An automated approach in estimation and prediction of riverbank shifting for flood-prone middle-lower course of the Subarnarekha river, India. Issue 3 (3rd July 2021)

• Record Type: Journal Article
• Authors: Jana, Subrata
• Is Part Of: International journal of river basin management. Volume 19:Issue 3(2021)
• Page Start: 359
• Page End: 377
• Publication Date: 2021-07-03
• Subjects: Digital shoreline analysis system -- intensive sand mining -- riverbank shifting rate -- predicted riverbank position -- erosion prevention action plan; Watershed management -- Periodicals; Water resources development -- Periodicals; Hydraulic engineering -- Periodicals; Watershed hydrology -- Periodicals; Water resources development; Watershed management; Watersheds; Periodicals; 551.483
• DOI: 10.1080/15715124.2019.1695259
• Languages: English
• ISSNs: 1814-2060

Abstract

ABSTRACT: The riverbank/bankline shifting is mainly caused by the monsoonal hydrodynamic behaviour and intensive sand mining from the riverbed in the middle-lower course of Subarnarekha River. The flood-induced channel migration triggers other natural and socio-economic hazards related to flood hazard, loss of riparian arable land and crop damage, compensation of customary settlements and casualty. Therefore, the estimation of spatio-temporal diversities of riverbank shifting and future prediction is essential concerning the sustainability of riparian dwellers. Despite the different approaches and techniques, the estimation and future prediction of riverbank shifting is yet difficult. To pursue a more generalized approach, this study seeks to apply the digital shoreline analysis system supported statistical models to estimate the riverbank shifting and future prediction considering the six different multi-temporal banklines. This method is effortless and non-tedious for swift and precise estimation of the riverbank shifting without critical steps in data gathering and mathematical treatment. The overall result divulges that in the vicinity of meandering bend, river course undergoes a higher rate of shifting. The model-derived positional error is high (0.121 m) in the estuary section and less (0.002 m) in the upper section with an overall mean error of 0.02 m. The short-term predicted position (2020) and recent actual position (2018) of banklines are well harmonized throughout the river course with only ∼0.05 m positional error. Consequently, this adopted automated approach is well suited for estimating the spatio-temporal variability of riverbank shifting and short-term prediction to take erosion prevention action plans on an immediate basis.