Seasonal Forecasting

When the Lakes Remember: Unravelling the Sudd Floods of 2022

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By Douglas Mulangwa – d.mulangwa@pgr.reading.ac.uk

Between 2019 and 2024, East Africa experienced one of the most persistent high-water periods in modern history: a flood that simply would not recede. Lakes Victoria, Kyoga, and Albert all rose to exceptional levels, and the Sudd Wetland in South Sudan expanded to an unprecedented 163,000 square kilometres in 2022. More than two million people were affected across Uganda and South Sudan as settlements, roads, and farmland remained inundated for months.

At first, 2022 puzzled stakeholders, observers and scientists alike. Rainfall across much of the region was below average that year, yet flooding in the Sudd intensified. This prompted a closer look at the wider hydrological system. Conventional explanations based on local rainfall failed to account for why the water would not recede. The answer, it turned out, lay far upstream and more than a year earlier, hidden within the White Nile’s connected lakes and wetlands.

Figure 1: Map of the White Nile Basin showing delineated sub-catchments, lakes, major  rivers, and the Sudd Wetland extent. Sub-catchments are labelled numerically (1–15) with names listed in the legend. Observation stations (A–F) mark key hydrological data collection  locations used in this study: Lake Victoria (A), Lake Kyoga (B), River Nile at Masindi Port (C), Lake Albert (D), River Nile at Juba (E), and the Sudd Wetland (F). Background river networks and sub-catchment boundaries are derived from the HydroSHED dataset, and wetland extent is based on MODIS flood mask composites. The map is projected in geographic coordinates (EPSG:4326) with a graduated scale bar for accurate distance representation using UTM Zone 36N.

The White Nile: A Basin with Memory

The White Nile forms one of the world’s most complex lake, river, and wetland systems, extending from Lake Victoria through Lakes Kyoga and Albert into the Sudd. Hydrologically, it is a system of connected reservoirs that store, delay, and gradually release floodwaters downstream.

For decades, operational planning assumed that floodwaters take roughly five months to travel from Lake Victoria to the Sudd. That estimate was never actually tested with data; it originated as a rule of thumb based on Lake Victoria annual maxima in May and peak flooding in South Sudan in September/October.

Our recent study challenged that assumption. By combining daily lake-level and discharge data (1950–2024) with CHIRPS rainfall and MODIS flood-extent records (2002–2024), we tracked how flood peaks propagated through the system, segment by segment. Using an automated peak-matching algorithm, we quantified the lag between successive annual maxima peaks in Lake Victoria, Lake Kyoga, Lake Albert, and the Sudd Wetland.

The unprecedented high-water regime of 2019-2024

Figure 2: Lake Victoria water levels (1950–2024) and Sudd Wetland extents (2002–2024), with the 2019–2024 anomalous period shown in dark blue and earlier observations in black. The orange dotted line marks the pre-2019 maximum, while the solid vermillion line denotes the highest peak observed during 2019–2024. The dashed magenta line represents the reconstructed 1878 Lake Victoria peak (1137.3 m a.s.l.) from Nicholson & Yin (2001). The shaded grey band highlights the 2022 flood year, when the Sudd reached its largest extent in the MODIS record.

Between 2019 and 2024, both Lake Victoria and the Sudd reached record levels. Lake Victoria exceeded its historic 1964 peak in 2020, 2021, and 2024, while the Sudd expanded to more than twice its previous maximum extent. Each year from 2019 to 2024 stayed above any pre-2019 record, revealing that this was not a single flood season but a sustained multi-year regime.

The persistence of the 2019–2024 high-water regime mirrors earlier basin-wide episodes, including the 1961–64 and 1870s floods, when elevated lake levels and wetland extents were sustained across multiple years rather than confined to a single rainy season.  However, the 2020s stand out as the most extensive amongst all the episodes since the start of the 20th century. These data confirm that both the headwaters and terminal floodplain remained at record levels for several consecutive years during 2019–2024, highlighting the unprecedented nature of this sustained high-water phase in the modern observational era.

2019–2024: How Multi-Year Rainfall Triggers Propagated a Basin-Wide Flood

The sequence of flood events began with the exceptionally strong positive Indian Ocean Dipole of 2019, which brought extreme rainfall across the Lake Victoria basin. This marked the first in a series of four consecutive anomalous rainfall seasons that sustained elevated inflows into the lake system. The October–December 2019 short rains were among the wettest on record, followed by above-normal rainfall in the March–May 2020 long rains, another wet short-rains season in late 2020, and continued high rainfall through early 2021. Together, these back-to-back wet seasons kept catchments saturated and prevented any significant drawdown of lake levels between seasons. Lake Victoria rose by more than 1.4 metres between September 2019 and May 2020, the highest increase since the 1960s, and remained near the 1960s historical maximum for consecutive years. As that excess water propagated downstream, Lakes Kyoga and Albert filled and stayed high through 2021. Even when regional rainfall weakened in 2022, these upstream lakes continued releasing stored water into the White Nile. The flood peak that reached the Sudd in 2022 corresponded closely to the 2021 Lake Victoria high-water phase.

This sequence shows that the 2022 disaster was not driven by a single rainfall event but by cumulative wetness over multiple seasons. Each lake acted as a slow reservoir that buffered and then released the 2019 to 2021 excess water, resulting in multi-year flooding that persisted long after rainfall had returned to near-normal levels.

Transit Time and Floodwave Propagation

Quantitative tracking showed that it takes an average of 16.8 months for a floodwave to travel from Lake Victoria to the Sudd. The fastest transmission occurs between Victoria and Kyoga (around 4 months), while the slowest and most attenuated segment lies between Albert and the Sudd (around 9 months).

This overturns the long-held assumption of a five-month travel time and reveals a system dominated by floodplain storage and delayed release. The 2019–2021 period showed relatively faster propagation because of high upstream storage, while 2022 exhibited the longest lag as the Sudd absorbed and held vast volumes of water. By establishing this timing empirically, the study offers a more realistic foundation for early-warning systems.

Figure 3: Lake Victoria, Lake Kyoga, and Lake Albert water levels, and Sudd Wetland inundated extent, from 2016 to 2024. Coloured spline curves indicate annual flood-wave trajectories traced from the timing of Lake Victoria annual maxima through the downstream of the White Nile system. Blue shading on the secondary (right) axis shows 180-day rolling rainfall totals over each basin. The panel sequence (Victoria–Kyoga, Kyoga–Albert, Albert–Sudd) highlights the progressive translation of flood waves through the connected lake–river–wetland network.

Wetland Activation and Flood Persistence

Satellite flood-extent maps reveal how the Sudd responded once the inflow arrived. The wetland expanded through multiple activation arms that progressively connected different sub-catchments:

  • 2019: rainfall-fed expansion on the east (Baro–Akobo–Sobat and White Nile sub-basins)
  • 2020–2021: a central-western arm from Bahr el Jebel extending into Bahr el Ghazal and a north-western connection from Bahr el Jebel to Bahr el Arab connected around Bentiu in Unity State.
  • 2022: The two activated arms persisted so the JJAS seasonal rainfall in South Sudan and the inflow from the upstream lakes just compounded the activation leading to the massive flooding in Bentiu, turning the town into an island surrounded by water.

This geometry confirms that the Sudd functions not as a single floodplain but as a network of hydraulically linked basins. Once activated, these wetlands store and recycle water through backwater effects, evaporation, and lateral flow between channels. That internal connectivity explains why flooding persisted long after rainfall declined.

The Bigger Picture

Understanding these long lags is vital for effective flood forecasting and anticipatory humanitarian action. Current early-warning systems in South Sudan and Uganda mainly rely on short-term rainfall forecasts, which cannot capture the multi-season cumulative storage and delayed release that drive multi-year flooding.

By the time floodwaters reach the Sudd Wetland, the hydrological signature of releases from Lake Victoria has been substantially transformed by storage, delay, and attenuation within the intermediate lakes and wetlands. This means that downstream flood conditions are not a direct reflection of upstream releases but the result of cumulative interactions across the basin’s interconnected reservoirs.

The results suggest that antecedent storage conditions in Lakes Victoria, Kyoga, and Albert should be incorporated into regional flood outlooks. When upstream lake levels are exceptionally high, downstream alerts should remain elevated even if rainfall forecasts appear moderate. This approach aligns with impact-based forecasting, where decisions are informed not only by rainfall predictions but also by hydrological memory, system connectivity and potential impact of the floods.

The 2019–2024 high-water regime joins earlier basin-wide flood episodes in the 1870s, 1910s, and 1960s, each linked to multi-year wet phases across the equatorial lakes. The 1961–64 event raised Lake Victoria by about 2.5 metres and reshaped the Nile’s flow for several years. The 1870s flood appears even more extensive, showing that compound, persistent flooding is part of the White Nile’s natural variability.

Climate-change attribution studies indicate that the 2019–2020 rainfall anomaly was intensified by anthropogenic warming, increasing both its magnitude and probability. If such events become more frequent, the basin’s long-memory behaviour could convert short bursts of rainfall into multi-year high-water regimes.

This work reframes how we view the White Nile. It is not a fast, responsive river system but a slow-moving memory corridor in which floodwaves propagate, store, and echo over many months. Recognising this behaviour opens practical opportunities: it enables longer forecast lead times based on upstream indicators, supports coordinated management of lake releases, and strengthens early-action planning for humanitarian agencies across the basin.

It also highlights the need for continued monitoring and data sharing across national borders. Sparse observations remain a major limitation: station gaps, satellite blind spots, and non-public lake-release data all reduce our ability to model the system in real time. Improving this observational backbone is essential if we are to translate scientific insight into effective flood preparedness.

By Douglas Mulangwa (PhD researcher, Department of Meteorology, University of Reading), with contributions from Evet Naturinda, Charles Koboji, Benon T. Zaake, Emily Black, Hannah Cloke, and Elisabeth M. Stephens.

Acknowledgements

This research was conducted under the INFLOW project, funded through the CLARE programme (FCDO and IDRC), with collaboration from the Uganda Ministry of Water and Environment, the South Sudan Ministry of Water Resources and Irrigation, the World Food Programme(WFP), IGAD Climate Prediction and Application Centre  (ICPAC), Médecins Sans Frontières (MSF), the Red Cross Red Crescent Climate Centre, Uganda Red Cross Society (URCS), the South Sudan Red Cross Red Crescent Society (SSRCS) and the Red Cross Red Crescent Climate Centre (RCCC).

Extending the predictability of flood hazard at the global scale

Rebecca Emerton1 Comment

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

New Forecast Model Provides First Global Scale Seasonal River Flow Forecasts

Rebecca Emerton2 Comments

new_web_figure2_rivernetwork

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.

While seasonal river flow forecasting systems exist for some regions around the world, such as the U.S., Australia, Africa and Europe, the forecasts are not always accessible, and forecasts in other regions and at the global scale are few and far between.  In order to gain a global overview of the upcoming hydrological situation, other information tends to be used – for example historical probabilities based on past conditions, or seasonal forecasts of precipitation. However, precipitation forecasts may not be the best indicator of floodiness, as the link between precipitation and floodiness is non-linear. A recent paper by Coughlan-de-Perez et al (2017), “should seasonal rainfall forecasts be used for flood preparedness?”, states:

“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.”

twitter_screenshotOver 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 new seasonal outlook is produced by forcing the Lisflood hydrological river routing model with surface and sub-surface runoff from SEAS5, the latest version of ECMWF’s seasonal forecasting system, (also launched last week), which consists of 51 ensemble members at ~35km horizontal resolution. Lisflood simulates the groundwater and routing processes, producing a probabilistic forecast of river flow at 0.1o horizontal resolution (~10km, the resolution of Lisflood) out to four months, initialised using the latest ERA-5 model reanalysis.

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.

new_web_figure1_basins
The new GloFAS Seasonal Outlook Basin Overview and River Network Layers.

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.

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The new GloFAS Seasonal Outlook showing the river network and reporting points providing hydrographs and persistence diagrams.

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

Contact: r.e.emerton@pgr.reading.ac.uk