​ Zeinab Abu Romman, PhD
Environmental & Water Resources Engineer
Zeinab Abu Romman, PhD
Integrated Water Resources Modeling Engineer | Python & GIS Automation for Surface & Subsurface Systems | Sustainable Water Management
Welcome to my portfolio.
As a specialized Climate Impact Hydro-Modeler, Integrated Water-Resource Analyst and Automation Expert, I deliver end-to-end solutions that combine advanced numerical, spatiostatistical analysis with automated Python and GIS workflows to tackle surface and subsurface water challenges. My portfolio showcases interactive dashboards visualizing real-time allocation, drought and water-quality metrics, predictive modeling tools for climate-impact scenarios and customizable data-ingestion applications that accelerate project timelines. Each project reflects my ability to integrate complex hydrological and hydrogeological models with Power BI reporting tools, empowering stakeholders to make informed decisions quickly. By orchestrating cross-functional collaborations and leveraging cloud-based data pipelines, I ensure seamless deployment and scalability of water resources solutions. My work demonstrates a commitment to sustainable water management through data-driven insights and resilience-focused modeling.
Feel free to reach out to explore opportunities together!
Research Interests
I’m passionate about advancing environmental and water-resources engineering by integrating sustainability, geospatial technology, climate science and policy analysis. My work tackles complex challenges—from land restoration and carbon management to data-driven water-policy evaluation—through physics-based modelling, big-data pipelines and spatial statistics.
Land Restoration & Water-Cycle Integration
Developing 3-D reclamation models under Canadian climatic scenarios—combining rainfall, evapotranspiration and soil-erosion tools to quantify restoration success and groundwater recharge. These insights guide resilient land-restoration designs and support adaptive management in post-mining landscapes.
Hydrological Data Imputation & Uncertainty Analysis
Applying spatial-statistical and machine-learning methods to fill gaps in Canadian streamflow and precipitation records. By enhancing data completeness and quantifying uncertainty, I strengthen the reliability of hydrologic and groundwater models in data-sparse regions.
Water-Policy Performance Evaluation
Integrating license-allocation datasets, groundwater trends and climate projections with spatial statistics to assess policy effectiveness. My recommendations for adaptive regulatory frameworks include advanced monitoring networks and stakeholder-driven licensing criteria to bolster watershed health and groundwater sustainability.
Automated Big-Data Pipelines & Dashboards
Building end-to-end Python/R and FME workflows for real-time ingestion, processing and visualization of hydrologic and emissions data. Interactive dashboards empower decision-makers with actionable insights, accelerating evidence-based policy development and resource-management solutions.