Jonathan Mellor, Ph.D
Jonathan Mellor uses multidisciplinary complex systems methodologies to better understand the linkages between water and health in developing world countries. A Virginia native, he took this approach for his doctoral research working under Prof. James Smith at the University of Virginia to better understand how household drinking water becomes contaminated and thereby causes diarrhea in South African children. Prior to this, he was a Master’s International student at Michigan Technological University and Peace Corps Volunteer in Uganda where he studied household water usage and interventions to reduce diarrhea. As a postdoctoral associate, he is currently working with Prof. Julie Zimmerman to study the linkages between climate change, drinking water quality and child health in developing countries. He holds a B.S. in Physics from the College of William and Mary, an M.S. in Environmental Engineering from Michigan Technological University and an M.S. in Physics and a Ph.D. in Civil Engineering from the University of Virginia.
Jonathan Mellor is now an Assistant Professor at the University of Connecticut. At UConn he is continuing his work to uncover the linkages between climate change, water resources and health. His specific focus is on studying how communities in Africa and elsewhere can better adapt to a rapidly changing climate to improve their resiliency to climatic variability.
As an environmental engineering, I use systems approaches and the coupling of human, engineered and natural environments to study the complexities of water systems in water-stressed communities around the world. My expertise lies in developing tools to integrate field-based water and health research with systems approaches to explore counterfactual scenarios and the adaptive capacity of such communities. I am currently studying how developing world communities might be affected by and adapt to climate change.
The World Health Organization conservatively estimates that temperature increases due to climate change were already causing an additional 47,000 diarrhea-related deaths globally in the year 2000. This disease burden is disproportionally affecting the nearly 800 million people worldwide who lack access to a safe drinking water source. Since these populations typically get their drinking water from streams, small ponds or inadequate engineered water systems, they are highly vulnerable to floods, droughts and other changes to weather patterns that are already occurring as a result of anthropogenic climate change.
Coupled Human/Engineered/Natural Systems:
Although the connections between climate change, water and diarrhea seem self-evident, quantifying and understanding these relationships is complicated because it requires the understanding of the complex and transdisciplinary human/engineered/natural Water, Sanitation and Hygiene (WASH) system. When rainfall increases there might be more water available for consumption, but that water is more likely to be contaminated because contaminants are flushed into water supplies. During dry periods, water contamination can become concentrated and there might be less water for consumption. Underlying these natural systems are the engineered systems that are frequently inadequate in many resource-limited regions of the developing world. For instance, water systems frequently work on an intermittent basis which can cause them to become contaminated. Humans and their interaction and adaptation to the engineered and natural WASH systems are also critically important. Lastly, all three WASH system components are likely to vary significantly between different geographic and cultural settings.
The novel means we are using to study this system is an agent-based model (ABM). ABMs use agents who live in a given environment and who follow certain behavior rules. In our case, agents are people and households who collect and use water in different ways every day. Their environment consists of the water resources available to them and the climate of their region. Our models rely on data collected from developing countries about the quality and quantity of water that people use on a given day. When we correlate that data with the actual weather on the day the data was taken, we can get a better understanding about the water available to people during different weather events. We can then overlay our model with different climate change predictions to simulate what might happen if, for instance, there is a longer than usual rainy season or a drought.
By using this technique we can take a bottom up approach to understand how particular practices by our agents might impact their drinking water quality or diarrhea rates during different climate change scenarios. We can then use our model to understand and prioritize potential adaptation strategies to lessen the effects of climate change.
Overall, we hope to use this information to quantitatively inform academics, policy makers and the general public about how much climate change will impact vulnerable populations and how much adaptation might be needed to lessen the effects of climate change.