Land (Mar 2023)

Shifting Sands: Assessing Bankline Shift Using an Automated Approach in the Jia Bharali River, India

  • Jatan Debnath,
  • Dhrubajyoti Sahariah,
  • Anup Saikia,
  • Gowhar Meraj,
  • Nityaranjan Nath,
  • Durlov Lahon,
  • Wajahat Annayat,
  • Pankaj Kumar,
  • Kesar Chand,
  • Suraj Kumar Singh,
  • Shruti Kanga

DOI
https://doi.org/10.3390/land12030703
Journal volume & issue
Vol. 12, no. 3
p. 703

Abstract

Read online

Bank erosion hazard is a frequent occurrence that poses threats to floodplain ecosystems. This analysis examined changes to the Jia Bharali River channel in India using the GIS-based Digital Shoreline Analysis System [DSAS]. The Jia Bharali’s future channel was predicted so as to identify the most erosion-susceptible zones. The rate of bankline movement was calculated using remotely sensed data collected over a period of 45 years (1976–2021). The results show that the river’s erosion and deposition rates were higher in the early years than towards the later part of the period under analysis. On the right and left banks of the river, the average shift rate was −9.22 and 5.8 m/y, respectively, which is comparatively high. The chosen portion of the river was evenly divided into three zones, A, B, and C. The most positively affected zone was zone A. The left bank of zone B exhibited a higher rate of erosion than the right bank, indicating that the river was moving to the left [eastward] in this zone. At the same time, the right bank was being eroded faster than the left, indicating a westward thrust at zone C. The predicted result demonstrates that the left bank of zone B and the right bank of zone C would have a higher average migration rate. Therefore, these banks were identified as being the most susceptible to bank erosion. The study evaluates the spatio-temporal change of the river in sensitive regions where neighboring settlements and infrastructure were at risk of changing channel dynamics. Using the actual and forecasted bankline, the degree of accuracy was confirmed. The results of the automated prediction approach could be useful for river hazard management in the Jia Bharali and in similar environmental settings with tropical high precipitation zones.

Keywords