Extending the predictability of flood hazard at the global scale

Email: rebecca.emerton@reading.ac.uk

When I started my PhD, there were no global scale operational seasonal forecasts of river flow or flood hazard. Global overviews of upcoming flood events are key for organisations working at the global scale, from water resources management to humanitarian aid, and for regions where no other local or national forecasts are available. While GloFAS (the Global Flood Awareness System, run by the European Centre for Medium-Range Weather Forecasts (ECMWF) and the European Commission Joint Research Centre (JRC) as part of the Copernicus Emergency Management Services) was producing operational, openly-available flood forecasts out to 30 days ahead, there was a need for more extended-range forecast information. Often, due to a lack of hydrological forecasts, seasonal rainfall forecasts are used as a proxy for flood hazard – however, the link between precipitation and floodiness is nonlinear, and recent research has shown that seasonal rainfall forecasts are not necessarily the best indicator of potential flood hazard. The aim of my PhD research was to look into ways in which we could provide earlier warning information, several weeks to months ahead, using hydrological analysis in addition to the meteorology.

Presidente Kuczynski recorre zonas afectadas por lluvias e inund
Flooding in Trujillo, Peru, March 2017 (Photo: Presidencia Perú on Twitter)

Broadly speaking, there are two key ways in which to provide early warning information on seasonal timescales: (1) through statistical analysis based on large-scale climate variability and teleconnections, and (2) by producing dynamical seasonal forecasts using coupled ocean-atmosphere GCMs. Over the past 4.5 years, I worked on providing hydrologically-relevant seasonal forecast products using these two approaches, at the global scale. This blog post will give a quick overview of the two new forecast products we produced as part of this research!

Can we use El Niño to predict flood hazard?

ENSO (the El Niño Southern Oscillation), is known to influence river flow and flooding across much of the globe, and often, statistical historical probabilities of extreme precipitation during El Niño and La Niña (the extremes of ENSO climate variability) are used to provide information on likely flood impacts. Due to its global influence on weather and climate, we decided to assess whether it is possible to use ENSO as a predictor of flood hazard at the global scale, by assessing the links between ENSO and river flow globally, and estimating the equivalent historical probabilities for high and low river flow, to those that are already used for meteorological variables.

With a lack of sufficient river flow observations across much of the globe, we needed to use a reanalysis dataset – but global reanalysis datasets for river flow are few and far between, and none extended beyond ~40 years (which includes a sample of ≤10 El Niños and ≤13 La Niñas). We ended up producing a 20th Century global river flow reconstruction, by forcing the Camaflood hydrological model with ECMWF’s ERA-20CM atmospheric reconstruction, to produce a 10-member river flow dataset covering 1901-2010, which we called ERA-20CM-R.

elnino_flood_hazard_gif_beccalize

Using this dataset, we calculated the percentage of past El Niño and La Niña events, during which the monthly mean river flow exceeded a high flow threshold (the 75th percentile of the 110-year climatology) or fell below a low flow threshold (the 25th percentile), for each month of an El Niño / La Niña. This percentage is then taken as the probability that high or low flow will be observed in future El Niño/La Niña events. Maps of these probabilities are shown above, for El Niño, and all maps for both El Niño and La Niña can be found here. When comparing to the same historical probabilities calculated for precipitation, it is evident that additional information can be gained from considering the hydrology. For example, the River Nile in northern Africa is likely to see low river flow, even though the surrounding area is likely to see more precipitation – because it is influenced more by changes in precipitation upstream. In places that are likely to see more precipitation but in the form of snow, there would be no influence on river flow or flood hazard during the time when more precipitation is expected. However, several months later, there may be no additional precipitation expected, but there may be increased flood hazard due to the melting of more snow than normal – so we’re able to see a lagged influence of ENSO on river flow in some regions.

While there are locations where these probabilities are high and can provide a useful forecast of hydrological extremes, across much of the globe, the probabilities are lower and much more uncertain (see here for more info on uncertainty in these forecasts) than might be useful for decision-making purposes.

Providing openly-available seasonal river flow forecasts, globally

For the next ‘chapter’ of my PhD, we looked into the feasibility of providing seasonal forecasts of river flow at the global scale. Providing global-scale flood forecasts in the medium-range has only become possible in recent years, and extended-range flood forecasting was highlighted as a grand challenge and likely future development in hydro-meteorological forecasting.

To do this, I worked with Ervin Zsoter at ECMWF, to drive the GloFAS hydrological model (Lisflood) with reforecasts from ECMWF’s latest seasonal forecasting system, SEAS5, to produce seasonal forecasts of river flow. We also forced Lisflood with the new ERA5 reanalysis, to produce an ERA5-R river flow reanalysis with which to initialise Lisflood, and to provide a climatology. The system set-up is shown in the flowchart below.

glofas_seasonal_flowchart_POSTER_EGU

I also worked with colleagues at ECMWF to design forecast products for a GloFAS seasonal outlook, based on a combination of features from the GloFAS flood forecasts, and the EFAS (the European Flood Awareness System) seasonal outlook, and incorporating feedback from users of EFAS.

After ~1 year of working on getting the system set up and finalising the forecast products, including a four-month research placement at ECMWF, the first GloFAS -Seasonal forecast was released in November 2017, with the release of SEAS5. GloFAS-Seasonal is now running operationally at ECMWF, providing forecasts of high and low weekly-averaged river flow for the global river network, up to 4 months ahead, with 3 new forecast layers available through the GloFAS interface. These provide a forecast overview for 307 major river basins, a map of the forecast for the entire river network at the sub-basin scale, and ensemble hydrographs at thousands of locations across the globe (which change with each forecast depending on forecast probabilities). New forecasts are produced once per month, and released on the 10th of each month. You can find more information on each of the different forecast layers and the system set-up here, and you can access the (openly available) forecasts here. ERA5-R, ERA-20CM-R and the GloFAS-Seasonal reforecasts are also all freely available – just get in touch! GloFAS-Seasonal will continue to be developed by ECMWF and the JRC, and has already been updated to v2.0, including a calibrated version of the hydrological model.

NEW_WEB_figure1_basins
Screenshot of the GloFAS seasonal outlook at www.globalfloods.eu

So, over the course of my PhD, we developed two new seasonal forecasts for hydrological extremes, at the global scale. You may be wondering whether they’re skilful, or in fact, which one provides the most useful forecasts! For information on the skill or ‘potential usefulness’ of GloFAS-Seasonal, head to our paper, and stay tuned for a paper coming soon (hopefully! [update: this paper has just been accepted and can be accessed online here]) on the ‘most useful approach for forecasting hydrological extremes during El Niño’, in which we compare the skill of the two forecasts at predicting observed high and low flow events during El Niño.

 

With thanks to my PhD supervisors & co-authors:

Hannah Cloke1, Liz Stephens1, Florian Pappenberger2, Steve Woolnough1, Ervin Zsoter2, Peter Salamon3, Louise Arnal1,2, Christel Prudhomme2, Davide Muraro3

1University of Reading, 2ECMWF, 3European Commission Joint Research Centre

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