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Hydrologic Extremes at the Global Scale: teleconnections, extreme-rich/poor periods, climate drivers and predictability

Periodic Reporting for period 1 - HEGS (Hydrologic Extremes at the Global Scale: teleconnections, extreme-rich/poor periods, climate drivers and predictability)

Reporting period: 2019-05-17 to 2021-05-16

Hydrologic extremes (floods and intense precipitations) are among Earth’s most common natural hazards and cause considerable loss of life and economic damage. Despite this, some of their key characteristics are still poorly understood at the global scale. The IPCC thus reports “a lack of evidence and thus low confidence regarding the sign of trend in the magnitude and/or frequency of floods on a global scale”. More generally, the space-time variability of hydrologic extremes is yet to be thoroughly described at the global scale. As a striking illustration, the recent initiative “23 unsolved problems in Hydrology” includes questions such as: Is the hydrological cycle regionally accelerating/decelerating under climate and environmental change? How do extremes around the world teleconnect with each other and with other factors? How do flood-rich and drought-rich periods arise, are they changing, and if so why?

It is vital to fill these knowledge gaps to inform design, safety and financial procedures and to improve hazard preparedness and response. The project’s ambition is hence to better understand the global space-time variability of hydrologic extremes, using a three-pillar research strategy based on methodological innovation, extensive data analysis and proof-of-concept case studies. The specific objectives are to:
1. Develop a statistical framework to describe the global-scale variability of extremes in relation to climate;
2. Analyse global precipitation/streamflow datasets with the aim of quantifying teleconnections, spatial clustering, trends and extreme-rich/poor periods, along with their climate drivers;
3. Explore practical applications such as global early warning systems allowing international disaster response organisations to trigger early actions.

Successful completion of the project will deliver new tools to analyse extremes at the global scale and will hence contribute to more efficient risk management.
The work performed so far mostly focused on developing and testing the statistical framework (objective 1) and applying it to global datasets of floods and extreme precipitation (Objective 2).

We developed a very general statistical framework to describe how environmental data vary in Space, Time or other Dimensions (it has hence been named STooDs). This of course includes data describing hydrologic extremes such a floods and extreme precipitation, but it is not limited to it. The computing code implementing STooDs has been released and is freely available as an open-source software. It has also been quite extensively tested on several case studies, including: the hidden climate patterns controlling flood occurrences in France and Eastern Australia; an attempt at predicting high, medium and low flows across Australia from large-scale climate; the co-occurrence of hot-and-dry fire-prone conditions in Australia.

Datasets describing floods and extreme precipitation at the global scale have also been analyzed using the STooDs framework. These datasets have been thoroughly reviewed and include more than 1800 hydrometric stations and more than 1700 raingauges located in most regions of the world (although with a strongly varying density). Results highlight interesting teleconnection patterns at the global scale: distant regions of the world are in phase or sometimes in anti-phase in terms of probabilities of flood occurrence. In terms of time variability, both floods and extreme precipitation are dominated by year-to-year variability: the evidence for extreme-rich/poor periods is limited, and trends are minor (extreme precipitation) or even hardly noticeable (floods). This is to be contrasted with other variables such as sea surface temperatures which, when analyzed with exactly the same statistical framework, show clear evidence of both trends and low-frequency variability. The analysis of these results is currently being finalized.
The STooDs framework corresponds to a significant methodological innovation. In particular, it builds on the recent and promising concept of "hidden climate indices", and it implements the following new functionalities: (1) handling of thousands of stations and hence global-scale applicability; (2) joint analysis of several environmental variables and hence applicability to compound hazards; (3) flexible specification of the probabilistic model. These new functionalities make STooDs a very general probabilistic framework that can be reused beyond the particular case studies of this project. Along with the release of the open-source code, this has the potential to indirectly induce new advances in hydroclimatology (e.g. by analyzing other variables such as droughts, extreme wind, etc.) and beyond.

The extensive data analysis performed as part of the second objective contributes to a better characterization of the natural variability of hydrologic extremes at the global scale. It confirms the fact, already emphasized by several authors, that floods and extreme precipitation do not vary in the same way. It moves beyond this observation by building probabilistic models to quantify these distinct variabilities at the global scale. This is a much-needed improvement since hydrologic extremes have major socio-economic impacts yet remain incompletely understood at the global scale. These probabilistic models might be used and hence have potential impacts for scientific activities such as climate models evaluation, climate change attribution and adaptation, etc.

Finally, we are currently in the process of evaluating the predictability of hydrologic extremes from large-scale climate variables at the global scale. Should this predictability be significant, further applications with large socio-economic impact such as seasonal forecasting could be considered.
Major Flood Events of 2019
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Droughts and Heat Waves in South-East Australia
The effect of El Nino on Eastern Australian Rainfall