This interview presents Mr. Kanito’s journey and passion for advancing sustainable water management under the twin pressures of climate change and human activity. He emphasizes the power of integrating observation data, space technologies, and machine learning for groundwater modelling. His vision is to equip communities with the knowledge and tools to manage water resources sustainably, ensuring a healthier, more resilient future for all.
How do you personally and professionally relate to water and/or space technologies?
Growing up in Ethiopia, where mainly rural communities often struggle with access to clean and reliable water, I developed a deep appreciation for the value of this vital resource. This early experience shaped both, my personal and professional path, leading me to study natural resources management. Later, it has been the central theme of my academic and professional journey, shaping my work on groundwater modelling and climate resilience to sustainable resource management. Over time, I became fascinated by how space technologies can help us bridge information gaps that often limit water resource planning in developing regions. For me, applying space-based tools to monitor and manage water systems isn’t just a scientific pursuit, it’s a personal mission to make data and technology accessible for communities that need them most.
Can you tell us about your current position as a Doctoral Researcher at King Fahd University of Petroleum and Minerals (KFUPM)?
As a PhD student at King Fahd University of Petroleum and Minerals (KFUPM), my work focuses on groundwater modelling under climate change conditions, with a particular interest in uncertainty analysis and sustainable water management. I use satellite data, physics-informed machine learning, and numerical modeling tools to better understand how climate variability impacts groundwater resources. My goal is to develop data-driven models that can support long-term water planning and policy decisions. Being at KFUPM allows me to work at the intersection of advanced science and technology to address a real-world environmental challenge.
What first motivated you to pursue geology, particularly combining it with remote sensing, GIS and machine learning?
The roots of my motivation to pursue geology stem from a deep curiosity about the Earth's subsurface and a desire to understand the vital resources it holds, especially groundwater. As I advanced in my studies, I realised that many traditional approaches were not enough to address the complex and changing nature of groundwater in environments like Ethiopia’s, where reliable data can be hard to obtain. This realization led me to embrace remote sensing and GIS technologies, which offer powerful ways to observe and analyse environmental changes over large areas that are often difficult to monitor on the ground. The integration of machine learning then opened entirely new horizons for me. Combining these disciplines enables me to develop models that support sustainable water management decisions. This synergy of geology, technology, and data science is where I find the most purpose and inspiration in my work.
How have your studies in Soil Science and Climate Change Engineering shaped your current research focus?
My studies in Soil Science provided me with a solid foundation of the complex interactions between land, water, and ecosystems overall, but particularly of the effect of soil on water movement and availability. This background helped me appreciate the delicate balance necessary for sustaining healthy groundwater systems. Later, studying Climate Change Engineering broadened my perspective by highlighting how global environmental changes can disrupt these natural balances. Together, these fields have shaped my current research by deepening my understanding of both, the physical processes in the soil and the broader impacts of climate variability. This holistic view is essential to support better water resource planning and help communities adapt sustainably to environmental challenges.
What role does remote sensing play in your work?
Remote sensing plays a crucial role in my work by allowing me to see and understand groundwater systems from a perspective that would be impossible to achieve on the ground alone. It gives me the ability to monitor changes in water resources over time and across large regions, especially in areas where traditional field data is sparse or hard to access. This technology acts like a window into the Earth's hidden water stores, enabling me to capture real-time information that is crucial for managing water sustainably.
Moreover, remote sensing helps me connect the dots between environmental changes and their impact on groundwater availability. In essence, remote sensing transforms how I approach groundwater research by shifting it from local snapshots to a comprehensive, dynamic understanding that can benefit communities facing water scarcity.
What role does machine learning play in your research?
Machine learning is like the analytical engine behind my research - it helps me turn complex environmental data into meaningful insights. Through these algorithms, I can train models to recognize how groundwater responds to changing climate conditions or human pressures, and even forecast future trends with higher confidence. What’s most exciting is that it enables me to merge field data, satellite observations, and numerical simulations into one unified dataset for comprehensive analysis framework. In essence, machine learning extends my analytical reach - helping me discover the unseen and make smarter, evidence-based recommendations for sustainable water management.
What are the biggest data challenges you face (e.g. data scarcity, resolution, temporal gaps) when using remote sensing for hydrogeological studies in vulnerable regions?
One of the biggest challenges I face when using remote sensing for hydrogeological studies in vulnerable regions is dealing with low-resolution satellite data. The available open imagery lacks the spatial resolution needed to capture local variations. Another hurdle is translating what satellites see at the surface down to what’s happening underground. Groundwater processes are hidden and complex, so even the best remote sensing data can’t always give direct answers. As a result, I often need to creatively integrate different data sources to build a more complete understanding. Navigating these data challenges requires a mix of technical skills, but it’s also what makes research in vulnerable regions so rewarding. Every step forward can have a meaningful impact on water management and community resilience.
What are three skills you use daily in your research?
Three skills I use daily are analytical thinking to make sense of complex data, adaptability to tackle unexpected research challenges, and clear communication to share complex ideas with both specialists and non-specialists.
Please tell us about your latest work on Spatial Assessment of Soil Erosion for Prioritizing Efforts on SDG15.3 in Ethiopia. How can your findings guide real conservation action on the ground?
My latest work involves using spatial analysis to map and assess soil erosion across vulnerable areas in southern Ethiopia. By combining satellite data, field observations, and GIS tools, I identified hotspots where erosion is severely impacting land productivity and ecosystem health. This targeted approach helps prioritize where conservation efforts and resources are most urgently needed to halt land degradation. The strength of this work lies in its practical guidance: by showing exactly which areas face the greatest risk, policymakers, land managers, and communities can make informed decisions. Ultimately, this work supports Ethiopia’s commitment to SDG15.3 by turning data into clear action plans that protect soil, enhance biodiversity, and improve livelihoods on the ground.
Your studies show that vegetation cover (Cover Management factor (C-factor), Normalized Difference Vegetation Index (NDVI)) is among the key influences on both soil erosion and drought risk. Based on this, what kinds of land-use or management practices do you think could help communities strengthen their resilience?
Vegetation plays a critical role in protecting soil and reducing drought risk by stabilizing the land and improving moisture retention. To strengthen community resilience, promoting practices that maintain or increase vegetation cover is essential. This includes agroforestry, which improves soil structure and provides shade and organic matter. Sustainable groundwater utilization and management are vital. Efficient use of groundwater for irrigation can support vegetation during dry periods, helping maintain healthy cover even in drought-prone areas.
Other effective practices are conservation agriculture techniques that protect the soil surface, reduce erosion, and enhance soil moisture. Restoring degraded lands through native plant reforestation and protecting existing natural vegetation can also build long-term resilience. Supporting these practices with community education and local involvement ensures they are sustainable and tailored to the unique needs of each area.
How can satellite data and remote sensing technologies help better predict and manage droughts, floods and soil erosion in Ethiopia, and what advantages do they offer over ground-based approaches?
Satellite data and remote sensing technologies offer a powerful way to monitor Ethiopia’s natural environment on a large scale and in real time. They help predict droughts, floods, and soil erosion by providing continuous, up-to-date information on rainfall, vegetation health, soil moisture, land cover changes, and others across vast and remote areas. Compared to ground-based methods, satellites cover regions that are difficult or costly to access, filling critical data gaps. Their regular, consistent observations allow for early warning systems that can alert communities and decision-makers to emerging risks. This timely information supports proactive management, helping to reduce disaster impacts and guide effective conservation efforts before problems escalate.
Are there emerging remote sensing datasets that you believe will significantly advance the field in the next 5-10 years?
There are several emerging remote sensing datasets that I believe will transform our understanding and management of environmental resources in the coming years. Advances in higher-resolution satellite imagery, both spatially and temporally, will help monitor changes on the ground with unprecedented detail and frequency. Additionally, the integration of drones and small satellites into remote sensing networks will create more flexible and cost-effective ways to gather localized data alongside global monitoring. These innovations will empower researchers and decision-makers with richer, more precise information, enabling smarter, faster responses to challenges.
Based on your research findings, what actions would you recommend to policymakers and local communities to strengthen water and land resilience in Ethiopia?
Based on my research, I would recommend that policymakers and local communities focus on integrated water and land management strategies that prioritize both conservation and sustainable use. Protecting and restoring natural vegetation and managed aquifer recharge (MAR) areas are key steps to maintaining reliable water supplies. Investing in affordable groundwater exploration, mapping, and infrastructure is essential, especially given Ethiopia’s poor groundwater accessibility and limited data. Promoting policies that support efficient water use, especially groundwater management, alongside soil conservation practices, will build resilience against climate variability and help secure livelihoods for the future.
What is your favourite aggregate state of water?
My favorite aggregate state of water is liquid. It’s in this form that water nourishes life, shapes landscapes, and connects communities by flowing through rivers, sustaining crops, and providing for daily needs. The versatility and vitality of liquid water inspire much of my work in understanding and protecting this precious resource.
