Integrated remote sensing and machine learning tools for estimating ecological flow regimes in tropical river reaches
Debi Prasad Sahoo1, Bhabagrahi Sahoo2, Manoj Kumar Tiwari3
1Research Scholar, School of Water Resources, Indian Institute of Technology Kharagpur, West Bengal-721302, India.
Journal of Environmental Management
|September 7, 2022
Summary
Remote sensing offers a new way to estimate river ecological flow regimes, especially where streamflow data is scarce. This study developed fusion models using satellite data and machine learning, showing strong potential for managing aquatic environments globally.
Area of Science:
- Hydrology and Remote Sensing
- Environmental Management
Background:
- Declining streamflow gauging stations necessitate advanced hydrometry techniques.
- Remote sensing (RS) is crucial for estimating ecological flow regimes (EFR) in data-scarce river basins.
- Limitations exist in RS for narrow tropical rivers due to data resolution.
Purpose of the Study:
- To propose a novel framework integrating enhanced spatiotemporal adaptive reflectance fusion (FUS) with machine learning for daily streamflow estimation.
- To address limitations of spatial and temporal resolution in satellite data for EFR in tropical rivers.
- To evaluate Artificial Neural Network (ANN), Random Forest Regression (RFR), and Support Vector Regression (SVR) based fusion models (ANNFUS, RFRFUS, SVRFUS).
Main Methods:
- Integration of Aqua-MODIS (250m x 1-day) and Landsat (30m x 1-day) satellite data in the near-infrared region.
- Application of enhanced spatiotemporal adaptive reflectance fusion (FUS) technique.
- Machine learning algorithms: Artificial Neural Network (ANN), Random Forest Regression (RFR), and Support Vector Regression (SVR).
Main Results:
- All developed models (ANNFUS, RFRFUS, SVRFUS) successfully simulated daily streamflow with Nash-Sutcliffe Efficiency (NSE) > 0.8 and Kling-Gupta Efficiency (KGE) > 0.8.
- Models achieved relative root mean square error (rRMSE) between 0.051-0.12 and normalized RMSE between 0.23-0.36.
- The Support Vector Regression-based fusion model (SVRFUS) demonstrated superior performance in reproducing high, median, and low streamflow regimes (NSE > 0.85, KGE > 0.8).
Conclusions:
- The proposed RS-based framework effectively estimates daily streamflow and ecological flow regimes, even with limited gauging data.
- The SVRFUS model shows significant potential for accurate streamflow simulation across different flow conditions.
- This approach is replicable for global river basins, aiding aquatic environmental management at defunct gauging stations.
Related Concept Videos
01:24Rapidly Varying Flow
124
Rapidly varying flow (RVF) in open channels is characterized by abrupt changes in flow depth over a short distance, with the rate of depth change relative to distance often approaching unity. These flows are inherently complex due to their transient and multi-dimensional nature, making exact analysis difficult. However, approximate solutions using simplified models provide valuable insights into their behavior.Key Features of Rapidly Varying FlowRVF is commonly observed in scenarios involving...
124
01:30Typical Model Studies
427
Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
427
01:29Gradually Varying Flow
107
Gradually varying flow (GVF) in open channels describes situations where water depth changes slowly along the channel due to factors like non-uniform bed slope, channel shape variations, or obstructions. This flow type occurs when the depth adjusts gradually to balance gravitational forces, shear forces, and energy requirements, resulting in a low rate of depth change.Characteristics of Gradually Varying FlowGVF is commonly observed in natural streams, rivers, and canals, where flow depth...
107
01:17Design Example: Analyzing Capacity Contours for Flood Risk Assessment
91
Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
91
01:18Uniform Depth Channel Flow: Problem Solving
121
To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
121
01:27Uniform Depth Channel Flow
131
Uniform depth channel flow keeps fluid depth consistent along channels such as irrigation canals. In natural channels, such as rivers, approximate uniform flow is often assumed. This condition occurs when the channel’s bottom slope matches the energy slope, balancing potential energy lost from gravity with head loss due to shear stress. This balance prevents depth changes along the channel length, resulting in a steady, uniform flow.Uniform flow in open channels with a constant cross-section...
131