Merowe Dam Water Quality Monitoring

Multi-Parameter Satellite-Based Analysis Using Sentinel-2 & Google Earth Engine

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A comprehensive remote sensing pipeline for monitoring water quality dynamics across Merowe Dam reservoir (925.7 km²), Sudan, using Sentinel-2 multispectral imagery (2023-2024) processed through Google Earth Engine and XArray.

Features | Parameters | Results | Getting Started | Methodology | Citation

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Study Area

Merowe Dam is a large hydroelectric dam on the Nile River in northern Sudan. The reservoir extends over 925.7 km² and is a critical water resource for the region. This project monitors its water quality using freely available satellite data.

Property	Value
Location	Northern Sudan (18.4°N - 19.3°N, 31.7°E - 32.8°E)
Reservoir Area	925.7 km²
Satellite	Sentinel-2 SR Harmonized (10-20m resolution)
Time Period	January 2023 - December 2024
Images Processed	510 cloud-filtered scenes

Features

• Automated cloud masking using cloud probability + SCL band dual-filtering
• JRC Global Surface Water boundary for precise water body delineation
• 7 water quality parameters derived from published empirical algorithms
• Monthly spatiotemporal maps at 10m resolution
• Time series analysis with Mann-Kendall trend testing
• Trophic State Index classification (Carlson TSI)
• Algal bloom detection and frequency mapping
• Composite Water Quality Index (weighted multi-parameter)
• Upstream vs downstream spatial gradient analysis
• Seasonal variability and inter-parameter correlation analysis

Water Quality Parameters

Parameter	Algorithm	Reference
Chlorophyll-a	Red-Edge ratio (B5/B4)	Empirical band ratio
Chlorophyll-a (NDCI)	Quadratic NDCI model	Mishra & Mishra (2012)
NDCI	(B5-B4)/(B5+B4)	Normalized Difference Chlorophyll Index
Turbidity (FNU)	Red band reflectance model	Dogliotti et al. (2015)
TSS	Linear red band model	Empirical
CDOM	Blue/Green ratio exponential	Brezonik et al. (2015)
Secchi Depth	Blue/Green log-ratio	Empirical
FAI	NIR baseline difference	Hu (2009)

Results

Monthly Chlorophyll-a Maps

Monthly Turbidity Maps

Monthly Total Suspended Solids

Monthly CDOM

Monthly Secchi Depth

Multi-Parameter Time Series

Inter-Parameter Correlation

Seasonal Box Plots

Chlorophyll-a Anomaly from Long-Term Mean

Trophic State Index (Carlson TSI)

Algal Bloom Frequency Map

Monthly Water Quality Index

Monthly Chlorophyll-a Heatmap

Key Findings

• Trophic State: The reservoir is classified as eutrophic (99.4% of observations), with occasional hypereutrophic episodes (0.6%)
• Chlorophyll-a Trend: Statistically significant increasing trend (Mann-Kendall p=0.04, Sen's slope = +0.0034/timestep)
• Seasonal Pattern: Highest Chl-a in summer (JJA), peaking in July (~111 ug/L); lowest in winter (DJF, ~98 ug/L)
• Bloom Hotspots: Algal bloom frequency is highest near the dam wall (southwest) and upstream inflow areas
• Strong Correlations: Chl-a and TSS (R=0.74), Chl-a and NDCI (R=0.85), CDOM and Secchi Depth (R=1.00)

Getting Started

Prerequisites

• Python 3.8+
• A Google Earth Engine account
• A GEE cloud project (for high-volume API access)

Installation

# Clone the repository
git clone https://github.com/Osman-Geomatics93/Merowe-Dam-Water-Quality.git
cd Merowe-Dam-Water-Quality

# Install dependencies
pip install -r requirements.txt

Authentication

import ee
ee.Authenticate()
ee.Initialize(project="your-project-id", opt_url="https://earthengine-highvolume.googleapis.com")

Run

Open Merowe_Dam_Water_Quality_v3.ipynb in Jupyter Notebook or Google Colab and run all cells sequentially.

Methodology

Sentinel-2 SR Harmonized (L2A)
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        v
Cloud Masking (Cloud Prob + SCL)
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        v
JRC Water Boundary Clipping
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        v
Water Quality Algorithms (7 params)
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        v
XArray Dataset (510 timesteps)
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        +---> Monthly Spatial Maps
        +---> Time Series Analysis
        +---> Mann-Kendall Trend Test
        +---> Trophic State Index (TSI)
        +---> Algal Bloom Detection
        +---> Water Quality Index (WQI)
        +---> Upstream vs Downstream Gradient
        +---> Seasonal & Correlation Analysis

Tech Stack

Tool	Purpose
Google Earth Engine	Cloud-based satellite image processing
XArray + xee	N-dimensional labeled array operations
Matplotlib + Seaborn	Scientific visualization
Pandas	Tabular data analysis
Folium	Interactive map preview
pyMannKendall	Non-parametric trend testing

Project Structure

Merowe-Dam-Water-Quality/
|-- Merowe_Dam_Water_Quality_v3.ipynb   # Main analysis notebook
|-- Images/                              # Output visualizations
|   |-- 1.png  ... 13.png
|-- requirements.txt                     # Python dependencies
|-- LICENSE                              # MIT License
|-- README.md                            # This file

Citation

If you use this work in your research, please cite:

@software{merowe_water_quality_2025,
  author    = {Osman},
  title     = {Multi-Parameter Water Quality Monitoring of Merowe Dam Using Sentinel-2 and Google Earth Engine},
  year      = {2025},
  url       = {https://github.com/Osman-Geomatics93/Merowe-Dam-Water-Quality}
}

Algorithm References

• Mishra, S. & Mishra, D.R. (2012). Normalized difference chlorophyll index. Remote Sensing of Environment.
• Dogliotti, A.I. et al. (2015). A single band algorithm for turbidity retrieval. Remote Sensing of Environment.
• Brezonik, P. et al. (2015). Factors affecting CDOM in lakes. Limnology and Oceanography.
• Hu, C. (2009). A novel ocean color index to detect floating algae. Remote Sensing of Environment.
• Carlson, R.E. (1977). A trophic state index for lakes. Limnology and Oceanography.

License

This project is licensed under the MIT License - see the LICENSE file for details.

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Built with Google Earth Engine and Python