Dario Pumo is an Associate Professor (CEAR-01/B) at the Department of Engineering, University of Palermo (Italy).

He received his MSc degree (summa cum laude) in Civil Engineering (Hydraulics) and his PhD in Hydraulic Engineering from the University of Palermo. In 2009, he was a Research Fellow at Princeton University (NJ, USA) under the NASA-funded WaterSCAPES project.

His research activity focuses on hydrology, ecohydrology, and hydroinformatics, with particular emphasis on climate change mitigation and adaptation. He has been involved in several national and international funded projects, both as principal investigator and member of research units.

He is a member and delegate for “Website and Communications” of the PhD Board in D084 - Chemical, Environmental, Biomedical, Hydraulic And Materials Engineering, where he also teaches doctoral courses on machine learning and artificial neural networks applied to hydrological systems. He is member of the Interclass Board for the degree programs in “Environmental and Territorial Engineering” (LM35) at the Department of Engineering and “Spatial Planning” (LM 48) at the Department of Architecture. Within the LM35 programme, serves as a member of the “Quality Assurance” and “Students Orientation” committees, and as delegate for “Internationalisation, double degree programs, and Erasmus”. He has served as appointed technical consultant in hydrology for Italian Tribunals and Courts of Justice. 

He has been teaching at undergraduate and graduate levels since 2018 and has supervised numerous MSc theses. He is author and co-author of several peer-reviewed publications in leading international journals and actively participates in the scientific community as reviewer, invited speaker, session chair, and member of editorial boards and scientific committees.

OPEN BADGE: English C1 for Academic Purposes (EAP)

He conducts high impact research in the field of hydrology, eco-hydrology and hydroinformatics, including specific topics such as:

• hydrological/ecohydrological modeling in semi-arid regions and wetlands
• hydrological control on biodiversity and vegetation water stress
• rainfall-runoff models and water resources assessment
• environment monitoring and early warning systems for rainfall triggered landslides;
• statistical hydrology: trend analysis and analysis of hydrological extremes;
• hydrological changes induced by climate and land use changes;
• indicators for hydrological regime alterations and river discharge monitoring;
• NBS, green roofs and climate adaptive solutions
• Machine Learning, Soft Computing, Artificial Neural Network, Genetic Programming

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