Forecasts of Water Variables

Should I wear my gumboots tomorrow?

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Many water users are affected by weather/climate variations (at short to seasonal ranges) and therefore there is a need to manage such variations to their advantage through better understanding. The production of hydrological forecasts involves a number of components (i.e. selection of meteorological/climate forecasts, pre-processing, hydrological model(s) and setup, calibration and initialization, updating, and generation, evaluation and visualization/communication of forecasts), which are strongly subject to multiple sources of error/uncertainty. To improve the hydrological forecast system, each of these components has to be evaluated and considered for refinements. In addition, there is a need to better understand the key factors influencing the spatial and temporal variation in hydrological predictability; bearing also in mind that the user needs, e.g. for water management or security, are mostly local. To improve the usefulness of forecasts, we are currently moving towards impact-based forecasts, in which the predictions are linked to local vulnerability and/or profit of the users.

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Hydrological forecasting production chain.

To ensure usefulness, hydrological forecasts need to be unbiased, reliable and coherent. One way to achieve this is by refining pre-processing methods using multiple-data sources in hydrological forecasting. Use of multi-model approaches is another way, as different (climate or hydrological) models have strengths in capturing different aspects of reality. Finally, there is a gap between experts and users on the potential value in forecasts, which is mainly driven by the lack of knowledge of user needs. This points towards the need to consider new ways to communicate forecasts and their uncertainty.

Research and Development questions

What are the limits of predictability in the hydrological forecasting systems and which are the drivers affecting the skill both in space and time for different water variables?

Hydrological forecasting at the large scale allows the evaluation of forecasts at multiple catchments for different initialization and lead times. The spatial pattern of predictability can further be linked to the physiographic-hydrologic-climatic characteristics of the catchment systems, whilst sensitivity analyses can quantify the relative importance of meteorological forecasts and initial hydrological conditions to the hydrological predictability. 

How can we improve the local accuracy and link the forecasts to local user needs?

Technologically new in-situ data and earth observations, also occasionally provided in (near) real time, are assimilated to improve forecasts through an accurate initialisation of the model states. With local knowledge, forecasts are tailored towards the user needs, post-processed (transforming data into information) and adapted to the regional scale.

How are the forecasts perceived by different users and how to better communicate hydrological forecasts at various spatial and temporal resolutions?

The gap of knowledge between scientists/data providers and users is narrowed by evaluating and communicating hydrological forecasts from an impact (local vulnerability, profit etc.) perspective; hence resulting into better decision-making. We explore new user-driver metrics and visualisation methods, and continuously monitor the co-evolution of knowledge resulting in the co-design of forecasting services.

Our core publications in this Scientific focus

Arheimer, B., Lindström, G., & Olsson, J. (2011). A systematic review of sensitivities in the Swedish flood-forecasting system. Atmospheric Research, 100(2–3), 275–284. https://doi.org/10.1016/j.atmosres.2010.09.013

Crochemore, L., Ramos, M.-H., & Pappenberger, F. (2016). Bias correcting precipitation forecasts to improve the skill of seasonal streamflow forecasts. Hydrology and Earth System Sciences, 20, 3601–3618. https://doi.org/10.5194/hess-20-3601-2016

Foster, K., Bertacchi Uvo, C., and Olsson, J.: The development and evaluation of a hydrological seasonal forecast system prototype for predicting spring flood volumes in Swedish rivers, Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2017-588, in review, 2017.

Olsson, J., Uvo, C. B., Foster, K., & Yang, W. (2016). Technical Note: Initial assessment of a multi-model approach to spring flood forecasting in Sweden. Hydrology and Earth System Sciences, 20, 659–667. https://doi.org/10.5194/hess-20-659-2016

Olsson, J., & Lindström, G. (2008). Evaluation and calibration of operational hydrological ensemble forecasts in Sweden. Journal of Hydrology, 350(1–2), 14–24. https://doi.org/10.1016/j.jhydrol.2007.11.010

Pechlivanidis, I. G., Bosshard, T., Spångmyr, H., Lindström, G., Gustafsson, D., & Arheimer, B. (2014). Uncertainty in the Swedish operational hydrological forecasting systems. In M. Beer, S. K. Au, & J. M. Hall (Eds.), Vulnerability, Uncertainty, and Risk: Quantification, Mitigation and Management (pp. 253–262). Liverpool, UK. https://doi.org/10.1061/9780784413609.026

Links

Find operational short to medium range forecasts
Europe: http://riverinfo.eu/
Sweden: http://vattenweb.smhi.se

Find operational seasonal forecasts
Europe: http://swicca.eu/indicator-interface/seasonal-forecasts-maps/