Research & innovation priorities

UWMH works on new disruptive technologies and options able to change the way we think about the water cycle and its link to energy and materials within the regional and urban environment.

Uncertainty aware, extreme events (floods and droughts) risk management


Recognizing and embracing uncertainty in natural and human-engineered systems should be at the heart of any risk-related study, or approach, aiming to provide reliable, future-proofed and uncertainty-aware information and guidance to stakeholders, decision and policy makers.

This simple, yet often forgotten principle is the pillar of our thinking and modelling rationale and a conscious choice away from the utopia of determinism and a precisely known future.

By building upon probabilistic notions and concepts, such those of random variables, stochastic processes, and Monte-Carlo simulations we specialize on the development of uncertainty-aware frameworks and modelling approaches for the reliable and risk-informed management, operation and design of next generation hydrosystems. Of special interest are developments related with uncertainty quantification of predictive models and datasets; hydroclimatic time series augmentation (e.g., gap-filling, downscaling methods, synthetic data generation) and forecasting, as well as probabilistic modelling of extremes (e.g., floods, heatwaves, and droughts), whose criticality is underpinned by their critical link with human life and security.

In a nutshell to better reflect what we do, we often recall the aphorism in E. J. Gumbel’s (1958) book “Statistics of extremes” (Ch. 6) that states that “The improbable is bound to happen one day.” What we do is an attempt to secure and prepare water systems, infrastructures and human society in general, against the day that (im)probable risks become a reality.

  • Elsayed, H., Djordjevic, S., Savic, D., Tsoukalas, I., Makropoulos, C., 2020. The Nile Water-Food-Energy Nexus under Uncertainty: Impacts of the Grand Ethiopian Renaissance Dam. J. Water Resour. Plan. Manag.
  • Papaioannou, G., Efstratiadis, A., Vasiliades, L., Loukas, A., Papalexiou, S., Koukouvinos, A., Tsoukalas, I., Kossieris, P., 2018. An Operational Method for Flood Directive Implementation in Ungauged Urban Areas. Hydrology 5, 24.
  • Tsoukalas, I. 2021. The tales that the distribution tails of non-Gaussian autocorrelated processes tell: Efficient methods for the estimation of the k-length block-maxima distribution, doi:10.1080/02626667.2021.2014056
  • Tsoukalas, I., Efstratiadis, A., Makropoulos, C., 2019. Building a puzzle to solve a riddle: A multi-scale disaggregation approach for multivariate stochastic processes with any marginal distribution and correlation structure. J. Hydrol. 575, 354–380.
  • Tsoukalas, I., Efstratiadis, A., Makropoulos, C., 2018a. Stochastic Periodic Autoregressive to Anything (SPARTA): Modeling and Simulation of Cyclostationary Processes With Arbitrary Marginal Distributions. Water Resour. Res. 54, 161–185.
  • Tsoukalas, I., Efstratiadis, A., Makropoulos, C., 2017. Stochastic simulation of periodic processes with arbitrary marginal distributions, in: 15th International Conference on Environmental Science and Technology. CEST 2017. Rhodes, Greece.
  • Tsoukalas, I., Kossieris, P., Efstratiadis, A., Makropoulos, C., 2016. Surrogate-enhanced evolutionary annealing simplex algorithm for effective and efficient optimization of water resources problems on a budget. Environ. Model. Softw. 77, 122–142.
  • Tsoukalas, I., Kossieris, P., Makropoulos, C., 2020. Simulation of Non-Gaussian Correlated Random Variables, Stochastic Processes and Random Fields: Introducing the anySim R-Package for Environmental Applications and Beyond. Water 12, 1645.
  • Tsoukalas, I., Makropoulos, C., 2015. Multiobjective optimisation on a budget: Exploring surrogate modelling for robust multi-reservoir rules generation under hydrological uncertainty. Environ. Model. Softw. 69, 396–413.
  • Tsoukalas, I., Makropoulos, C., Koutsoyiannis, D., 2018b. Simulation of Stochastic Processes Exhibiting Any‐Range Dependence and Arbitrary Marginal Distributions. Water Resour. Res. 54, 9484–9513.

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