Climate · ML · Earth Systems

Ronnice
Chepkoech

Climate–ML researcher & earth systems modeller with 7+ years developing open-source platforms that couple machine learning with land, energy, water, and agricultural systems across Eastern Africa.

7+
Years Research
140+
Trained
2
First-Author Pubs
SFU
Canada Fellowship
🌍
GeoCLEWs-Agro
CLEWs
ML Models
MODFLOW
CMIP Data

About Me

I am a climate–ML researcher and environmental engineer based in Kenya, specialising in the intersection of machine learning and earth system science. My work centres on developing ML-integrated modelling platforms that support climate-resilient policymaking across Eastern Africa.

I created GeoCLEWs-Agro during a 6-month visiting research fellowship at Simon Fraser University (SFU), Canada (Dec 2023 – Jun 2024), working within the DeltaE+ Research Group and funded by Global Affairs Canada. The platform integrates ML-augmented CLEWs (Climate, Land, Energy, and Water Systems) modelling — directly addressing parameterization emulation, climate projection, bias correction, and climate impact modelling challenges.

My research spans earth system model outputs, observational records, and applied ML for scenario analysis and resource planning. I hold an MSc in Environmental Engineering from Taita Taveta University and have co-authored peer-reviewed research published in Springer Nature journals.

Beyond research, I have delivered capacity-building training to 140+ students, professionals, and policymakers and represented my institutions at 6+ regional and international scientific forums.

CLEWs Modelling ML for Climate Parameterization Emulation WEAP / MODFLOW Python / R Open-Source Dev Eastern Africa Policy Engagement
ML & Programming
Python
R / SPSS
ML Modelling
Java
Climate & Earth Systems
CLEWs / OSeMOSYS
WEAP
MODFLOW / MT3DMS
HEC-RAS / HEC-HMS
GIS & Visualisation
ArcGIS / QGIS
Power BI / Tableau

Key Projects

Research Leadership
Climate Resilience Research @ CESMECC

Led 3+ interdisciplinary research projects on climate resilience and sustainable mining at CESMECC, producing peer-reviewed and policy-relevant outputs. Applied CLEWs-based ML modelling frameworks for stakeholder-facing policymaking support.

Extended parameterization and scenario analysis capabilities for mining–climate nexus.
Built partnerships with 5+ institutions; represented at 6 regional and international scientific forums.
CLEWs Scenario Analysis Policy Research 2023
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Predictive Modelling
Mine Contaminant Transport Modelling, Mlolongo

Investigated water quality and modelled contaminant transport from an abandoned mine to surrounding aquifers using MODFLOW and MT3DMS. Predicted spatial distribution and migration of contaminants over time, informing groundwater protection strategy.

Predictive modelling showed 5.1 km radius vulnerability zone for shallow aquifers.
Published as first-author paper in Discover Water (Springer Nature, 2025).
MODFLOW MT3DMS Groundwater Published 2025
Read Paper
Regulatory & Field Work
Environmental Impact Assessments @ NEMA

As EIA Officer at Kenya's National Environment Management Authority, reviewed 15+ EIA reports, facilitated 10+ public consultations, and conducted 15+ field inspections — building a rigorous foundation in quantitative environmental assessment.

Technical analysis of environmental and climate impacts across diverse project types.
Streamlined EIA documentation processes for improved regulatory compliance.
EIA Review Field Inspection Kenya 2018–2019

Publications

01
Discover Water · Springer Nature · 2025
Assessment of the Transportation of Abandoned Mine Water Contaminants to the Surrounding Aquifers: A Case of Mlolongo Open-Pit Lake, Kenya
Ronnice Chepkoech, Bernard Ouma Alunda, Antony Mutua Nzioka · Vol. 5, Issue 1, Art. 94 · DOI: 10.1007/s43832-025-00278-y
Investigated water quality and modelled contaminant transport from an abandoned mine site to surrounding aquifers using MODFLOW and MT3DMS codes. Identified multiple contaminants exceeding WHO threshold limits and predicted spatial distribution and migration patterns — showing vulnerability of shallow aquifers within a ~5.1 km radius. Open Access (CC BY-NC-ND 4.0).
First Author Open Access MODFLOW
View Paper
02
Journal of Water and Environmental Sustainability (WES) · 2024
First-Author Publication — Water & Environmental Sustainability
Ronnice Chepkoech et al. · Article ID: 218323 · journalwes.com
Peer-reviewed research published in the Journal of Water and Environmental Sustainability, addressing climate–water–environment interactions. Full details available via the journal link.
First Author Peer Reviewed
View Paper

Training & Capacity Building

🎓
140+
Participants trained
CLEWs & Climate ML Workshops
Delivered hands-on CLEWs modelling training to students, professionals, and policymakers across Kenya. Designed and updated toolkits aligned with the latest ML-for-climate research advances.
🏛️
6+
Forums represented
Regional & International Engagement
Represented AECSC and CESMECC at regional and international scientific forums, raising institutional visibility and forging partnerships with 5+ academic and government institutions.
📋
6+
Proposals drafted
Grant Writing & Reporting
Drafted and submitted 6+ grant proposals; authored 7+ technical reports and policy documents for donor organizations and government stakeholders on climate–energy–water nexus topics.
🔬
3+
Research projects led
Research Leadership @ CESMECC
Led multi-disciplinary research on climate resilience and sustainable resource management, producing peer-reviewed and policy-relevant insights for government and international stakeholders.
🌿
15+
EIA reports reviewed
Environmental Assessment @ NEMA
Reviewed EIA reports, conducted field inspections, and facilitated public consultations across Kenya — developing deep expertise in regulatory environmental and climate impact assessment.
💻
1
Open-source platform
GeoCLEWs-Agro on GitHub
Developed and maintains GeoCLEWs-Agro as a publicly available open-source ML-integrated earth systems platform — demonstrating reproducible methodology and commitment to open science.
Open for Climate–ML
Project Collaboration

Available for remote, project-based work in climate modelling, ML applied to earth system science, parameterization emulation, bias correction, and climate impact assessment. Let's connect.

Send Email
📞
+254 704 693 141
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Nairobi, Kenya · Remote-ready