The workshop was organised under the umbrella of ECMWF, the Copernicus services CEMS and C3S, the Hydrological Ensemble Prediction EXperiment (HEPEX) and the Global Flood Partnership (GFP). The workshop lasted 3 days, with a keynote speaker followed by Q&A at the start of each of the 6 sessions. Each keynote talk focused on a different part of the forecast chain, from hybrid hydrological forecasting to the use of forecasts for anticipatory humanitarian action, and how the global and local hydrological scales could be linked. Following this were speedy poster pitches from around the world and poster presentations and discussion in the virtual ECMWF (Gather.town).
What was your poster about?
Gwyneth – I presented Evaluating the post-processing of the European Flood Awareness System’s medium-range streamflow forecasts in Session 2 – Catchment-scale hydrometeorological forecasting: from short-range to medium-range. My poster showed the results of the recent evaluation of the post-processing method used in the European Flood Awareness System. Post-processing is used to correct errors and account for uncertainties in the forecasts and is a vital component of a flood forecasting system. By comparing the post-processed forecasts with observations, I was able to identify where the forecasts were most improved.
Helen – I presented An evaluation of ensemble forecast flood map spatial skill in Session 3 – Monitoring, modelling and forecasting for flood risk, flash floods, inundation and impact assessments. The ensemble approach to forecasting flooding extent and depth is ideal due to the highly uncertain nature of extreme flooding events. The flood maps are linked directly to probabilistic population impacts to enable timely, targeted release of funding. The Flood Foresight System forecast flood inundation maps are evaluated by comparison with satellite based SAR-derived flood maps so that the spatial skill of the ensemble can be determined.
What did you find most interesting at the workshop?
Gwyneth – All the posters! Every session had a wide range of topics being presented and I really enjoyed talking to people about their work. The keynote talks at the beginning of each session were really interesting and thought-provoking. I especially liked the talk by Dr Wendy Parker about a fitness-for-purpose approach to evaluation which incorporates how the forecasts are used and who is using the forecast into the evaluation.
Helen – Lots! All of the keynote talks were excellent and inspiring. The latest developments in detecting flooding from satellites include processing the data using machine learning algorithms directly onboard, before beaming the flood map back to earth! If openly available and accessible (this came up quite a bit) this will potentially rapidly decrease the time it takes for flood maps to reach both flood risk managers dealing with the incident and for use in improving flood forecasting models.
How was your virtual poster presentation/discussion session?
Gwyneth – It was nerve-racking to give the mini-pitch to +200 people, but the poster session in Gather.town was great! The questions and comments I got were helpful, but it was nice to have conversations on non-research-based topics and to meet some of the EC-HEPEXers (early career members of the Hydrological Ensemble Prediction Experiment). The sessions felt more natural than a lot of the virtual conferences I have been to.
Helen – I really enjoyed choosing my hairdo and outfit for my mini self. I’ve not actually experienced a ‘real’ conference/workshop but compared to other virtual events this felt quite realistic. I really enjoyed the Gather.town setting, especially the duck pond (although the ducks couldn’t swim or quack! J). It was great to have the chance talk about my work and meet a few people, some thought-provoking questions are always useful.
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.
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.
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.
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.
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
The Maritime Continent commonly refers to the groups of islands of Indonesia, Borneo, New Guinea and the surrounding seas in the literature. My study area covers the Maritime Continent domain from 20°S to 20°N and 80°E to 160°E as shown in Figure 1. This includes Indonesia, Malaysia, Brunei, Singapore, Philippines, Papua New Guinea, Solomon islands, northern Australia and parts of mainland Southeast Asia including Thailand, Laos, Cambodia, Vietnam and Myanmar.
The ability of climate model to simulate the mean climate and climate variability over the Maritime Continent remains a modelling challenge (Jourdain et al. 2013). Our study examines the fidelity of Coupled Model Intercomparison Project phase 5 (CMIP5) models at simulating mean climate over the Maritime Continent. We find that there is a considerable spread in the performance of the Atmospheric Model Intercomparison Project (AMIP) models in reproducing the seasonal mean climate and annual cycle over the Maritime Continent region. The multi-model mean (MMM) (Figure 1b) JJA precipitation and 850hPa wind biases with respect to observations (Figure 1a) are small compared to individual model biases (Figure 1c-j) over the Maritime Continent. Figure 1 shows only a subset of Fig. 2 from Toh et al. (2017), for the full figure and paper please click here.
We also investigate the model characteristics that may be potential sources of bias. We find that AMIP model performance is largely unrelated to model horizontal resolution. Instead, a model’s local Maritime Continent biases are somewhat related to its biases in the local Hadley circulation and global monsoon.
To characterize model systematic biases in the AMIP runs and determine if these biases are related to common factors elsewhere in the tropics, we performed cluster analysis on Maritime Continent annual cycle precipitation. Our analysis resulted in two distinct clusters. Cluster I (Figure 2b,d) is able to reproduce the observed seasonal migration of Maritime Continent precipitation, but it overestimates the precipitation, especially during the JJA and SON seasons. Cluster II (Figure 2c,e) simulate weaker seasonal migration of Intertropical Convergence Zone (ITCZ) than observed, and the maximum rainfall position stays closer to the equator throughout the year. Tropics-wide properties of clusters also demonstrate a connection between errors at regional scale of the Maritime Continent and errors at large scale circulation and global monsoon.
On the other hand, comparison with coupled models showed that air-sea coupling yielded complex impacts on Maritime Continent precipitation biases. One of the outstanding problems in the coupled CMIP5 models is the sea surface temperature (SST) biases in tropical ocean basins. Our study highlighted central Pacific and western Indian Oceans as the key regions which exhibit the most surface temperature correlation with Maritime Continent mean state precipitation in the coupled CMIP5 models. Future work will investigate the impact of SST perturbations in these two regions on Maritime Continent precipitation using Atmospheric General Circulation Model (AGCM) sensitivity experiments.
Jourdain N.C., Gupta A.S., Taschetto A.S., Ummenhofer C.C., Moise A.F., Ashok K. (2013) The Indo-Australian monsoon and its relationship to ENSO and IOD in reanalysis data and the CMIP3/CMIP5 simulations. Climate Dynamics. 41(11–12):3073–3102
Toh, Y.Y., Turner, A.G., Johnson, S.J., & Holloway, C.E. (2017). Maritime Continent seasonal climate biases in AMIP experiments of the CMIP5 multimodel ensemble. Climate Dynamics. doi: 10.1007/s00382-017-3641-x
Over the past ~decade, extended-range forecasts of river flow have begun to emerge around the globe, combining meteorological forecasts with hydrological models to provide seasonal hydro-meteorological outlooks. Seasonal forecasts of river flow could be useful in providing early indications of potential floods and droughts; information that could be of benefit for disaster risk reduction, resilience and humanitarian aid, alongside applications in agriculture and water resource management.
“Ultimately, the most informative forecasts of flood hazard at the seasonal scale are streamflow forecasts using hydrological models calibrated for individual river basins. While this is more computationally and resource intensive, better forecasts of seasonal flood risk could be of immense use to the disaster preparedness community.”
Over the past months, researchers in the Water@Reading* research group have been working with the European Centre for Medium-Range Weather Forecasts (ECMWF), to set up a new global scale hydro-meteorological seasonal forecasting system. Last week, on 10th November 2017, the new forecasting system was officially launched as an addition to the Global Flood Awareness System (GloFAS). GloFAS is co-developed by ECMWF and the European Commission’s Joint Research Centre (JRC), as part of the Copernicus Emergency Management Services, and provides flood forecasts for the entire globe up to 30 days in advance. Now, GloFAS also provides seasonal river flow outlooks for the global river network, out to 4 months ahead – meaning that for the first time, operational seasonal river flow forecasts exist at the global scale – providing globally consistent forecasts, and forecasts for countries and regions where no other forecasts are available.
The seasonal outlook is displayed as three new layers in the GloFAS web interface, which is publicly (and freely) available at www.globalfloods.eu. The first of these gives a global overview of the maximum probability of unusually high or low river flow (defined as flow exceeding the 80th or falling below the 20th percentile of the model climatology), during the 4-month forecast horizon, in each of the 306 major world river basins used in GloFAS-Seasonal.
The second layer provides further sub-basin-scale detail, by displaying the global river network (all pixels with an upstream area >1500km2), again coloured according to the maximum probability of unusually high or low river flow during the 4-month forecast horizon. In the third layer, reporting points with global coverage are displayed, where more forecast information is available. At these points, an ensemble hydrograph is provided showing the 4-month forecast of river flow, with thresholds for comparison of the forecast to typical or extreme conditions based on the model climatology. Also displayed is a persistence diagram showing the weekly probability of exceedance for the current and previous three forecasts.
Over the coming months, an evaluation of the system will be completed – for now, users are advised to evaluate the forecasts for their particular application. We welcome any feedback on the forecast visualisations and skill – feel free to contact me at the email address below!
To find out more, you can see the University’s press release here, further information on SEAS5 here, and the user information on the seasonal outlook GloFAS layers here.
*Water@Reading is “a vibrant cross-faculty centre of research excellence at the University of Reading, delivering world class knowledge in water science, policy and societal impacts for the UK and internationally.”
Full list of collaborators:
Rebecca Emerton1,2, Ervin Zsoter1,2, Louise Arnal1,2, Prof. Hannah Cloke1, Dr. Liz Stephens1, Dr. Florian Pappenberger2, Prof. Christel Prudhomme2, Dr Peter Salamon3, Davide Muraro3, Gabriele Mantovani3
1 University of Reading 2 ECMWF 3 European Commission JRC