Predoctoral researcher


+34 942 20 16 16 Ext. 56221


Diego Urrea is a Civil Engineer graduated from the Escuela Colombiana de Ingeniería Julio Garavito, and holds a Master’s degree in Integrated Management of Water Systems from the University of Cantabria (Spain, 2019-2020), where he was recognized as the student with the best academic record. Since 2015, he has been involved in hydraulic and hydrological consulting, assuming roles such as specialized design engineer and project coordinator in the design of hydraulic infrastructure, sanitation systems, wastewater treatment plants, road drainage, flood modeling, and vulnerability analysis.

Currently, Diego is pursuing his career as a predoctoral researcher at the Institute of Environmental Hydraulics of the University of Cantabria (IHCantabria). He began his journey at this institute as a student intern, engaging in projects related to hydrology, climate, climate change, and the development of advanced statistical models for the prediction of climatic phenomena.

In his predoctoral research, Diego focuses on critical research lines for the management of multivariate and non-stationary hydrological risks. The comprehensive approach of his research allows him to significantly contribute to resilience and preparedness in the face of emerging hydrological challenges.

Research Lines

Compound Flood Risk Modeling: This approach analyzes the interaction of compound events associated with extreme rainfall in space and time, contributing to flooding in continental areas. This study is crucial for understanding how the combination of these factors can increase the severity and frequency of floods.

Multivariate Analysis in Hydrology: It uses advanced statistical techniques to assess the probability of extreme hydrological events, considering multiple variables simultaneously, allowing for a more realistic estimation of the frequency and severity of floods.

Non-Stationarity in Hydrological Time Series: Investigates how climate and anthropogenic changes alter the statistical properties of hydrological time series events, developing models that adapt to changes in variance and mean over time.

Assessment and Prediction of Extreme Events Under Climate Change: Explores how changing climate patterns can affect the frequency and magnitude of extreme events such as floods, contributing to the planning of resilient infrastructure and adaptation strategies.

Implications of Multivariate Return Period Estimation: Develops models that predict the probability of compound events considering multiple factors simultaneously, essential for impact definition.



Gomez Rave, D. V., Urrea Mendez, D. A., and Del Jesus Peñil, M. (2024). Understanding Compound Flooding hazard in Estuaries: Insights and Implications, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6265, https://doi.org/10.5194/egusphere-egu24-6265.

Urrea Méndez, D. A., V. Gómez, D., and Del Jesus Peñil, M. (2024). Exploring Multivariate Return Periods: Enhancing Accuracy in Hydrological Analysis for Flood Prediction , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5685, https://doi.org/10.5194/egusphere-egu24-5685.

Gomez Rave, D. V., Urrea Méndez, D. A., and Del Jesus Peñil, M. (2023). Multivariate Probability Analysis of Compound Flooding Dynamics., EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-12736, https://doi.org/10.5194/egusphere-egu23-12736.

Urrea Méndez, D. A., Gómez Rave, D. V., and Del Jesus Peñil, M. (2023). The Future of Extreme Event Risk Assessment: A Look at Multivariate Return Periods in More than Three Dimensions, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-11828, https://doi.org/10.5194/egusphere-egu23-11828.

Urrea Méndez, D., & del Jesus, M. (2023). Estimating extreme monthly rainfall for Spain using non-stationary techniques. Hydrological Sciences Journal, 68(7), 903–919. https://doi.org/10.1080/02626667.2023.2193294

Urrea Méndez, D. A., (2020). Evaluación de los efectos del cambio climático sobre la inundación en Corrales de Buelna. http://hdl.handle.net/10902/19716