Forecasts of water variables – interview with Ilias Pechlivanidis

I can’t exactly remember the first time I met Ilias, but I can remember that every time we have the opportunity to sit together, we end up having great talks about hydrology, modelling and Greece (including its food/drink specialities and beautiful places to visit!).

Ilias is a senior hydrologist at the Swedish Meteorological and Hydrological Institute (SMHI) since 2012. He has broad experience in hydrological modelling and forecasting, and has been recently appointed Scientific leader of the theme “Forecasts of Water Variables” at SMHI’s R&D department.

I had the opportunity to ask him some questions about this new challenge and the future of hydrological forecasting for water managers:

Maria-Helena Ramos (MHR): What is the main focus of the research at SMHI on the forecasting of water variables?

Ilias Pechlivanidis (IP): I would firstly like to thank you, Maria-Helena, for giving me the opportunity to openly share our group’s vision with the broader HEPEX community. Hopefully this blog post will be useful to all friends and colleagues, and particularly inspire the young forecasters.

Going back to your question, I should say that as a service provider at the national, continental and very soon global scale, we produce forecasts of different water (quantity and quality) variables and indicators, such as discharge, soil moisture, groundwater levels, evapotranspiration, snow, sediments etc., at different timescales (at short to seasonal ranges) and hydro-climatic gradients. To ensure usefulness, these forecasts need to be unbiased, reliable and coherent.

We investigate all the components of the hydrological production chain (i.e. selection of meteorological forecasts, pre-processing, hydrological model(s) and setup, calibration and initialization, updating, and generation, evaluation and visualization/communication of forecasts), and evaluate and refine these components for understanding better the sources for predictability. Our efforts focus on identifying the key factors influencing the spatial and temporal variation in hydrological predictability and improving the communication methods bearing also in mind that the user needs are mostly local.

I also find important to note that SMHI is among the very few institutes that acts as a factory and a storage house of many data products, which are further used in our different multi-basin hydrological investigations at the large scale (see our HYPE hydrological model setups).

These spatially broad model applications require a constant interaction with users, in order to learn from their experience of applying our models at the local scale and better comprehend local processes within our work at national to global scales. Despite the challenges of producing and analyzing big data, I consider myself privileged to be given the opportunity to conduct comparative hydrological forecasting analyses and hence complementing the ‘deep’ knowledge from single catchment investigations.

MHR: SMHI has a strong focus on developing forecast products for end-users. In your opinion, is there a “science of hydrological forecasting” and is it currently in phase with operational developments for water (and related risks) management?

IP: Maybe I could say here that a nice lesson learnt working at SMHI’s services is that there is no ‘end-users’ but rather an endless chain of ‘users’.

Indeed, SMHI has been developing various products and services for different users; as an example here for the HEPEX interest, I would point:

  • the Swedish national demonstrator, including from short term to seasonal forecasts, climate predictions and many more,
  • the pan-European short-to-medium range hydrological forecasting service,
  • the pan-European seasonal forecasting demonstrator interface as a proof-of-concept for the Copernicus Climate Change Services (C3S).

I have found very intriguing the different level of understanding between users, and so each service should meet the unique user needs. Nevertheless there is always a continuous need for product and service evolution.

We have been acting as scientific knowledge purveyors incorporating robust new insights and successful outcomes from R&D projects into our products and services. Until recently efforts of the scientific community have been technical and numerical allowing a significant improvement of the services, yet setting unfortunately a knowledge gap between data providers and users.

Despite the deep SMHI in-house knowledge of user needs, application of social science on user engagement has allowed better communication of results and co-evolution of knowledge. We have therefore been investing on co-designing services, providing guidance on ways that our products can address problems at the local and regional scales, and also on ‘teach the teachers’ trainings to ensure that results are adequately communicated.

Another point here is that more and more institutes are nowadays developing similar products and services for different water-related sectors. Although scientific inter-comparisons are very important to improve our process understanding, I believe that different setups have strengths in capturing different aspects of reality; ‘useful information exists in each product/service, so one simply needs to extract it’. To produce the best product for our users, we are currently focusing on operationally implementing multi-model approaches, including different meteorological forecasting systems, pre-processing methods, and/or hydrological models, and further identifying best approaches for multi-model averaging.

MHR: If a young scientist wants to start a PhD thesis today on “forecasting water variables”, what would you suggest as a topic for a 3-year research work, for instance?

IP: As a member of the HEPEX community, I am not sure I could answer this question deterministically. I personally see a lot of potential in different topics, aiming to address fundamental scientific and operational challenges. Without being strict in my selection, I am generally inspired by 3 topics (as I support using 3 arguments in 3 sentences):

  1. Seamless hydrological forecasting; we need to understand how to better bridge between weather, seasonal forecasting and beyond for hydrological purposes, particularly nowadays that integration of available systems (setup for operating at different time horizons and for meeting different types of users) is computationally feasible.
  2. Assimilation of data to advance operational services at the large scale; given that in-situ observations are not usually available in an adequate spatio-temporal resolution, I see the need to explore new Earth Observation technologies and data provided in (near) real time to improve model initializations and hence forecasts.
  3. Hydrological forecasting from an impact perspective for better decision-making; we must bridge the gap between forecast experts and users, driven by the need for a better knowledge/integration of user needs, and highlighting the importance to consider new ways to communicate forecasts and their uncertainty.

MHR: Are there opportunities today for people willing to collaborate with your group?

IP: Absolutely. Our group has been participating in numerous national and international research (and consultancy) projects, with quite major funding coming from the European Commission. This has allowed us to build a strong network with other scientific groups, operational/research institutes, users, and SMEs. Researchers have also been visiting us to work on a targeted challenge. Post-doctoral researchers have also been significantly contributing to our success.

Taking this opportunity, I would kindly like to inform the HEPEX community that we are currently offering a Post-doc contract in the hydrological forecasting scientific theme. For more details please check here.

Thank you, Ilias, for this interview. We are looking forward to hearing more about your challenges and achievements in the future!


  1. Nice discussion, Ilias! I particularly like the concept of a ‘chain of users’, and that matches my own experience with climate and weather forecasts that are used for streamflow forecasting, that is used for water management decisions, that in turn influence agriculture and energy management decisions, and so on.

  2. Thanks for this response and valid example Andy! From my experience at SMHI, I can indeed support/highlight the ‘endless’ number of users in our services. It is also exciting that current research efforts aim on understanding better who/why/how forecasts are used in practice!

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