Integrating Sediment Dynamics into Flood Risk Modeling in Koshi River Basin

A machine learning-based approach to improve flood risk modeling to overcome static parameters and traditional GIS approaches and integrate sediment dynamics.

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**Ongoing project : Spring 2025 - Spring 2026

The Gap

The Koshi River Basin spans Nepal and northern India, and experiences recurring catastrophic floods that cause annual damages of approximately $14 million USD while impacting millions of lives. Current flood risk assessment methods in this region rely primarily on static parameters such as elevation, slope, drainage, and density which are analyzed through traditional GIS approaches. While these methods are valuable they fail to account for a crucial dynamic variable that fundamentally alters flood behavior: The Koshi River's immense sediment load i.e almost 12 times that of the Amazon River[2].

This underscores the urgent need for sediment-aware models.[3].

Key Challenges

Traditional models' neglect of hydromorphological feedbacks
Stochastic nature of sediment deposition patterns
Computational complexity in modeling non-linear sediment-flow interactions
Lack of high-resolution DEM data for accurate channel mapping
Integration of multi-temporal sediment flux measurements

Framework Development

Develop an integrated methodological framework that leverages Sentinel-2 multispectral imagery to quantify sediment dynamics and incorporate this data into flood risk models. Extract sediment concentration patterns using spectral indices and band ratio transformations to create sediment-adjusted digital elevation models that better represent actual flood behavior in the Koshi Basin[4].

ML Algorithm Evaluation

Implement machine learning algorithms (Random Forest, XGBoost, SVMs) to establish reliable relationships between Sentinel-2 spectral signatures and sediment concentrations using the Normalized Difference Suspended Sediment Index (NDSSI). Apply these sediment-aware models to improve flood prediction accuracy by accounting for continuous channel morphology changes in high-sediment environments.

Quantitative Assessment

Evaluate flood prediction improvements gained through sediment-integrated modeling using cross-validation techniques with F1-score, precision, recall, and AUC metrics. Quantify how the inclusion of Sentinel-2 derived sediment data enhances model performance compared to traditional GIS-based flood risk approaches that overlook sediment dynamics.

Technical Methodology

Data Collection & Preprocessing

Process multi-temporal Sentinel-2 imagery to extract suspended sediment concentrations across the Koshi River Basin. Apply atmospheric correction cloud masking to ensure data quality. Calculate NDSSI using band ratios B4/B3 and B4/B2, while integrating traditional flood modeling inputs including SRTM and Copernicus DEMs (30m) processed through ArcGIS Hydrology tools[5].

Hydrodynamic Modeling

Integrate derived sediment data into hydrodynamic modeling frameworks to account for sediment-induced changes in channel morphology and water conveyance capacity. Configure simplified models with the Muskingum-Cunge routing method while incorporating sediment transport parameters to simulate the unique "super-elevated" channels characteristic of the Koshi system as identified in ICIMOD research.

Machine Learning Implementation

Develop a dual-approach machine learning system that: (1) accurately extracts sediment concentrations from Sentinel-2 spectral signatures through regression models, and (2) integrates these sediment inputs with hydrological variables to improve flood prediction. Engineer features combining satellite-derived sediment data with topographic and hydrologic parameters to create comprehensive flood risk models that account for dynamic channel changes[6].

Expected Outcomes

Sediment-Aware Flood Model

A comprehensive model adaptable to other Himalayan river systems, leveraging advanced spectral indices for sediment quantification.

Python Geospatial Library

Open-source toolset with sediment-adjusted modeling methodology for researchers and emergency management agencies.

Interactive Visualization

Web-based interface with Leaflet.js implementation allowing users to visualize how sediment accumulation modifies flood behavior.

Enhanced Prediction Accuracy

Quantifiable improvement in flood prediction metrics, reducing false negatives in early warning systems.