Entropy based approach for precipitation monitoring network in Bihar, India

Abstract

Study region: Bihar State, located in India’s eastern region, displays significant spatial and temporal variation in rainfall during the Indian Summer Monsoon period with subsequent flooding problems. Study focus: Recent severe flooding problems highlight the need for improved spatial precipitation monitoring to enable effective flood management and reduce water-related disasters. To address this challenge, we employed Shannon entropy theory to assess the spatial distribution of precipitation and identify critical areas for rain gauge network improvements. We used Principal of Maximum Entropy (POME) to compute entropy measures and Value of Monitoring (VOM) with Thiessen polygons, and Adjacent Station Groups (ASGs). New hydrological insights for the region: The results showed that the Marginal Entropy (ME) values lie between 0.039 and 0.048. The maximum values of ME are in the northeast area of the study region, exhibiting larger complexity and variability in the environmental conditions typical for northeast Bihar. The VOM was in the range of − 1 to + 1 suggesting strategic placement of additional 12 rain gauge stations to improve the existing monitoring network. The new locations were in the south mountainous area, the east, and the northwest, enhancing network coverage and addressing spatial and temporal precipitation variability. These findings support the design of a more effective monitoring network and have significant implications in hydrological modelling, flood prediction, and water resources management.

Keywords

• Climate change studies
• Entropy-based approach
• Flood forecasting
• Network expansion
• Redundancy
• Shannon entropy theory