🌍 Environmental Modelling and Analysis
— GIS, Regression & Cokriging Interpolation, ETo Modelling, and Flood Simulation

This project presents a complete environmental data modelling workflow built from scratch using QGIS, Flood Modeller, and Python-based statistical analysis.
It integrates spatial interpolation, ETo estimation, and flood simulation to model rainfall, water availability, and flood risk across diverse environments.

Developed independently for the final project of EART62012: Environmental Monitoring and Modelling (University of Manchester, 2023), this work demonstrates the ability to translate complex geospatial and hydrological data into clear, reproducible analytical insights.

🧭 Project Summary & Key Highlights

1. Climate & Hydrological Modelling (Ethiopia)

• Predicted spatial rainfall and evapotranspiration (ETo) across the Rift Valley Lakes Basin using Regression, MQUAD, and Cokriging interpolation in QGIS.
• Compared model outputs to evaluate interpolation accuracy and correlation between rainfall and elevation (DEM).
• Visualised spatial variability and discussed implications for climate change and agricultural water management.

2. UK Hydrological Analysis

• Analysed river gauge data to model peak flows and flood frequency using the ReFH and FEH rainfall–runoff methods.
• Constructed rating curves to estimate discharge from continuous water-level records, identifying data uncertainty and model limitations.

3. Hydraulic River Modelling

• Built 1D and 2D flood simulations in Flood Modeller to assess river cross-sections, floodplain storage, and embankment elevations.
• Compared model outputs (Upton_001 vs Upton_002) and demonstrated how extending floodplain geometry improves model realism.

4. Climate Hydrology & Irrigation

• Computed ETo sensitivity to key variables (latitude, elevation, wind speed, humidity).
• Modelled seasonal irrigation demand, explaining how precipitation and temperature patterns drive water requirements.

5. Glacier Hydrology (Passu Glacier)

• Developed a degree-day melt model to simulate glacier runoff under varying DDF ratios, lapse rates, and snowline elevations.
• Introduced subglacial drainage dynamics and analysed their influence on discharge timing and magnitude.

6. Overall Impact

This project integrates data from multiple domains—climate, hydrology, topography, and glaciology—into a unified analytical workflow.
It demonstrates practical expertise in spatial interpolation, model calibration, parameter sensitivity analysis, and technical scientific reporting.

🧰 Tools & Skills

• GIS & Remote Sensing: QGIS, DEM processing, spatial interpolation (Regression, MQUAD, Cokriging)
• Hydrological & Hydraulic Modelling: Flood Modeller, ReFH / FEH rainfall–runoff, flood frequency analysis
• Climate & Glacier Analysis: ETo modelling, degree-day method, sensitivity and parameter testing
• Programming & Data Analysis: Python (Pandas, Matplotlib), Excel, data validation, correlation studies
• Scientific Reporting: Structured documentation, visualisation, and interpretation of environmental models

📄 File

ID11155827_PracticalBook_EART62012.pdf — Full report with all figures, tables, equations, and references.

🧠 Learning Outcome

Through this project, I developed the ability to:

• Integrate geospatial, hydrological, and climatic data into cohesive analytical workflows
• Apply statistical and spatial modelling techniques for real-world environmental prediction
• Communicate complex analytical results through professional documentation and data storytelling