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\begin{document}

\title{Historical changes in rainfall patterns over the Congo basin and impacts
on runoff (1903-2010)}



\author[1]{Christopher E. Ndehedehe}%
\author[2]{Nathan O. Agutu}%
\affil[1]{Australian Rivers Institute and School of Environment \& Science, Griffith University, Nathan, Queensland 4111, Australia}%
\affil[2]{Department of Geomatic Engineering and Geospatial Information Systems JKUAT, Nairobi, Kenya.}%


\vspace{-1em}



  
  \date{June 10, 2024}


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\selectlanguage{english}
\begin{abstract}
The Congo basin is one of the most hydrologically active and pristine
locations with limited understanding of how precipitation changes
impacts on stream flow dynamics and variations in catchment stores.
Given that the basin is among the three prominent convective regions
that dominates global rainfall climatology during transition seasons,
historical space-time variability of rainfall (1901-2014) over the basin
in relation to river discharge is analyzed in order to understand
significant hydro-climatic shift. Based on advance multivariate
analyses, the total variability of the leading modes (annual variations)
of rainfall increased during the 1931-1960 (56.3\%) and 1961-1990
(57.3\%) periods compared to the 1901-1930 baseline period (51.3\%). It
varied less between 1991 and 2014 (55.4\%) as opposed to the two
climatological periods between 1931 and 1990. Furthermore, the total
variability in the multi-annual rainfall signals declined from 16.5\% at
the start of the century (1901-1930) to 13.6\% in the 1991-2014 period
while the total variability accounted for by other short-term
meteorological signals oscillated between 4.0\% and 2.7\% during the
entire period. Between 1995 and 2010 there seems to be a change in the
hydrological regimes of the Congo river as the cumulative departures of
rainfall and discharge were in opposite directions. The considerable
association of discharge with rainfall in catchments characterized by
strong annual and seasonal amplitudes in rainfall implies that the
wetland hydrology of the basin is largely nourished by rainfall, in
addition to possible exchange of fluxes within the Congo floodplain
wetlands. Notably, a significant proportion of changes in the dominant
rainfall patterns is still not explained by those of river discharge.
This information signals the threshold of complex hydrological processes
in the region, and perhaps suggest the influence of anthropogenic
contributions (e.g., deforestation) and strong multi-scale
ocean-atmosphere phenomena as key secondary drivers of hydrologic
variability.%
\end{abstract}%



\sloppy


\textbf{1. Introduction}

Rainfall is a major source of freshwater availability for the
functioning of ecosystems and sustainability of both hydrological and
agricultural systems. Indeed, rainfall is an important aspect of the
hydrological cycle that shows an increasing acceleration owing to
climate change. As the climate warms, the hydrological cycle
accelerates, causing an increase in the spatiotemporal variability of
precipitation and also in the duration and intensity of extreme events
such as droughts, storms, and floods (e.g., Ndehedehe, 2019). The need
to assess the response of surface water hydrology to changes in climate
is therefore crucial.

The rise in extreme events, especially drought frequency, duration and
severity across several African domain in recent times (see, e.g.,
Ndehedehe et al., 2019; Haile et al., 2019; Mpelasoka et al., 2018;
Agutu et al., 2017; Ndehedehe et al., 2016b,c; Hua et al., 2016;
AghaKouchak, 2015; Shiferaw et al., 2014; Zhou et al., 2014) are strong
indications of climatic disturbance and perhaps the increasing
vulnerabilities of the African sub-region to the impacts of extreme
climatic conditions. These extreme drought events have been the direct
result of prolonged limited or below average rainfall. Among other
factors, they have been attributed mostly to the perturbations of the
surrounding oceans, natural variability, important processes of oceanic
variability and human actions, e.g., land use change, deforestation,
among others (e.g., Haile et al., 2019; Ndehedehe, 2019; Nicholson et
al., 2018; Andam-Akorful et al., 2017; Ndehedehe et al., 2016c,b; Epule
et al., 2014). Based on regional climate model simulation, deforestation
in the Congo basin, for example, will locally produce a heat low and
lead to reduced precipitation (Nogherotto et al., 2013). Assessing
evolutionary changes in rainfall patterns is therefore essential to
improve knowledge on the interactions between climate systems and
globally significant hydro-ecological domains such as the Congo basin.

The critical hydrological features of the Congo basin make it one of
those globally significant domains given it key roles in global climate
and the huge ecosystem services it provides on mutli-scales (e.g.,
Ndehedehe et al., 2018; Bell et al., 2015; Verhegghen et al., 2012;
Washington et al., 2013). For example, the sensitivity of climate to the
loss of the Congo basin rainforest and other ecological disturbance
through extensive land cover change and human interaction with the
ecosystem is well known (e.g., Bell et al., 2015; Malhi et al., 2013;
Verhegghen et al., 2012). Numerical simulations from climate models have
demonstrated the importance of the Congo forest to its local hydrology.
For instance, the study by Bell et al. (2015) show that deforestation in
the Congo basin results in increase albedo and will lead to cooler and
drier climate conditions over the entire basin. They noted that the
absolute depletion of the Congo basin forest will decrease rainfall by
42\% in the western part with a slight increase of 10\% in the basin's
eastern section. This ecological disturbance of the African rainforest
biome could have implications on regional hydrology, especially
modulating local rainfall regimes. In fact, it is argued that more than
50\% of the total atmospheric moisture contribution to local
precipitation emanates from within the Congo basin (Sor\selectlanguage{ngerman}í et al., 2017).
Furthermore, the Indian

Ocean and evaporation from the Congo basin are perceived as important
moisture sources for the basin. Dyer et al. (2017) argued that the
Indian Ocean contributes about 21\% of the moisture while 25\% of the
moisture is recycled within the Congo Basin. They also noted that much
of the wet season Congo basin rainfall was derived from the Indian Ocean
moisture, stressing the need to understand links between circulation
patterns over the Indian Ocean and the local circulation over the basin
to aid the optimisation of future climate projections.

The Congo basin (Figs. 1a-c) is one of the most hydrologically active
and pristine locations in the world with limited understanding of how
precipitation changes impact on stream flow dynamics and variations in
catchment stores. Although rainfall variability in different climatic
zones of Congo-Brazzaville over the common period 1932--2007 has been
studied (Samba and Nganga, 2012), limited and lack of complete and/or
continuous observational data is a key challenge to quantitative
assessment and characterization of the Congo river basin hydrology
(Munzimi et al., 2015; Conway et al., 2009). This was the basis for
assessing the performance of satellite precipitation in the Democratic
Republic of Congo (Munzimi et al., 2015) and several other studies that
relied on multi-satellite and global reanalysis data to aid the
characterization of surface water hydrology, hydro-climatic, and land
surface conditions in the region (see, e.g., Ndehedehe et al., 2018,
2019; Zhou et al., 2014; Lee et al., 2011, 2014; Nogherotto et al.,
2013; Asefi-Najafabady and Saatchi, 2013; O'Loughlin et al., 2013;
Conway et al., 2009; Crowley et al., 2006).

While simulating discharge of the Congo River is challenging (Santini
and Caporaso, 2018), semi-distributed rainfall-runoff models have been
employed to understand processes of runoff generation and study the
impacts of climate and human actions on water resources availability
(e.g., Tshimanga and Hughes, 2014). However, the response of the Congo
river to historical changes in rainfall patterns over different
climatological periods (1903-2010) is not well known. Apart from the
lack of sufficient gauge data for hydrological applications, Tshimanga
and Hughes (2014) also argued that the complexity of natural processes
limits our understanding of surface water hydrology. The connection
between Congo spring discharge and Gulf of Guinea SST (Materia et al.,
2012) and the coupled interactions of the nearby oceans with
precipitation patterns, provide important considerations for the
rainfall-discharge relationship over the Congo basin. A key hypothesis
this study aims to address is that `despite known variations in the
discharges of the Congo river, previous rainfall amounts have varied
comparatively less across the Congo basin'. This important hypothesis
requires an assessment of the historical relationship of changes in
river discharge with those of precipitation. In light of the proposed
water transfer from the Congo basin to nourish the Lake Chad (see, e.g.,
Ndehedehe et al., 2016b; Lemoalle et al., 2012), understanding key
hydrological metrics related to rainfall-runoff relationship are
important issues to be considered. In addition to environmental impact
assessments of this project on the donor basin, addressing technological
and socio-cultural constraints that may impede the actualization of this
project are also crucial. Ultimately, the knowledge of evolutionary
patterns of river flow and potential socio-hydrological problems that
could be linked to climate change can be used to improve diplomacy and
regional cooperation by stake holders and riparian countries of both
river basins to adapt and seek for innovative solutions on this project.

Given that the basin is among the three prominent convective regions
that dominates global rainfall climatology during transition seasons
(e.g., Washington et al., 2013), historical space-time variability of
rainfall (1901-2014) over the basin in relation to river discharge is
analysed in order to understand significant hydro-climatic shift. As we
still know little about the nature of climate and anthropogenic
influence on the basin, outcomes from model simulation studies, though
largely contrasting (e.g., White and Toumi, 2014; Materia et al., 2012),
are insufficient, warranting more research. In fact, the skills of
several global climate models to simulate historical precipitation over
central Africa is considerably limited (Aloysius et al., 2016). Hence,
assessing the unique interactions between rainfall and discharge during
the last century is essential to improve our understanding of the likely
threats of climate change on hydro-ecological assets and freshwater of
the Congo basin.

\textbf{{]}Figure 1{]}}

\textbf{2. Materials and method}

\emph{2.1. Precipitation}

The Global Precipitation Climatology Centre (GPCC, Schneider et al.,
2014; Becker et al., 2013) was used to assess the spatial and temporal
patterns of rainfall over the Congo basin. The data provides reliable
monthly gridded data sets of global land-surface precipitation at
0.5\selectlanguage{english}\emph{*} x 0.5\selectlanguage{english}\emph{*} , covers the period from 1901 to 2014, and can
be downloaded from the GPCC data portal
(www.ftp.dwd.de/pub/data/gpcc/html/downloadgate.html). The updated CRU
precipitation data (Harris et al., 2014) was also used in this study.
This version of the data (CRU TS3.10) includes the number of stations
used in the interpolation, thus allowing an objective determination of
the reliability of values. Apparently, the two datasets go back as far
as 1901 and we acknowledge the limitations of GPCC gauge distribution
over the Congo basin, which may not be as dense as other tropical
regions. However, the GPCC has more gauge locations than CRU over the
region. Given that this study is over the entire Congo basin region
(about 3.7 million km2), the spatial resolution of 0.5\selectlanguage{english}\emph{*} is
suitable in resolving the sources of variability in the Congo region.
Also, the error variance in GPCC and CRU is significantly less compared
to satellite products (Ndehedehe et al., 2019). We used the GPCC data in
all our analysis because of its reliability and as it is the most
commonly used product with more ground observations from stations
worldwide in the validation of global precipitation data sets. But a key
limitation of the CRU data as highlighted by its data providers is
uncertainties and unreliability in estimated trends. Overall, the
uncertainties and ambiguities surrounding the Congo precipitation is
still an ongoing discussion and it is open to further conversation in
future studies.

\emph{2.2. River discharge}

The river discharge used in this study was obtained from the GRDC
(Global Runoff Data Centre) archive (www.bafg.de/GRDC). The gauge
station at the Congo Kinshasa station was used to study hydrological
response of the Congo river to rainfall conditions. GRDC has several
discontinuous and abandoned gauges distributed across the basin,
however, the gauge at the Congo river in Kinshasa station has complete
historical data (1903\selectlanguage{english}\emph{?} 2010) with no gaps. Because of its
numerous discharges from tributaries that originates from Eastern
Africa, this hydrological station is one of the most significant in the
basin. Also, this gauge station showed considerable strong amplitude in
monthly discharge and explained considerable multi-annual changes (more
than 80\% of variability) in the GRACE-hydrological signal of the Congo
basin Ndehedehe et al. (2018). It is therefore logical to conclude that
the discharge at Kinshasa station encapsulates most of the flows within
the basin. Moreover, our analyses of observed relationship of discharge
with rainfall were based on the common period for which both data are
available.

\emph{2.3. Evolutionary patterns of rainfall and discharge}

Three different multivariate methods were employed to analyse the
spatial and temporal patterns of rainfall and discharge. First, the
principal component analysis (PCA, e.g., Jolliffe, 2002) was used to
decompose time series of GPCC-based precipitation into spatial and
temporal patterns. PCA is an important tool to assess the
spatio-temporal variability of continuous climate data such as rainfall
due to its simplicity and capability to isolate both inter-annual
signals, and long-term periodic variations (Ndehedehe et al., 2016a).
The method reduces the dimensions of multivariate data by creating new
variables that are linear functions of the original variables.
Significant modes of variability for each climatological period
(1901\emph{-} 2014) were assessed based on the statistical rotation of
PCA. To understand the spatio-temporal variability of rainfall during
each climatological period with a 30-year window, rainfall grids for
each window were decomposed using this technique. This window allows a

reasonable conclusion of climate change or climate variability impacts
on rainfall and river discharge,

especially whether the latter has changed significantly in response to
the former. The singular spectrum analysis (SSA, Ghil et al., 2002) was
employed to decompose monthly river discharge through a singular value
decomposition (SVD) of the lagged covariance matrices. The reconstructed
discharge time series (i.e., matrix of reconstructed principal
components of discharge) obtained from the SSA scheme were compared with
the leading temporal patterns of PCA-derived rainfall using correlation
and regression analyses (next sections) for the common period. Second,
to assess the relationship between local rainfall and discharge,
rainfall was further localised over the Congo basin using the
independent component analysis (ICA, e.g., Cardoso, 1999; Cardoso and
Souloumiac, 1993). The ICA method employed here is based on the JADE
(Joint Approximate Diagonalisation of Eigen matrices) algorithm, which
exploits the fourth order cumulants of the data matrix and is fully
detailed in Cardoso and Souloumiac (1993). Several applications of PCA
and ICA methods in droughts and hydro-climatic studies and localization
of groundwater signals have been documented (e.g., Agutu et al., 2017,
2019; Montazerolghaem et al., 2016; Ndehedehe et al., 2016b, 2017;
Sanogo et al., 2015). The temporal variations of rainfall associated
with the localised spatial patterns were correlated with

discharge to determine the influence of local rainfall on discharge.
Using the ICA method, rainfall grids were localised as:

\begin{equation}
\mathbf{X}_{\text{GPCC}}\left(x,\ y,\ t\right)=\ \mathbf{\text{TS}};\ \ \ \ \ \ \ \ \ \ \ \ \ \ \left(1\right)\nonumber \\
 \end{equation}

where (\emph{x, y} ), and \emph{t} are grid locations and time steps
(months), respectively. \textbf{T} is the temporal patterns and is
unit-less since it has been normalised using its standard deviation
while the spatial patterns \textbf{S} associated with \textbf{T} have
been scaled using the standard deviation of its temporal
patterns.\textbf{T} and \textbf{S} are interpreted together and
integrated to form what is traditionally called the ICA mode of
variability (e.g., Ndehedehe et al., 2017).

\emph{2.4. Multi-linear regression analysis}

The MLRA (multi-linear regression analysis), a statistical technique
used to model the relationships between a dependent variable and one or
more independent variables was used to characterize, trends, annual and
semi-annual amplitudes in rainfall. It uses a least square approach and
has been widely applied in hydrology and climate science to explain the
possible relationships between key variables (see, e.g., Ndehedehe et
al., 2016a; Rieser et al., 2010). The focus here is to separate the
harmonic components (mean annual and semi-annual amplitudes) of rainfall
over different 30-year climatological periods (1901\emph{-} 2014) and
then compare whether they are statistically different or have different
means using analysis of variance (ANOVA). ANOVA is a prominent
statistical method that is employed to assesses if the means of two or
more groups (in our case, trends, mean annual and semi-annual rainfall
patterns from each climatological period) are significantly different
from each other. In the MLRA technique, the trends and harmonic
components (mean annual and semi-annual amplitudes) for each
climatological period (1901\emph{-} 1930; 1931\emph{-} 1960;
1961\emph{-} 1990; 1991\emph{-} 2010) were compartmentalised from the
precipitation time series (\emph{P} ) through parameterizations as
(e.g., Ndehedehe and Ferreira, 2020; Rieser et al., 2010),

\textbf{P} (\emph{l, k, t} ) =\selectlanguage{greek}β\selectlanguage{english}\textsubscript{0} +
\selectlanguage{greek}β\selectlanguage{english}\textsubscript{1}\emph{t} + \selectlanguage{greek}β\selectlanguage{english}\textsubscript{2}\emph{sin} (2\selectlanguage{greek}π\selectlanguage{english}\emph{t} )
+ \selectlanguage{greek}β\selectlanguage{english}\textsubscript{3}\emph{cos} (2\selectlanguage{greek}π\selectlanguage{english}\emph{t} ) +
\selectlanguage{greek}β\selectlanguage{english}\textsubscript{4}\emph{sin} (4\selectlanguage{greek}π\selectlanguage{english}\emph{t} ) +
\selectlanguage{greek}β\selectlanguage{english}\textsubscript{5}\emph{cos} (4\selectlanguage{greek}π\selectlanguage{english}\emph{t} ) + \selectlanguage{greek}ε\selectlanguage{english}(\emph{t} )\emph{; (} 2)

where (\emph{l; k} ) are the grid locations, \emph{t} is the time
component, \selectlanguage{greek}β\selectlanguage{english}\textsubscript{0} is the constant offset,
\selectlanguage{greek}β\selectlanguage{english}\textsubscript{1}is the linear trend, \selectlanguage{greek}β\selectlanguage{english}\textsubscript{2} and
\selectlanguage{greek}β\selectlanguage{english}\textsubscript{3} account for the annual signal, \selectlanguage{greek}β\selectlanguage{english}\textsubscript{4} and
\selectlanguage{greek}β\selectlanguage{english}\textsubscript{5}represent the semi-annual signal, while \selectlanguage{greek}ε\selectlanguage{english}(\emph{t} )
is the random error term. The amplitudes of rainfall (i.e., mean annual
and semi-annual) over the region are then estimated as,

\(Annual\ =\left[\left(\ \beta 2\right)^{2}+\ \ \left(\ \beta 3\right)^{2}\ \right]^{\frac{1}{2}}\)and \(\text{\ Semi\ }Annual=\)\(\left[\left(\ \beta 4\right)^{2}+\ \ \left(\ \beta 5\right)^{2}\ \right]^{\frac{1}{2}}\)(3)

\emph{2.5. Linear rates, correlations, and cumulative departures}

A non-parametric method such as the Sen's slope (Sen, 1968) estimator
was used to estimate the linear rates in rainfall since it is robust and
resistant to outliers. Sen slope (\emph{S\textsubscript{k}} ) is the
median overall values of the whole data and is estimated as,

\begin{equation}
S_{k}=Median\left(\frac{Y_{j}-Y_{i}}{j-i}\right),\ for\ \left(1\ \leq i<j\leq n\right)\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ 4\nonumber \\
 \end{equation}

where \emph{Y\textsubscript{j}} and \emph{Y\textsubscript{i}} represents
data values at time \emph{j} and \emph{i} (\emph{j \textgreater{} i} ),
respectively while \emph{n} is the number of data. The slope can be
positive indicating increasing trend or negative, indicating decreasing
trend. Trend analysis for precipitation grids used a 20-year window
different from the MLRA of GPCC-based precipitation (Section 2.4), which
focused on trends based on 30-year climatological windows. Detecting
changes in rainfall in a relatively short window is crucial for better
understanding of significant climate trends and developing adaptation
and mitigation measures at a regional and local scale. Given that the
assessment of linear rates in rainfall is a key aspect of long-term
water resource evaluations strategy, cumulative rainfall departure
(Weber and Stewart, 2004) was estimated. The concept of cumulative
rainfall departures (Weber and Stewart, 2004) was also applied to
estimate cumulative departures of river discharge. Generally, cumulative
departures are useful when employed as a general indicator of
rainfall/discharge trends, with the upward and downward gradient
indicating relatively a rise and decline in rainfall, respectively
(e.g., Ndehedehe et al., 2017). Cumulative departures were both employed
to evaluate trends in river discharge against those of rainfall. All
temporal relationships between rainfall and discharge were based on the
Pearson's correlation coefficient and are deemed significant at
\selectlanguage{greek}\emph{α}\selectlanguage{english}= 0\emph{.} 05. Although it was not the main stay of this study,
the

grid based comparison of GPCC and CRU precipitation data was also
undertaken. Using correlation

and ANOVA in this regard provided a bit of perspective as to the
inherent differences and similarities between the widely used GPCC
observational reference and the CRU data over the region.

\textbf{3. Results}

\emph{3.1. Historical changes in precipitation patterns over the Congo
basin}

\emph{3.1.1. GPCC vs CRU based precipitation estimates}

The Congo basin precipitation estimates and its uncertainties are
important issues that sometimes results in polarized debates and
divergent opinions amongst climate researchers who have worked in the
region (Ndehedehe et al., 2019). The challenging terrain, environmental
conditions, and limited gauge observations in the Congo basin are some
reasons for the poor understanding of precipitation estimates. Although
time constraints (i.e., availability of historical observations) was a
deciding factor in our choice of precipitation, as earlier mentioned
(Section 2.1), the error variance in GPCC and CRU is significantly less
compared to satellite products. Apart from disturbance of ecological
assets and alteration to the Congo forest through prolonged drought and
deforestation, the influence of topography on the Central African
hydrology and its neighbouring East African countries cannot be
overlooked. As with the influence of topographical variability and gauge
density on precipitation products in the surrounding East African
countries (Agutu et al., 2020), the Rift valley highlands in East Africa
was earlier identified as an important driver of considerable anomaly in
precipitation over the Congo basin (Bell et al., 2015). But the
availability of in situ data to improve process-based knowledge on these
drivers and monitor these conditions, including land surface conditions,
local water recycling activity and contributions from oceanic hot spots
is an issue for the Congo basin. It is true that a history of
hydrological measurement exists in the Congo basin (Alsdorf et al.,
2016).

\textbf{{[}Figure 2{]}}

However, the decline and significant gaps in gauge measurement, coupled
with the unavailability of historical meteorological data for research
are key constraints that limits knowledge in the Congo basin. Hence, the
intricacies surrounding precipitation distribution over the Congo basin
was therefore assessed by comparing two observational reference gauged
precipitation products, the Global Precipitation Climatology Centre
(GPCC, Schneider et al., 2014) and Climate Research Unit (CRU TS4.01,
Harris et al., 2014) derived precipitation
(http://badc.nerc.ac.uk/data/cru/) using analysis of variance and other
metrics (linear rates, correlations, and cumulative departures). GPCC
and CRU precipitation estimates over the Congo basin during different
climatological periods seem to be somewhat consistent with a range of
correlation values between 0.80 and 1.00 (Fig. 2a).

\textbf{{[}Figure 3{]}}

However, correlation values between GPCC and CRU fluctuated between 0.40
and 0.80 in the low elevation areas of the Cuvette central (cf. Fig. 1b)
during the period (Fig. 2a). The considerable change in correlation
values around the Cuvette central during the different climatological
periods can be attributed to dynamics in the availability of gauged
stations. Further, the analysis of variance also confirms there is no
significant difference in the mean of these products in all
climatological periods (Fig. 2b). The spatio-temporal distribution of
meteorological stations is expected to vary, especially in regions with
poor investment in meteorological stations. GPCC is still nonetheless,
the largest gauge-observation data available globally and has been found
suitable in the characterization of agricultural and hydrological
drought in the African sub-regions, including the Congo basin (see,
e.g., Ndehedehe et al., 2019, 2020; Agutu et al., 2017).

\textbf{{[}Figure 4{]}}

\emph{3.1.2. Spatio-temporal variations and trends in precipitation}

The spatial distribution of annual and seasonal precipitation
(1901-2013) based on seasonal rainfall climatology of the region
(March-May (MAM); June-August (JJA); September-November (SON); and
December- February (DJF)) indicate considerable rainfall in the southern
and central sections of the basin in DJF and SON periods, respectively
(Fig. 3). Just for exploratory purposes, these seasonal distributions
are considered for the first and second halves of the century. There is
significant amount of rainfall all through the year in the Congo basin
except in the JJA period for the south and DJF for the north (Fig. 3).
In terms of direct water availability from rainfall, these sections of
the Congo basin, including the northern flank can be described as the
water tower of central Africa. The evidence of strong spatial
distribution of mean monthly and seasonal rainfall in the low elevation
areas of the Cuvette central and the in-terconnectedness of multiple
stream networks attest to the Congo basin being a freshwater rich
hydrological region (Figs. 3 and cf. Figs. 1a-b). Furthermore, the MLRA
and analysis of variance show that there is statistically significant
change in observed trends (\emph{F} = 442\emph{.} 7; \emph{p} =
0\emph{.} 000), annual (\emph{F} = 8\emph{.} 85; \emph{p} = 0\emph{.}
000), and semi-annual (\emph{F} = 4\emph{.} 93; \emph{p} = 0\emph{.}
002) amplitudes of rainfall during different climatological periods
(1901\emph{-} 1930, 1931\emph{-} 1960, 1961\emph{-} 1990 and
1991\emph{-} 2014) over the Congo basin (Figs. 4a-b). Consistent with
Fig. 3, the southern section of the Congo basin is characterized by
strong annual amplitudes in rainfall while towards the east, there is
considerable mean semi-annual amplitude of rainfall (Fig. 4a).

\textbf{{[}Figure 5{]}}

\textbf{{[}Figure 6{]}}

The spatio-temporal variability of rainfall using the PCA technique was
based on climatological classification similar to Fig. 4. Based on the
Bartlett's test statistics (e.g., Snedecor and Cochran, 1989) more than
five significant modes explaining non-random variations in rainfall at
95\% confidence level were identified. But for purposes of physical
interpretability, we focus on three of those rainfall modes. Leading
rainfall modes in the Congo basin are characterised by annual,
multi-annual and short-term seasonal signals (Figs. 5a-b and 6a-b).
There is a strong dipole patterns in dominant mode where strong spatial
patterns of rainfall on the opposite side of the equator are notable
(EOF-1, Figs. 5a-b and 6a-b). These spatial patterns show the different
wet seasons in the basin. From the time series associated with these
spatial patterns, the wet season (positive phase) in northern section
coincides with the dry season (negative phase) of the southern region
(EOF-1/PC-1, Figs. 5a-b and 6a-b). The multi-annual signal, which
corresponds to the bimodal rainfall patterns and short-term seasonal
signals are relatively stronger on the West and in regions encompassing
Congo, Equatorial Guinea, and Gabon (EOF-2 and EOF-3, Figs. 5a-b and
6a-b). Generally, there is significant difference in the observed
spatial loadings (averaged spatial distribution) of the first orthogonal
mode of rainfall over different climatology (EOF-1, Figs. 5a-b and 6a-b)
and confirms the extensive impacts of extreme and severe droughts, which
affected more than 50\% of the basin between 1901 and 1930 (Ndehedehe et
al., 2019). The total variability of the leading modes (annual
variations) of rainfall increased during the 1931\emph{-} 1960 (56.3\%)
and 1961\emph{-} 1990 (57.3\%) periods compared to the 1901\emph{-} 1930
baseline period (51.3\%). It varied less between 1991 and 2014 (55.4\%)
as opposed to the two climatological periods between 1931 and 1990.
Furthermore, the total variability in the multi-annual rainfall signals
declined from 16.5\% at the start of the century (1901\emph{-} 1930) to
13.6\% in the 1991\emph{-} 2014 period while the total variability
accounted for by other short-term meteorological signals oscillated
between 4.0\% and 2.7\% during the entire period.

\textbf{{[}Figure 7{]}}

\textbf{{[}Figure 8{]}}

The MLRA of rainfall for each 30-year climatological window (Fig. 4)
show that there are significant differences (\emph{F} = 442\emph{.}
7;\emph{p} = 0\emph{.} 000) in observed trends during these periods.
However, multi-decadal (e.g., 20 years) trends based on these window
lengths were also explored as part of an overall assessment of changes
in rainfall in the basin. A further analysis on the spatial distribution
of trends in annual (Fig. 7a) and maximum (Fig. 7b) rainfall confirms
the strong increase in rainfall between 1960 and 1963 in the south.
Widespread negative trends in annual rainfall during the 1981-2000 and
2001-2013 periods around the Democratic Republic of Congo and the
Cuvette central (Fig. 7a) coincides with drought/dry episodes already
reported in the literature (Ndehedehe et al., 2019; Ndehedehe, 2019;
Zhou et al., 2014). Additionally, there is inconsistency in the
distribution of spatial trends in annual and maximum rainfall (Figs.
7a-b). For example, during the 1981-2000 period there was a widespread
decrease in annual rainfall (Fig. 7a) compared to the distribution of
trends in maximum annual rainfall in the central Congo basin area (Fig.
7b).

\textbf{{[}Table 1{]}}

\textbf{{[}Figure 9{]}}

\emph{3.2. Rainfall vs river discharge}

Similar to rainfall, observed river discharge was also statistically
decomposed using the singular spectral analysis. This approach is
warranted because unlike rainfall, the discharge time series is a one
column vector unit and decomposing it similar to rainfall allows
comparison between their leading modes. The temporal patterns of the
leading modes of rainfall and discharge were compared with one another
(Figs. 8a-b). The relationship between leading modes (PC-1) of rainfall
and river discharge show marked fluctuations during the entire period
(1901-2010). The strongest linear correlation (\emph{r} = 0\emph{.} 71)
was observed during 1991-2010 period while the lowest (\emph{r} =
0\emph{.} 59) was observed at the start of the century (1903\emph{-}
1930). The other two climatological periods show moderately strong
correlations (\emph{r} = 0\emph{.} 68 and 0\emph{.} 64 for the 1930-1960
and 1961-1990 periods, respectively). Maximum correlation between the
second modes of rainfall and discharge is also linear and strong but
with a one-month phase lag. For example, the second modes of rainfall
and discharge during the 1903\emph{-} 1930 (\emph{r} = 0\emph{.} 71)

and 1991-2010 (\emph{r} = 0\emph{.} 66) were found to be well associated
at one-month lag. The total variability accounted for by the two leading
modes of rainfall and discharge are summarised in Table 1. The annual
signal in GPCC explains an average of 42.7\% of variability in discharge
while the multi-annual signal accounts for about 50\% at 1-month lag.
During all climatological periods, temporal relationships between the
first modes of rainfall and discharge are moderately strong and
significant (\emph{r} = 0\emph{.} 05). At the start of the century, more
of the variability in rainfall were rather consistent with those of
discharge (92\% and 96.7\% total variability for rainfall and discharge,
respectively) as opposed to the 1990\emph{-} 2010 period (91\% and
98.3\% total variability for rainfall and discharge, respectively) when
there was increased variability in discharge (Figs. 8a-b). Similar to
rainfall (Figs. 5a-b and 6a-b), the variability in leading modes of
discharge fluctuated during the century (between 69\% and 76\%). This
suggests that apart from rainfall, Congo river discharge is driven by
other factors, which may include the influence of human activities
through deforestation and sand mining. While the interaction and
exchange of fluxes within wetlands could disturb the variability of the
Congo river, Conway et al. (2009) confirmed the dominant control of
inter-annual and decadal rainfall variability in river flows whilst
acknowledging the key roles of human interventions. Notably, a
significant proportion of changes in the dominant discharge patterns is
still not explained by those of rainfall. This information signals the
threshold of complex hydrological processes in the region, and perhaps
suggest the influence of anthropogenic contributions and strong
multi-scale ocean-atmosphere phenomena as key secondary drivers of
hydrologic variability.

\textbf{{[}Figure 10{]}}

\textbf{{[}Figure 11{]}}

The inter-annual variations and cumulative departures of rainfall and
discharge were also explored to understand their interactions in the
Congo basin (Fig. 9). The inter-annual variations during the
1901\emph{-} 2010 period was consistent and captures the wettest period
(1960\emph{-} 1970) in the basin (Fig. 9a). Their cumulative departures
are somewhat similar but show an apparent inconsistent trend during the
1930-1950 and post 1999\emph{-} 2010 periods (Figs. 9b-c). Generally,
there seems to be a change in the hydrological regimes of the Congo
river, (especially after 1994) owing to the rather pervasive influence
of extreme droughts. This again could be the aftermath of extreme
droughts, which fluctuated between 50\% (i.e., affected areas) in the
early years of the century and 40\% during the 1994-2014 period
(Ndehedehe et al., 2019). This change is reflected in the observed
temporal relationship between maximum rainfall and discharge (Figs.
10a-d). For example, the rainfall-discharge relationship was stronger in
1961-1990 (\emph{r} = 0\emph{.} 77) and 1991-2010 (\emph{r} = 0\emph{.}
69) compared with other climatological periods when correlations were
weak (Figs. 10c and d). Moreover, the temporal series associated with
the ICA-localised spatial rainfall patterns over the Congo basin are
correlated with discharge (Fig. 11). As shown in the leading independent
modes (Fig. 11), the discharge-rainfall relationship is strongest at the
extreme south (\emph{r} = 0\emph{.} 65) and north (\emph{r} = 0\emph{.}
64) of the basin. These sections of the basin are characterised by
considerable amplitudes in annual and seasonal rainfall (EOF-1, Figs.
5a-b, 6a-b and cf. 3). The relationship (rainfall and discharge) is also
significant in other catchments where correlations of 0.56, 0.59 and
0.51 have been observed although temporal relationships of localised
rainfall at the equator with discharge are poor and insignificant
(\selectlanguage{greek}\emph{α} \selectlanguage{english}= 0\emph{.} 05). Given the trajectory of water inflow in the
Congo basin, the considerable association of rainfall with discharge
implies that the wetland hydrology of the Cuvette central is nourished
mostly by rainfall, in addition to exchanges of fluxes in the floodplain
wetlands.

\textbf{4. Discussion and conclusion}

The Congo basin is one of the most hydrologically active and pristine
locations with limited understanding of how precipitation changes
impacts on stream flow dynamics and variations in catchment stores. This
poor understanding in the hydro-climatic processes of the basin is most
often the results of gaps in hydrological information and insufficient
observational networks for hydrological applications (e.g., Ndehedehe,
2019; Tshimanga and Hughes, 2014; Conway et al., 2009). Due to limited
hydro-meteorological observation network in the basin, Munzimi et al.
(2015) mentioned that much is still unknown about the Congo river
basin's hydrological behaviour. The comparative assessment of
precipitation estimates from GPCC and CRU data over the Congo basin was
therefore undertaken to improve quantitative knowledge of Congo basin
precipitation and its influence on river discharge. There are still some
ongoing debates regarding the uncertainties and ambiguities in
precipitation estimates from various products over the Congo basin.
While that is not the focus of this chapter, results indicate both
products (GPCC and CRU) are generally consistent with high correlation
values and statistically insignificant difference in their means.
However, the poor association between

these data in the Congo Cuvette central during the period could suggest
dynamics in the availability of gauged stations. The GPCC is a widely
used observational reference data unlike the CRU precipitation. The
distribution of gauges is expected to vary in time and space and the
GPCC-based precipitation product has more gauged observations available
globally compared to the CRU-based precipitation.

The spatial distribution of rainfall in the Congo basin takes the form
of a north--south dipole patterns. These patterns result in wet and dry
seasons with opposite phase in the south and north of the basin. That
is, the wet season in the south (December-February) coincides with dry
season in the north and vice versa. The dipole patterns are caused by
the difference in seasons, the more reason why the northern section is
wet between May and October. The south on the other hand, remains
relatively dry during the same period. The dipoles and this alternating
seasonal rainfall patterns have been attributed to the position of the
Congo basin across the equator (Munzimi et al., 2015). Be it trends,
annual, or semi-annual amplitudes, rainfall during different
climatological periods (1901-1930, 1931-1960, 1961-1990 and 1991-2014)
over the Congo basin has undergone significant changes. These changes
were also echoed by Samba and Nganga (2012) who reported on considerable
declines in Congo rainfall between 1980 and 1990. The surrounding
oceans, especially the Indian Ocean have been identified as an important
source of observed precipitation experienced in wet years in the Congo
basin (Dyer et al., 2017). Generally, the Congo basin and much of
central Africa are hydrologic hotspots or web of climatic influence. For
instance, the SST anomalies along the Benguela Coast, and warming or
cooling of the three oceans (Atlantic, Indian, and Pacific) and indices
of oceanic variability have been identified as key factors that govern
the complex changes in precipitation patterns over West Central Africa
(e.g., Ndehedehe et al., 2019; Balas et al., 2007).

As opposed to West Africa where 3-4 significant modes of rainfall
variability have been identified (e.g., Ndehedehe et al., 2016a; Sanogo
et al., 2015), there are actually more than five statistically
significant modes of orthogonal rainfall modes of variability over the
Congo basin. Physical interpretation and attribution could be
challenging for some of these modes. However, we found that rainfall in
the Congo basin is characterised by annual, multi-annual (or bimodal),
trends, short term seasonal signals and those resulting from regional
factors and atmospheric-ocean interactions and large-scale processes.
Locally generated moisture and the interactions of the nearby oceans
(Sor\selectlanguage{ngerman}í et al., 2017; Dyer et al., 2017) with land surface processes in
the basin could also contribute to the magnitude of seasonal variations
observed in the Congo rainfall. Sorí et al. (2017) argued that the
various sources of moisture for the Congo basin makes nourishment
possible during extreme climate events (e.g., flood) thus modulating
water balance within the region. On the one hand, this could be the
reason for significant amount of rainfall in the Congo basin all through
the year except during the June-July period in the southern catchment.
On the other hand, the observed multiple significant modes of the Congo
basin rainfall can be attributed to signals emanating from the combined
influence of climate, natural

variability, ocean interactions and the influence of land surface
processes. As an example of the influence of land surface conditions on
Congo precipitation, locally generated heat low and reduced
precipitation were identified as the direct consequence of decreased
evaporation over deforested area in Central Africa (Nogherotto et al.,
2013). Arguably, this reassert the important role of land surface
processes in the Congo basin (Koster et al., 2004). In this era of
anthropogenic-induced climate change, the skills of global and regional
climate models in reproducing the Congo basin climatology (spatial
patterns, seasonality, and magnitude of precipitation) could be
restricted due to uncertainties (Aloysius et al., 2016). However, the
question of how the interactions of increasing anthropogenic pressure on
the Congo forest and important processes of inter-annual variability
impact surface water hydrology are therefore interesting future research
directions.

So, has rainfall amounts varied comparatively less across the Congo
basin despite known variations in the Congo river flow? In the analysis
of historical space-time variability of rainfall (1901\emph{-} 2014)
over the Congo basin we found that previous rainfall (1931\emph{-} 1990)
varied more compared to the 1991-2014 period. As opposed to the two
climatological periods between 1931 and 1990, rainfall varied less
between 1991 and 2014. It is not clear if the time slice during this
last climatological period contributed to this as it was not up to 30
years. However, there were more dry spells and drought events during
this period (1991 and 2014) in the Congo basin compared to the last two
climatological periods prior to 1991 (Ndehedehe et al., 2019; Hua et
al., 2016). The strong positive anomalies of rainfall in extreme wet
years are usually captured in the annual component and show strong
spatial distributions (loadings) in the northern and southern sections.
Although the analysis for the latter climatological period used only 23
years (1991-2014), the spatial distribution and amplitudes of rainfall
is consistent and similar to other periods with relatively higher modes
of variability. Generally, there seems to be a shift in the hydrological
regimes of the Congo river, (especially after 1994). This change can be
attributed to the rather pervasive influence of extreme droughts, which
fluctuated between 50\% (i.e., affected areas) in the early years of the
century and 40\% during the 1994-2014 period (Ndehedehe et al., 2019).
To further support this argument, the rainfall analysis by Samba and
Nganga (2012) in Congo-Brazzaville (Congo) during 1932--2007 period
indicates that the largest rainfall deficits were observed in the 1980s
and 1990s. And since 1985, Zhou et al. (2014) observed a consistent
drying of the Congolese forest, which was attributed to gradual decline
in precipitation. The change in rainfall trajectory in the post 1994
period is consistent with observed drought and drying in the region. In
view of inconsistent trends between rainfall and discharge as evidenced
in their cumulative departures, this suggests other key secondary
drivers of hydrologic variability that are non-climatic exist in the
basin.

The considerable association of discharge with rainfall in catchments
characterised by strong annual and seasonal amplitudes in rainfall
implies that the wetland hydrology of the basin is largely nourished by
rainfall, in addition to possible exchange of fluxes within the Congo
floodplain wetlands. Notably, a significant proportion of changes in the
dominant rainfall patterns is still not explained by those of river
discharge. This information signals the threshold of complex
hydrological processes in the region. Importantly, it also suggests the
influence of anthropogenic contributions (e.g., deforestation, changes
in land cover states, surface water developments, etc.) and strong
multi-scale ocean-atmosphere phenomena as key secondary drivers of
hydrologic variability. Ultimately, it is obvious nonetheless, that
several African regions have been identified as a hot spot with
considerable influence of several climate teleconnections (e.g., El-Niño
Southern Oscillation and Atlantic Multi-decadal Oscillation, Indian
Ocean Dipole, etc.). Given the impacts of these multi-scaled climate
indices

on rainfall variability and extreme climatic events (droughts and
floods) in Africa (see, e.g., Ndehedehe et al., 2020; Gizaw and Gan,
2017; Diatta and Fink, 2014; Paeth et al., 2012), the combined influence
of climate and human activities on hydrological variability is thus
palpable and multi-faceted.

\textbf{Acknowledgments}

Christopher is grateful to the American Geophysical Union (AGU) grant
sponsored by NASA and National Science Foundation in collaboration with
The Ohio State University and several other international agencies. This
funding supported his keynote speech at the AGU Chapman conference held
in Washington DC, USA, in September 2018. The authors further thank
NASA, NOAA, and GRDC for all the data used in this study.

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\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

Figure 1: Study region showing the Congo basin. The Congo river and its
numerous tributaries (i.e., blue lines on the left) are also indicated.
(a) The aerial averaged monthly rainfall (mm), (b) topographic map of
the Congo basin adapted from
https://commons.wikimedia.org/wiki/File:Congo\_Kinshasa\_Topography.png,
and (c) the location of the Congo basin in Africa. The blue lines and
polygons in (b) are the river networks and water bodies in the Congo
basin.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

Figure 2: Relationship between GPCC and CRU-based precipitation over the
Congo basin during different climatology. (a) Spatial distribution of
correlation coefficients (\emph{r} ) and (b) analysis of variance
(temporal patterns) between GPCC and CRU.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

Figure 3: Spatial distribution of GPCC-based precipitation (mm) over the
Congo basin during the 1901\emph{-} 1960 and 1961-2013 periods. The
seasonal precipitation (March-May (MAM); June-August (JJA);
September-November (SON); and December- February (DJF)) are classified
based on the rainfall climatology of the region and is adapted with full
permission from Ndehedehe et al. (2019).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

Figure 4: Analysis of (a) trends (mm/yr) and harmonic components (mean
annual and semi-annual amplitudes) of rainfall (mm) based on the
multiple linear regression (MLRA) technique and (b) their (trend, annual
and semi-annual amplitudes) corresponding means over different
climatology using analysis of variance.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

Figure 5: Spatial and temporal variability in the Congo basin rainfall
(mm). The average spatial distributions of rainfall (top panels) and
their corresponding temporal patterns (bottom panels) are indicated for
different climatological periods (30-year interval). (a) Changes in the
Congo precipitation during the (a) 1901-1930 and (b) 1931-1960. Spatial
(EOF) and temporal (PCs) patterns are jointly interpreted. The total
variability accounted for by each orthogonal modes of rainfall are
expressed in percentages while the y-axes of the PCs are in standardised
units.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image6/image6}

\end{center}
\end{figure}

Figure 6: Spatial and temporal variability in the Congo basin rainfall
(mm) similar to Fig. 5. (a) Changes in the Congo precipitation during
the (a) 1961\emph{-} 1990 and (b) 1991\emph{-} 2014. The total
variability accounted for by each orthogonal modes of rainfall are
expressed in percentages while the y-axes of the PCs are in standardised
units.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image7/image7}

\end{center}
\end{figure}

Figure 7: Spatial distribution of trends (mm/yr) in (a) annual and (b)
maximum annual rainfall (mm) over the Congo basin. Estimated rainfall
trends are based on 20 years interval except for the 1960\emph{-} 1963
and 2001\emph{-} 2013 periods.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image8/image8}

\end{center}
\end{figure}

Figure 8: Dominant modes of river discharge time series of the Congo
river between 1903 and 2010. The leading

modes (i.e., the annual variations and multi-annual variations) of river
discharge are compared with those of

precipitation. The time series of river discharge are the reconstructed
expansion coefficients.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image9/image9}

\end{center}
\end{figure}

Figure 9: Inter-annual variations and cumulative departures of rainfall
and discharge. (a) The temporal variations of rainfall and discharge
anomalies, (b) the cumulative departures of rainfall and discharge, and
(c) the cumulative departures of rainfall and discharge after applying
the local least-squares polynomial approximation-based filter of
Savitzky and Golay (1964) to smoothen out the effects of low frequency
noise.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image10/image10}

\end{center}
\end{figure}

Figure 10: Temporal relationship between rainfall and discharge during
the 1903-2010. This relationship is based on (a-d) maximum rainfall and
discharge during different climatological windows.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image11/image11}

\end{center}
\end{figure}

Figure 11: Localised rainfall (mm) over the Congo basin (1991\emph{-}
2010). The independent patterns (i.e., temporal patterns associated with
this localized spatial maps) of rainfall from the ICA decomposition were
compared with time series of river discharge.

Table 1: Percentages (\%) of observed variance explained by leading
rainfall and river discharge orthogonal modes during different
climatological periods in the last century (1901-2010).\selectlanguage{english}
\begin{longtable}[]{@{}llllll@{}}
\toprule
\textbf{Variable} & \textbf{Modes} & \textbf{1901-1930} &
\textbf{1931-1960} & \textbf{1961-1990} &
\textbf{1991-2010}\tabularnewline
\midrule
\endhead
Rainfall & PC-1 & 59.1 & 62.3 & 63.2 & 60.2\tabularnewline
& PC-2 & 32.8 & 31.7 & 31.9 & 31.0\tabularnewline
Discharge & PC-1 & 69.3 & 69.6 & 76.1 & 75.5\tabularnewline
& PC-2 & 27.4 & 27.2 & 21.4 & 22.8\tabularnewline
\bottomrule
\end{longtable}

\selectlanguage{english}
\FloatBarrier
\end{document}

