# Observed changes in flood hazard in Africa

Yves Tramblay, Gabriele Villarini, Wei Zhang

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Yves Tramblay, Gabriele Villarini, Wei Zhang. Observed changes in flood hazard in Africa. Environmental Research Letters, 2020, 15, 10.1088/1748-9326/abb90b. insu-03690237

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LETTER • OPEN ACCESS

Observed changes in flood hazard in Africa

To cite this article: Yves Tramblay et al 2020 *Environ. Res. Lett.* **15** 1040b5

View the [article online](#) for updates and enhancements.

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LETTER

# Observed changes in flood hazard in Africa

Yves Tramblay<fnref n="1" /> ID, Gabriele Villarini<fnref n="2" /> ID and Wei Zhang<fnref n="2" /> ID

E-mail: yves.tramblay@ird.fr

**Keywords:** Floods, Africa, trends, extreme precipitation

Supplementary material for this article is available [online](#)

## Abstract

Floods represent a major natural hazard in Africa, causing over 27 000 fatalities during the period 1950–2019. Despite its relevance, little is known about changes in flood hazard across this continent due to the lack of long-term high-quality streamflow records. Here we use a newly assembled discharge dataset of African rivers, and provide a long-term comprehensive view of flood hazard across this continent. We show that the annual maximum peak discharge does not exhibit a monotonic pattern, but overall decreasing trends prior to 1980 and increasing trends afterwards, especially in western and southern Africa. Our results indicate that these differing trends can be ascribed to changes in extreme precipitation around 1980. Moreover, these changes in intense precipitation pre/post 1980 are due to increased thunderstorm activity associated with enhanced convective available potential energy and zonal vertical shear driven by cooling temperature trends over western Africa. The changes in flood hazard in southern Africa can be tied to changes in Namibia low-level jet. Therefore, the observed increase in flooding since 1980 suggests that it would be beneficial to improve the monitoring, modeling and communication of flood hazard to reduce the socio-economic impacts of these events.

## 1. Introduction

Several studies have shown a possible increase in flood hazard for different regions of the globe based on both observations and future climate-driven scenarios (e.g. Alfieri et al 2017, Do et al 2017, 2020, Hodgkins et al 2017), however, there is also significant regional variability and the attribution of these changes is often contradictory (Najibi and Devineni 2018, Yin et al 2018). Even though there are continental-scale analyses to document long-term trends in floods and their potential driving mechanisms (e.g. Villarini et al 2009, Ivancic and Shaw 2015, Blöschl et al 2015, 2019, Winsemius et al 2016, Wasko and Sharma 2017, Wasko and Nathan 2020), very little is known at the African scale, and most of the published work deals with sub-regions with a limited number of stations (e.g. Di Baldassarre et al 2010, Nka et al 2015, Wilcox et al 2018). For instance, Di Baldassarre et al (2010) examined 79 series mostly located in central and north Africa spanning from 1900 to 2000; they found

no significant monotonic trends in annual maximum discharge at 65 stations, with increasing trends at only 4 hydrometric stations. Despite the lack of clearly increasing trends, they commented on the increase in flood fatalities in several African countries, especially during 1990–2009. Do et al (2017) provided a global assessment of flood trends using the Global Runoff Data Centre (GRDC) database, including 58 stations across Africa. Over the period 1955–2014, they reported a global decrease in flood magnitude, in particular in West Africa, but increasing trends for stations located in South Africa. Nka et al (2015) and Wilcox et al (2018) analyzed long term trends in 11 catchments located in West Africa; they reported an increase in flood magnitude after the 1980s at some sites, even though the flood magnitudes were smaller than what was measured in the early 1950s. Only a few studies have previously analyzed flood trends in North Africa (Abida and Ellouze 2008, Khomsi et al 2016), without detecting statistically significant trends. Africa is usually under-represented in global analyses due to the

<footnote n="1">¹ HydroSciences Montpellier (Univ. Montpellier, CNRS, IRD), 300, avenue du Professeur Emile Jeanbrau, 34090, Montpellier, France</footnote>

<footnote n="2">² IIHR—Hydroscience & Engineering, The University of Iowa, Iowa City, Iowa, United States of America</footnote>
limited number of stations with observed discharge available in databases such as the GRDC (Wasko and Sharma 2017, Yin et al 2018).

It is therefore clear that we have limited obser-
vational evidence to provide a comprehensive and
continent-wide assessment of the changes in flooding
across Africa. Here we leverage a newly compiled dis-
charge dataset to provide the first continental assess-
ment of trends in Africa, and to relate these findings to
changes in the large-scale environmental conditions
from 1950 to 2010.

## 2. Data and methods

The database used in the present work is based on
the collection of stations from the GRDC and the
SIEREM (Dieulin *et al* 2019) databases with a min-
imum of 10 full years of daily discharge data between
1950 and 2018. Years with more than 5% missing days
were discarded. In cases when different time series
existed for the same station in the different databases,
only the longest series was kept to avoid issues related
to the use of different rating curves. This work res-
ults in the largest ever built database of daily discharge
data in Africa, with a total of 1529 stations. These sta-
tions belong to different climate zones, according to
the Köppen-Geiger climate classification. The main
climate zone is Savannah (class Aw) (687 stations
located in the west and central Africa basins). The
second most represented climate zone is Steppe-hot
(Bsh), with 207 stations located in the Sahel region
and south Africa (Botswana, Namibia). The temper-
ate with dry winter classes (Cwa and Cwb) includes
187 and 125 stations respectively, located in South
Africa (Zambia, Angola, Rwanda, Mozambique, Zim-
babwe). The 98 stations belonging to the Desert-hot
class (Bwh) are mostly located in the northern and
southern boundaries of the Sahara desert. Eighty-
seven stations under a temperate climate with dry
hot summer, corresponding to Mediterranean cli-
mate (Csa), are found in North Africa and southwest-
ern part of South Africa.

Station catchments have been delineated
with the Hydroshed DEM (Lehner et al 2008)
(http://hydrosheds.cr.usgs.gov) at 15 sec resolution.
Dams and reservoirs have been extracted from the
Global Reservoir and Dam Database (GRanD) v1.3
(Lehner et al 2011) to identify regulated basins. The
number of dams included in each river basin has
been extracted. The rivers are considered regulated if
at least one dam exists in the catchment area, other-
wise the river is considered natural. It should be noted
that other structures like small dams that may not be
included in the GRanD database could be present
in the catchments classified as natural. In addition,
land cover maps from the European Space Agency
(ESA) CCI Land Cover time-series v2.0.7 (ESA 2017)

at a 300 m spatial resolution were also considered;
the number of pixels for each land cover class in
the basins has been extracted for 1992 and 2015 to
provide estimates of land cover changes. In addition
to the catchment data, the number of deaths, the pop-
ulation affected and the total damage costs related to
flood events since 1950 are extracted from the Emer-
gency Events Database (EM-DAT, CRED/UCLouvain,
Brussels, Belgium—www.emdat.be).

Annual maximum discharge values are extrac-
ted from the daily series with at least 15 complete
hydrological years (September of year n to August
of year n + 1) between 1950 and 2010 resulting
in 884 viable stations (it should be noted that the
definition of the hydrological year could influence
the detection of flood trends; Wasko et al 2020). For
trend detection, we use the Mann-Kendall test (Mann
1945) adapted to account for autocorrelation (Hamed
and Ramachandra Rao 1998). To get a quantitative
estimate of the slope of the trend, we use the Sen
slope estimator (recognizing that it implies a linear
trend, rather than a monotonic pattern). Since the
Mann-Kendall test only detects monotonic changes,
we also use the Pettitt test (Pettitt 1979) on the annual
maximum discharge values to detect change points.
The Kruskal-Wallis test (Kruskal and Wallis 1952)
is implemented to evaluate the difference in catch-
ment properties (i.e. catchment size, elevation, land
cover classes) for stations with significant trends. It
is a non-parametric method for testing whether dif-
ferent samples originate from the same distribution
and it is equivalent to a one-way analysis of variance
but relaxing the Gaussian hypothesis for the response
variables.

The atmospheric variables are obtained from
National Centers for Atmospheric Prediction and the
National Center for Atmospheric Research reana-
lysis (Kalnay et al 1996) at 2.5° × 2.5° spatial resol-
ution. These variables include surface temperature,
and three-dimension winds, temperature and specific
humidity. Daily precipitation is obtained from the
Rainfall Estimates on a Gridded Network (REGEN)
data set (Contractor et al 2020), which is a global
land-based data set starting from 1950 based on an
interpolated network of *in situ* data. The REGEN
data set combines different *in-situ* data including
the Global Historical Climatology Network—Daily
(GHCN-Daily) hosted by National Centres of Envir-
onmental Information, USA, and the Global Precip-
itation Climatology Centre made by Deutscher Wet-
terdienst, leading to a high station density compared
to other datasets. Tropical cyclone information is
obtained from the International Best Track Archive
for Climate Stewardship, including six-hourly longit-
ude, latitude, maximum sustained wind and time at
each tropical cyclone center (Knapp et al 2010). Trop-
ical cyclone locations are binned into 5° × 5° grids.
<figure><img src="image_4.png" /></figure>

## 3. Results

Figure 1(a) shows that there are statistically significant decreasing trends in annual maximum discharge at 214 out of 884 stations during 1950–2010, with increasing trends at only 60 stations. Overall, the largest changes are clustered in western and southern Africa. Based on these results, we would conclude that flooding in Africa has been decreasing since the 1950s, consistent with the results obtained by previous studies (Do et al 2017, Wasko and Sharma 2017). However, a closer observation of the time series highlights the presence of abrupt changes in these records, similar to what previously detected in some rivers in West Africa, including the Senegal and Niger basins (Wilcox hboxet al 2018, Descroix et al 2018). Statistically significant change points are detected at 168 stations, with most of the changes occurring in the 1970s and 1980s (figure S1 (https://stacks.iop.org/ERL/15/1040b5/mmmedia)). Therefore, if we split the time series before and after 1980 and perform the trend analysis on each of the two sub-series separately (Villarini et al 2009), we end up with a different story (figures 1(b)–(c)). The decreasing trends identified over the entire period were largely representative of the tendencies prior to 1980 (figures 1(a)–(b)). However, the picture is much different after 1980: most of the detected trends are now increasing, especially in western and southern Africa. For the time period 1950–1980, 100 negative trends and 42 positive trends are reported. However, 68 upward trends are detected after 1980 and only 19 decreasing (at the 5% significance level; figures 1(b) and (c)). When considering the number of deaths, the population affected and the total economic costs of floods, these quantities show increasing trends since 1950 (figure 2). The number of deaths has remained elevated but stable since 1990 with more than 6000 deaths/decade due to floods. Over the whole period

1950–2019, floods caused the deaths of 27 702 people, affecting over 82 million people, and the total costs of floods show a marked increase after the year 2000. While these impacts include changes in population, exposure and reporting, they better align with the idea that flooding in Africa has been increasing, rather than decreasing as we would have concluded based on the analyses of the entire record.

Why do we see these differences before and after 1980? We started by examining the differences in catchment properties to distinguish stations with positive or negative trends using the Kruskal-Wallis test. Stations with decreasing trends in annual maximum discharge during the period 1950–1980 have, on average, a larger catchment size and lower elevation than catchments with positive trends. For the time period 1981–2010, robust comparisons cannot be made since there are only 19 catchments with decreasing trends. The presence of dams and reservoirs does not strongly influence flood trends, since only one third of the basins with decreasing floods during the two sub-periods are regulated by at least one dam or reservoir. However, the presence of dams is respectively higher (smaller) in the sample of stations with decreasing (increasing) trends by comparison with the stations without trends. These findings are consistent with previous studies in other regions, which described larger catchments being more prone to decreasing floods (Ivancic and Shaw 2015, Do et al 2017, Sharma et al 2018) due to a greater influence of soil moisture storage (Nathan and Wasko, 2019), and similar trends in regulated or natural catchments (Ficklin et al 2018). No relationships could be found between flood trends and the land cover of the basins, highlighting the complex interplays between catchment morphology, land use and land cover with climatic variability.

While it is hard to identify physical drivers associated with these basins as responsible for the observed
<figure><img src="image_5.png" /></figure>

trends, changes in the weather and climate of this continent provide a framework to interpret these results. Total precipitation at the annual or seasonal scales decreased between 1950–1980 and 1981–2010 (figure S2). Analyses of extreme precipitation, on the other hand, tell a different story (figure 3). There are decreasing trends in the 99th percentile of the seasonal precipitation distribution during 1950–1980, while the opposite is true for the 1981–2010 period (see also Harrison et al. 2019). These

results are consistent during both June-July-August (JJA) and December-January-February (DJF), and the strongest signal is in West Africa, consistent with what was found by Panthou et al. (2018) and Taylor et al. (2017) and during JJA. Therefore, the precipitation analyses indicate that the observed reversal of the trends in flood extremes pre- and post-1980 is not consistent with changes in the total amount of precipitation, but rather with changes in extreme precipitation, especially in West Africa.
<figure><img src="image_6.png" /></figure>

The next question we ask is related to the drivers responsible for the observed changes in extreme precipitation. Figure 4 shows that these results can be explained in terms of changes in the environmental conditions conducive to summertime thunderstorms and mesoscale convective systems. In JJA during 1950–1980, convective available potential energy (CAPE) shows increasing trends in southern

West Africa, and decreasing trends north of it (figure 4(a)). The situation is very different, however, during 1981–2010: the decreasing trends are much more muted, and have been replaced by increasing trends, matching well the region exhibiting increasing trends in flooding and extreme precipitation post-1980 (figure 1(c)). These findings are further supported by the analyses of the zonal vertical shear, leading to
<figure><img src="image_7.png" /></figure>

more favorable conditions for the enhancement of mesoscale convective activity during 1981–2010 compared to 1950–1980 (figure 4, bottom panels). From a climate dynamics perspective, these results can be tied to a cooling trend in western Africa in JJA during

1981–2010. Climatologically, the mean temperature in the Saharan region is higher than the Sahelian region during JJA (Cook and Vizy 2015), causing a meridional temperature gradient (north high and south low) (figure S3). Moreover, we find negative
temperature trends in the Sahelian region and posit-
ive trends in the Saharan regions during JJA of 1981–
2010 (figure S3). The Saharan/Sahelian warming/
cooling trends may lead to an increase in the meridi-
onal temperature gradient, which may be responsible
for the increasing vertical shear based on the thermal
wind relationship (figure S3).

If we switch our attention to southern Africa, we
detect a reversal in trends pre/post 1980, even though
of a smaller magnitude compared to West Africa. The
trends in extreme precipitation are different between
the two sub-period in DJF (figure 3, bottom panels).
The trends in southern Africa can be explained in
the context of the Namibia low-level jet, which trans-
ports moisture in south-west Africa (figure S4). Com-
paring the intensity of the jet before and after 1980,
we observe that there is an enhancement of moisture
transported on land in the most recent decades, lead-
ing to an enhancement in extreme precipitation over
that part of the continent. Analyses of tropical cyc-
lone activity in south-eastern Africa do not point to a
significant difference between the two periods (figure
S5).

4. Conclusions

This study provided a comprehensive view of the
changes in flood hazard across Africa, and showed
that flood extremes have not been changing mono-
tonically. We were able to attribute these changes to
extreme precipitation associated with thunderstorms
and mesoscale convective systems in West Africa, and
to changes in the Namibia low-level jet in southern
Africa. Based on these results, flood hazard in Africa
can alternate between quiet and active periods in
response to the large-scale environmental conditions.
A careful assessment of future climate conditions will
be necessary to get a better indication of the flood haz-
ard for this continent during the 21st century. In par-
ticular, we showed that total and extreme precipita-
tion can have opposite trends, with the latter playing
an important role in explaining the trends in flood-
ing. This has important implications as we consider
what the future flood hazard for Africa may shape up
to be. For instance, recent climate scenarios (Kendon
et al 2019) indicate that most of tropical Africa is pro-
jected to experience more intense precipitation, while
at the same time a reduction in wet-season rainfall.
Therefore, according to our findings, it is also likely
that flooding would be projected to increase.

Here we were able to attribute the observed
changes in flood extremes largely to the climate sys-
tem; when we tried to frame them in the context of
changes that happened within the watersheds, we did
not find that they could explain the differences in
trends among different basins. With that said, future
work should evaluate in more details the role played
by changes in the landscape, land use/land cover, and

damming, as we expect that they modulate the hydro-
logic response to the observed changes in extreme
precipitation (Laraque et al 2001, Descroix et al 2009,
2012, 2018, Mahe et al 2013, Sidibe et al 2018). Given
the high vulnerability of African countries to flooding,
the results highlight the benefits of improving monit-
oring, modeling and communication of the flood risk
across this continent to mitigate the adverse impacts
of future floods.

Acknowledgments

This material is based upon research supported by
the Thomas Jefferson Fund of the Embassy of France
in the United States and the FACE Foundation. The
authors would like to thank Nathalie Rouche and
Claudine Dieulin for the SIEREM data extraction and
processing. The data provided by the Global Run-
off Data Centre, 56068 Koblenz, Germany is also
acknowledged. Villarini also acknowledges the sup-
port of the U.S. Army Corps of Engineers' Institute
for Water Resources.This work is dedicated to the
memory of Claudine Dieulin who passed away in
January 2020 during the course of this project.

Author contributions

YT and GV designed the experiments; YT and WZ
performed the analyses. All authors interpreted the
results and wrote the paper.

Competing interests

Nothing to declare.

Data availability statement

The data that support the findings of this study are
available upon reasonable request from the authors.

ORCID iDs

Yves Tramblay https://orcid.org/0000-0003-0481-5330

Gabriele Villarini https://orcid.org/0000-0001-9566-2370

Wei Zhang https://orcid.org/0000-0001-8134-6908

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