated by NDVI values across seasons are likely important drivers for biogeochemical and ecological processes.

The authors tried to present the use of dredged sediments to recover expected plant communities by comparing wild sites. However, I found this comparison might not be effective due to the gap in the time scale between the reference and the constructe...

Coastal waters in Bangladesh face growing pressure from human activities and climate change, yet tools to assess ecosystem health remain scarce. In the present study, a Chlorophyll-based Productivity Status Index (CPSI) was developed by log-normalising Chlorophyll-a concentrations from 71 stations across three coastal areas of Bangladesh (Cox's Bazar, Saint Martin's Island, and Kuakata) and integrating it with a salinity-based typology. CPSI values ranged from 0 to 100 (mean = 47.05 ± 19.35). Validation against phytoplankton density showed a strong positive relationship (Pearson r = 0.59, p < 0.001), confirming that CPSI reliably tracks phytoplankton biomass. Four typologies emerged: small river-dominated estuaries (SRDE; CPSI = 39.70 ± 13.20), estuaries with extensive intertidal flats (EEIF; CPSI = 51.99 ± 19.56), fully marine exposed sandy coasts (FMESC; CPSI = 53.93 ± 19.93), and fully marine exposed rocky coasts (FMERC; CPSI = 35.11 ± 12.86). A combined model of Environmental Quality Index (EQI) and coastal typology explained 23% of the variance in CPSI (R² = 0.23, F (4, 66) = 5.13, p = 0.001). FMESC had significantly higher CPSI than FMERC (p = 0.002), while EEIF and SRDE occupied intermediate positions, showing that typology adds ecological information beyond physico-chemical quality. Productivity levels followed the order: FMESC > EEIF > SRDE > FMERC. The CPSI-typology framework offers a preliminary method to standardise Chlorophyll-a observations and investigate type-specific productivity patterns. This study highlights the importance of combining biological indices with physical typologies for coastal water quality assessment, especially in data-limited regions.