Institution:     University of Calgary

Expertise tags:  Water Resources Engineering; Stochastic Hydrology; Deep Learning; Climate Change

Research description:
My research focuses on stochastic hydrology, where I develop and apply advanced statistical and computational methods to better understand hydrological processes, quantify uncertainties, and assess climate change impacts. My work integrates probabilistic modeling, extreme value analysis, and machine learning techniques to improve the representation of hydroclimatic variability across spatial and temporal scales.

A key component of my research involves the development of data-driven downscaling and bias-correction frameworks for climate model outputs, with a particular focus on high-resolution precipitation. I leverage deep learning approaches, such as generative adversarial networks (GANs), alongside stochastic methods to generate physically consistent, high-resolution hydroclimatic datasets that preserve key characteristics such as intermittency, seasonality, and extremes.

Overall, my research bridges statistical hydrology, climate science, and data science, with the goal of advancing reliable tools for climate-resilient water resources management and infrastructure planning.

Connection to IGWS:
We collaborate on joint research initiatives aimed at analyzing hydroclimatic extremes and assessing their hydrological impacts under climate change across regional and global scales.
Active since:    2021

Institution:     University of Calgary

Expertise tags:  Hydrological modelling, Climate change, Uncertainty quantification, Machine learning

Research description:
My research sits at the intersection of hydrology, climate science, and statistical learning, with a focus on understanding and predicting hydroclimatic variability and extremes in a changing climate. I develop integrated modelling frameworks that combine process-based understanding with modern statistical and machine learning approaches—including physics-informed neural networks (PINNs)—to improve the representation, scalability, and efficiency of environmental models. A central component of my work is uncertainty quantification and inference in complex, high-dimensional settings, with an emphasis on bridging physical insight and data-driven methods to enhance predictive skill while maintaining interpretability. Through this work, I aim to support more informed decision-making in water resources and climate risk management by providing tools that are both scientifically grounded and computationally efficient.

Connection to IGWS:
My collaboration with IGWS spans multiple aspects of hydroclimatology and environmental modelling, including joint work on large-sample hydrological modelling, parameter estimation, and the development of data-driven emulation frameworks for efficient exploration of model behaviour. Our work also engages with broader questions of hydroclimatic variability and extremes under changing climate conditions, combining statistical and machine learning approaches with process-based understanding. An important dimension of this research is assessing the impacts of climate variability and change on critical infrastructure and resources, including water supply systems and transportation networks such as rail. The collaboration includes ongoing co-authorship, methodological and data exchange, and coordinated research activities focused on advancing scalable and robust modelling frameworks for water and climate systems that support risk assessment and decision-making.

Institution:     University of Virginia

Expertise tags:  Artificial Intelligence for Geosciences, Explainable AI, Climate predictability and teleconnections, Climate change impacts

Research description:
Mamalakis' research interests include hydroclimatic variability and predictability, artificial intelligence for geoscience, explainable AI, and the development of data-driven tools for improving prediction and understanding of environmental systems.

Connection to IGWS:
We have collaborated on joint research problems on developing and applying AI models for precipitation downscaling. The collaboration includes co-authorship and participation in joint scientific activities.

Institution:     University of Padova, Italy
Profile
Expertise tags:  Extreme precipitation; extreme value analysis; non-asymptotic statistics; remote sensing of precipitation; hydrological and geomorphological impacts

Research description:
My research sits at the intersection of atmospheric physics, hydrology, geomorphology, climatology, and climate change, with a particular focus on extreme precipitation and its associated hazards. My current work centres on the statistical characterisation of extreme precipitation events and the projection of future extremes through the integration of climate and statistical models. I have developed extensive expertise in precipitation remote sensing using weather radars and satellites, including the derivation of extreme value statistics from these data sources. My long-term research goal is to establish quantitative links between the physical mechanisms driving precipitation and the statistical patterns that emerge from them.

Connection to IGWS:
We have collaborated on methodological developments and research projects. Our collaboration includes co-authorship of scientific articles and joint research projects.

Institution:     University of Cyprus, Cyprus

Expertise tags:  Hydrological modelling, ecohydrology, stochastic hydrology, risk management

Research description:
A. Paschalis’ research at the University of Cyprus focuses on advancing the understanding and modelling of ecohydrological systems, from catchment to global scales. His research group develops novel models for understanding and predicting the coupled behaviour of the water cycle and vegetation dynamics in both natural and built environments. His work also examines these dynamics under uncertainty, using statistical hydrology and stochastic methods to quantify variability and improve predictions. In the context of climate change, his research assesses impacts on water resources and ecosystem functioning, supporting hydrological risk assessment and sustainable water management

Connection to IGWS:
We have collaborated on research focusing on understanding the intensification of precipitation extremes and risk of flooding under climate change, at global scales, using multi source data and methods from statistical and stochastic hydrology.

Institution:     University of Lausanne

Expertise tags:  Precipitation extremes, urban hydrology, hydrometeorology, climate change

Research description:
My expertise lies in developing physically based and stochastic approaches to project changes in hydrometeorological extremes and their hydrological implications. My work focuses particularly on rainfall extremes, how they are modified by urbanization and climate change, and their consequences for flood hazards.

Connection to IGWS:
We have collaborated on topics related to the intensification of extreme rainfall and the development of simple yet more robust approaches for constructing design storms used in planning sustainable future urban drainage systems. This collaboration has resulted in co-authorship on two papers that are currently under review.

Institution:     University of Virginia, United States
 

Expertise tags:  Hydroclimatic extremes, Climate downscaling, Generative AI, Extreme value statistics

Research description:

My research focuses on hydro-climatic extremes, including precipitation variability, atmospheric rivers, and climate teleconnections, integrating both statistical and machine learning approaches. During my doctoral work, I developed probabilistic and data-driven frameworks to understand extreme events and their links to large-scale climate drivers. More recently, my work emphasizes AI-driven climate downscaling using deep learning and generative models such as U-Net, GANs, and diffusion models. A key aspect of my research is improving the physical consistency and interpretability of these models through explainable AI and uncertainty quantification. Overall, my work aims to bridge statistical hydrology and modern AI to better understand and predict extreme climate phenomena.

Connection to IGWS:
We collaborate on research in hydro-climatic extremes and precipitation downscaling, combining statistical and machine learning ((Generative AI) approaches. Our collaboration includes co-authorship on multiple studies and ongoing scientific exchange on generative modeling, stochastic processes, and climate data analysis.

Active since:    2024

Institution:     University of Calgary

Expertise tags:  Natural Hazards, Machine Learning, Statistics, Hydrology

Research description:
My research addresses the human dimensions of climate change across interconnected domains — from hydroclimatic extremes such as floods and droughts, to urban water supply and demand under shifting climate and population pressures. I develop data-driven and statistical frameworks that integrate large observational datasets, reanalysis products, and climate projections to characterize how a warming atmosphere alters the frequency and severity of extreme events and their infrastructure implications. A growing thread of my work connects climate hazards directly to population health, examining how heatwaves, wildfire smoke, and air quality extremes affect emergency department visits and child health outcomes, in collaboration with physicians, epidemiologists, and public health researchers. Across these themes I apply machine learning, causal inference, and remote sensing to bridge the gap between physical climate signals and real-world human consequences.

Connection to IGWS:
My collaboration with IGWS spans multiple dimensions of climate change impacts, including ongoing joint work on the health effects of wildfire smoke and extreme heatwaves, large-scale rainfall–runoff modelling across the contiguous US to identify dominant hydrological controls, and urban climate adaptation through satellite-based heat mapping developed in partnership with the City of Calgary. Our shared activities include co-authorship, data and methodological exchange, and coordinated research on climate impacts across water systems and human health.