Evaluating water availability and flow characteristics for Dikrong river in Arunachal Pradesh using Acoustic Doppler Current Profiler

被引:0
|
作者
Goswami, Ghritartha [1 ]
Darang, Joon [1 ]
Prasad, Ram Kailash [1 ]
Mandal, Sameer [2 ]
机构
[1] North Eastern Reg Inst Sci & Technol, Dept Civil Engn, Nirjuli, Arunachal Prade, India
[2] North Eastern Reg Inst Sci & Technol, Dept Agr Engn, Nirjuli, Arunachal Prade, India
关键词
ADCP; Discharge; Erosion prone zone; LULC; Velocity; Water availability; ADCP; DISCHARGE; STREAMFLOW; VELOCITY;
D O I
10.1007/s40899-024-01082-7
中图分类号
TV21 [水资源调查与水利规划];
学科分类号
081501 ;
摘要
The Dikrong river, which flows through Arunachal Pradesh and Assam, holds vital importance for local communities. However, a comprehensive study on this river has been lacking for this river system. To bridge this gap, an extensive investigation for sustainable regional development and effective water resource planning in the Dikrong river basin was carried out based on Acoustic Doppler Current Profiler (ADCP), historical data analysis and Land Use/Land Cover (LULC) change analysis. The study highlights the effectiveness of ADCP's in accurately assessing various river parameters, including bathymetry mapping, current profiling, discharge measurements, and three-dimensional velocity profiling. The results indicate diverse flow patterns at different locations during low river discharge, highlighting the importance of understanding these patterns for efficient river ecosystem management. By utilizing ADCP data, the flow patterns and discharge characteristics are investigated across various transects that reveal a distinct discharge surge, particularly in the mid-section. However, varied flow velocities between river banks and the mid-section highlight the river's intricate behaviour. The historical flow patterns were examined to get insights into flow characteristics. Although historical data indicated a downward discharge trend for monthly peak flood, this is possibly due to climate change and land use alterations. Further, employing machine learning techniques (Support Vector Machine, Google Earth Engine), the study assesses LULC changes, revealing declining forest cover and increasing settlements and agriculture/barren land. Finally, it presents a comprehensive analysis of flow dynamics, water availability, erosion-prone zones, and high-risk areas along the river.
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页数:21
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