A Vision for Hydrological Prediction

Abstract

IMproving PRedictions and management of hydrological EXtremes (IMPREX) was a European Union Horizon 2020 project that ran from September 2015 to September 2019. IMPREX aimed to improve society’s ability to anticipate and respond to future extreme hydrological events in Europe across a variety of uses in the water-related sectors (flood forecasting, drought risk assessment, agriculture, navigation, hydropower and water supply utilities). Through the engagement with stakeholders and continuous feedback between model outputs and water applications, progress was achieved in better understanding the way hydrological predictions can be useful to (and operationally incorporated into) problem-solving in the water sector. The work and discussions carried out during the project nurtured further reflections toward a common vision for hydrological prediction. In this article, we summarized the main findings of the IMPREX project within a broader overview of hydrological prediction, providing a vision for improving such predictions. In so doing, we first presented a synopsis of hydrological and weather forecasting, with a focus on medium-range to seasonal scales of prediction for increased preparedness. Second, the lessons learned from IMPREX were discussed. The key findings were the gaps highlighted in the global observing system of the hydrological cycle, the degree of accuracy of hydrological models and the techniques of post-processing to correct biases, the origin of seasonal hydrological skill in Europe and user requirements of hydrometeorological forecasts to ensure their appropriate use in decision-making models and practices. Last, a vision for how to improve these forecast systems/products in the future was expounded, including advancing numerical weather and hydrological models, improved earth monitoring and more frequent interaction between forecasters and users to tailor the forecasts to applications. We conclude that if these improvements can be implemented in the coming years, earth system and hydrological modelling will become more skillful, thus leading to socioeconomic benefits for the citizens of Europe and beyond.

What Are the Key Drivers Controlling the Quality of Seasonal Streamflow Forecasts?

“…Large‐scale (i.e., continental) multibasin modeling can complement the “deep” knowledge from basin‐based modeling, enhance process understanding, increase robustness of generalizations, and facilitate classification of basin behavior and prediction (Gudmundsson et al, 2012; Kumar et al, 2013; Pechlivanidis & Arheimer, 2015). Specifically, for seasonal hydrological forecasting, multibasin modeling can support better understanding of prediction uncertainty and go beyond sensitivities related to initial hydrological conditions and meteorological forecasts that regional investigations can only target (Lavers et al, 2020; Wood & Lettenmaier, 2008). This type of modeling has the potential to cross regional and international boundaries, while analysis over a number of basins allows the consideration of different geophysical and climatic zones and hydrological regimes (Gupta et al, 2014; Krysanova et al, 2017); hence, it can provide a deeper understanding of the underlying sensitivities in forecast quality.…”

Comparing seasonal streamflow forecast systems for management of a fresh water reservoir in the Netherlands

“…Along with this future research a prototype of a (pre-)operational system is being developed. Recently, much effort has been put into bridging the gap between science and operational use (e.g., Soares et al, 2018;van Den Hurk et al, 2016;Lavers et al, 2020). Skillful hydrological forecasts are the first step in successful operational use.…”

Introducing electronic circuits and hydrological models to postsecondary physical geography and environmental science students: systems science, circuit theory, construction, and calibration

“…The SLEQ evaluation was also the most anonymous. These anonymous types of feedback forms make it more likely that the students will provide a less-biased assessment of the class (Stone et al, 1977), although complete anonymity may not always be reliable nor accurate due to lessened accountability (Lelkes et al, 2012). Therefore, both the instructor-provided feedback and SLEQ evaluation can be used together to obtain a more comprehensive understanding of how the students assessed the circuit activity.…”