Geomorphosite assessment and prioritization across diverse landscapes of West Bengal India using expert and visitor modified geosite assessment model

West Bengal has a unique diversity of geomorphological landscapes ranging from the Eastern Himalaya and Sub-Himalayan piedmont to plateau, fluvial, deltaic and coastal environments. However, comparative and spatially explicit assessment of geosites across these contrasting landscapes is limited. The present study employs the Modified Geosite Assessment Model (M-GAM) to assess and rank 42 representative geosites in the major geomorphic provinces of West Bengal. The evaluation comprises expert assessment of 27 standardised sub-indicators and visitor opinions collected through a structured survey of 202 respondents. The expert-based assessment incorporated visitor-derived importance factors (Im) to reflect the perceived importance of individual attributes. The visitor instrument demonstrated acceptable internal consistency for the Main Value (MV; Cronbach’s α = 0.79) and Additional Value (AV; Cronbach’s α = 0.84) subscales. The results show high variability among geosites in scientific/educational, scenic/aesthetic, protection, functional and touristic attributes. The mean AV score (11.90) was higher than the mean MV score (8.53), indicating the importance of functional and tourism-supporting attributes in the relative M-GAM scores. The 42 geosites were ranked into relatively high-13, moderate-18 and low-11 priority groups using quartile-based thresholds within the study inventory. Correlation analysis revealed positive correlations between some of the M-GAM components, for example, between accessibility and number of visitors ( r = 0.74). The VTr – AV correlation ( r = 0.81) is treated with caution since VTr is a component of AV itself. Further, a comparison of expert and visitor assessments identifies sites where geomorphological significance and visitor perceived importance are convergent or divergent. Overall, the study demonstrates the value of M-GAM as a comparative decision support framework for geosite prioritization across contrasting landscapes, whilst acknowledging the uncertainties associated with visitor sampling, expert scoring, weighting, aggregation and relative classification.