Precipitation time series of the 19 meteorological stations satisfied the above criteria; i.e., all-time series have 10-year minimum length with a missing data gap, if any, of the 12-month uninterrupted length at maximum. The layout of the meteorological stations in the Seyhan River basin is shown in Figure 2 from which it is seen that the stations are scattered over the basin almost homogeneously.

<table><caption>Table 1. Meteorological stations in the Seyhan River basin.</caption><thead><tr><th>Code</th><th>Station Name</th><th>Latitude</th><th>Longitude</th><th>Altitude (m)</th><th>Institution</th><th>Observation Period</th><th>Missing Data (month)</th><th>No-Rainy Months (%)</th><th>Mean (mm)</th><th>Min (mm)</th><th>Max (mm)</th></tr></thead><tbody><tr><td>6204</td><td>Tufanbeyli</td><td>38.2600</td><td>36.2195</td><td>1415</td><td>MGM</td><td>1998–2012</td><td>3</td><td>6.67</td><td>545</td><td>312</td><td>706</td></tr><tr><td>6560</td><td>Saimbeyli</td><td>37.9811</td><td>36.0853</td><td>1050</td><td>MGM</td><td>1986–1995</td><td>4</td><td>4.17</td><td>923</td><td>625</td><td>1233</td></tr><tr><td>6893</td><td>Çamardı</td><td>37.8358</td><td>34.9975</td><td>1603</td><td>MGM</td><td>1969–1982</td><td>1</td><td>7.74</td><td>412</td><td>316</td><td>546</td></tr><tr><td>6902</td><td>Feke</td><td>37.7764</td><td>35.9000</td><td>583</td><td>MGM</td><td>1970–1993</td><td>1</td><td>4.86</td><td>910</td><td>598</td><td>1352</td></tr><tr><td>17351</td><td>Adana Bölge</td><td>37.0041</td><td>35.3443</td><td>23</td><td>MGM</td><td>1960–2016</td><td>0</td><td>11.40</td><td>663</td><td>317</td><td>1265</td></tr><tr><td>17802</td><td>Kayseri</td><td>38.7251</td><td>36.3904</td><td>1542</td><td>MGM</td><td>1963–2009</td><td>5</td><td>4.08</td><td>423</td><td>267</td><td>597</td></tr><tr><td>17837</td><td>Pınarbaşı</td><td>38.4522</td><td>35.7912</td><td>1402</td><td>MGM</td><td>1965–2010</td><td>8</td><td>4.53</td><td>408</td><td>269</td><td>585</td></tr><tr><td>17840</td><td>Tomarza</td><td>38.4781</td><td>36.5035</td><td>1599</td><td>MGM</td><td>1968–2011</td><td>0</td><td>5.30</td><td>524</td><td>354</td><td>748</td></tr><tr><td>17906</td><td>Uluışla</td><td>37.5480</td><td>34.4867</td><td>1453</td><td>MGM</td><td>1962–2011</td><td>11</td><td>6.33</td><td>322</td><td>182</td><td>428</td></tr><tr><td>17934</td><td>Pozantı</td><td>37.4758</td><td>34.9022</td><td>1080</td><td>MGM</td><td>1963–1992</td><td>24</td><td>6.11</td><td>719</td><td>380</td><td>1299</td></tr><tr><td>17936</td><td>Karaisalı</td><td>37.2505</td><td>35.0628</td><td>240</td><td>MGM</td><td>1965–2011</td><td>0</td><td>4.61</td><td>881</td><td>437</td><td>1451</td></tr><tr><td>17981</td><td>Karataş</td><td>36.5683</td><td>35.3894</td><td>22</td><td>MGM</td><td>1963–2011</td><td>5</td><td>15.31</td><td>777</td><td>366</td><td>1365</td></tr><tr><td>D18M003</td><td>Uzunpınar</td><td>38.9710</td><td>36.8990</td><td>1740</td><td>DSI</td><td>1959–2005</td><td>11</td><td>7.45</td><td>303</td><td>161</td><td>493</td></tr><tr><td>D18M004</td><td>Seyhan Baraj</td><td>37.7000</td><td>35.0830</td><td>55</td><td>DSI</td><td>1974–2015</td><td>4</td><td>14.48</td><td>657</td><td>314</td><td>1117</td></tr><tr><td>D18M011</td><td>Kazancık</td><td>39.0670</td><td>36.7330</td><td>1585</td><td>DSI</td><td>1965–2003</td><td>8</td><td>7.69</td><td>274</td><td>176</td><td>433</td></tr><tr><td>D18M012</td><td>Hasan</td><td>37.8330</td><td>35.5830</td><td>1400</td><td>DSI</td><td>1990–2005</td><td>0</td><td>3.65</td><td>1006</td><td>713</td><td>1539</td></tr><tr><td>D18M013</td><td>Çavuşlar</td><td>37.5670</td><td>34.9500</td><td>1225</td><td>DSI</td><td>1963–2002</td><td>2</td><td>9.17</td><td>628</td><td>328</td><td>1123</td></tr><tr><td>D18M018</td><td>Gıçak</td><td>37.5670</td><td>35.2000</td><td>975</td><td>DSI</td><td>1988–2006</td><td>0</td><td>10.53</td><td>843</td><td>520</td><td>1173</td></tr><tr><td>D18M019</td><td>Çeralan</td><td>38.1000</td><td>36.0000</td><td>1600</td><td>DSI</td><td>1991–2005</td><td>4</td><td>6.67</td><td>970</td><td>622</td><td>1342</td></tr></tbody></table>

<figure><img src="image_154.png" /><figcaption>Figure 2. Layout of the meteorological stations in the Seyhan River basin.</figcaption></figure>
The average annual total precipitation is 641 mm in the Seyhan River basin; it changes from 274 mm in Kazancik (D18M011) in the north to 1006 mm in Hasan Cavuslar (D18M012) in the central part of the basin. A general decreasing tendency in the precipitation is observed from the coastal south to the north in the basin (Figure 3). As for the temporal change in the precipitation of the Seyhan River basin, three meteorological stations were taken: Adana (17351) from the southern, Feke (6902) from the central and Uzunpinar (D18M003) from the northern part (Figure 4). The annual total precipitation time series of the meteorological stations depict a visual stationarity along the observation period, meaning that no significant trend is observed. Also, a parallel tendency is noticed from the temporal fluctuations in the precipitation of the selected meteorological stations. This is simply connected to the size of the basin which is not so large as to create a difference.

The drainage area of the basin is 20,731 km² which is composed of 2.82% of the surface area of Turkey. The annual mean flow at the outlet of the basin to the Mediterranean Sea is 211.07 m³/s. The most important river in the basin is the Seyhan River, which gives its name to the basin. It is formed by the confluence of Zamanti and Goksu rivers and flows into the Mediterranean Sea. It has a length of 560 km, making it one of the largest rivers in Turkey [35,36].

The Seyhan River basin has been studied widely due to its importance for irrigation, energy and flood control as well as water resources management and planning. For example, the Impact of Climate Change on Water Resources Project, the Seyhan Basin Pollution Prevention Action Plan and the Seyhan Basin Sectoral Water Allocation Plan are among the studies completed by the General Directorate of Water Management [35,36] and the Water Management and Preparation of Basin Protection Action Plan is one performed by the Scientific and Technological Research Council of Turkey [37]. Further examples to be mentioned are given by Dikici et al. [38] who studied drought analysis with the Palmer drought index, Selek and Tuncok [39] who examined the effect of climate change on surface water management, and Topaloğlu [40] who determined the best-fit probability distribution functions for flow and precipitation in the basin. It should be emphasized that these are only a very short list of the studies conducted in the Seyhan River basin. On the other hand, making observation and mapping the formation of drought by using precipitation, soil moisture and plant as indicators the European Drought Observatory (EDO) has found that the most severe drought and precipitation deficit in the Seyhan River basin was recorded in 1990.

<figure><img src="image_155.png" /><figcaption>**Figure 3.** Spatial change in the annual precipitation over the Seyhan River basin.</figcaption></figure>
<figure><img src="image_156.png" /><figcaption>Figure 4. Temporal variability in precipitation.</figcaption></figure>

## 3. Methodology

### 3.1. Standardized Precipitation Index (SPI)

With the help of SPI [41], dry periods of the monthly precipitation can be identified and monitored as well as wet periods. The SPI is obtained simply as the standard normal variable transformed from the gamma distribution [42] by (Equation (1))

$$SPI_{ij} = \frac{x_{ij} - \mu_j}{\sigma_j} \qquad (1)$$

where $x_{ij}$ is the precipitation (in mm) in month j ($j = 1, 2, 3, \dots, 12$) of year i ($i = 1, 2, \dots, n$); $\mu_j$ and $\sigma_j$ are the average and standard deviation of precipitation (in mm) in month j, respectively. The SPI values are calculated for different time scales such as k = 1, 3, 6, 9, 12, 24 months. These arbitrarily selected time scales are used to represent the three types of drought: meteorological, agricultural, and hydrological [41].

### 3.2. Frequency Analysis

Frequency analysis is performed to characterize the drought and to determine the best-fit probability distribution function. In the frequency analysis of drought, the 2- and 3-parameter Gamma (G2, G3), the Generalized Extreme Value (GEV), the 2- and 3-parameter log-normal (LN2, LN3), Log-Pearson Type 3 (LP3) and the 2- and 3-parameter Weibull (W2, W3) probability distribution functions are often used in the literature [1,3]. For the sake of consistency with the literature, the above probability distribution functions were considered for the frequency analysis of drought in this study.

For months, when no precipitation is observed in certain years, the frequency analysis is applied on the non-zero values only to distinguish zero values from non-zero values, because the frequency analysis would otherwise not be meaningful. Any process that is composed of zero and non-zero values is called a censored or intermittent process. The total probability theorem is available to use for such processes to examine them in two parts: the zero- and non-zero parts.
According to the total probability theorem (Equation (2)) [43]

$$P(X \geq x) = P(X \geq x | X = 0)P(X = 0) + P(X \geq x | X \neq 0)P(X \neq 0) \quad (2)$$

is used. Here (Equation (3)),

$$P(X \geq x | X = 0) = 0 \quad (3)$$

Therefore, Equation (4)

$$P(X \geq x) = P(X \geq x | X \neq 0)P(X \neq 0) \quad (4)$$

is obtained.

$P(X \neq 0)$ is the rate of years with non-zero values in the SPI$_k$ ($k = 1, 3, 6, 9, 12, 24$ months) time series. Equation (4) can also be written in terms of the cumulative probability distribution as

$$1 - F(x) = (1 - p)[1 - F^{*}(x)] \quad (5)$$

In Equation (5), $p$ is the probability of zero values. $F(x)$ is the cumulative probability distribution function of all $X$ including zeros which is expressed as $P(X \leq x | X \geq 0)$ and $F^{*}(x)$ is the cumulative probability distribution function of the non-zero values of $X$ which is expressed as $P(X \leq x | X \neq 0)$. The rate of the non-zero values, $1 - p$ in Equation (5), can be expressed in terms of the probability as (Equation (6))

$$1 - p = P(X \neq 0) \quad (6)$$

The magnitude of an event with return period $T$ can be predicted by solving Equation (5) for $F^{*}(x)$ and then by using the inverse transformation of $F^{*}(x)$ to get the value of $X$. From Equation (5), Equation (7)

$$\frac{1 - F(x)}{1 - p} = 1 - F^{*}(x) \quad (7)$$

and Equation (8)

$$F^{*}(x) = \frac{F(x) - p}{1 - p} \quad (8)$$

can be written. Considering that the return period of a given severity for a particular drought duration can be predicted by Equation (9)

$$F(x) = 1 - \frac{1}{T} \quad (9)$$

Equation (8) turns into Equation (10)

$$F^{*}(x) = \frac{1 - \frac{1}{T} - p}{1 - p} \quad (10)$$

### 3.3. New Concepts and Definitions

The already known concepts related to the drought process are the dry period length, drought duration, drought severity and drought intensity. Also, the frequency or return period is used to characterize the drought. These concepts are defined as follows:

(a) Dry period length (L): The cluster which consists of consecutive negative values of SPI is referred to as the dry period length (Figure 5). It begins in a month with a negative SPI and continues until a positive SPI value is obtained in the time series. A dry period is shown mathematically as in Equation (11)

$$A = \{SPI | SPI < 0\} \quad (11)$$
where $s(A)$ is the number of elements of set A that shows the length of the dry period as (Equation (12))

$$L = s(A) \qquad (12)$$

(b) *Drought duration (D)*: Duration of droughts in an L-month long dry period is $1 \le D \le L$.

(c) *Drought severity (S)*: The accumulation of negative SPI values preceded and followed by positive SPI clusters is called severity. The severity of a drought D month-long is calculated by Equation (13)

$$S = \sum_{i=1}^{D} SPI_{i}, \quad SPI_{i} \in A \qquad (13)$$

In other words, it is the largest absolute value of the cumulative drought index (SPI in this study) in the dry period considered (Equation (14)):

$$S = \sum_{i=1}^{D} |SPI_{i}| \qquad (14)$$

(d) *Drought intensity (I)*: The intensity is obtained by dividing the severity of the drought by its duration (Equation (15)):

$$I = S/D \qquad (15)$$

(e) *Return period (or frequency)*: The return period of a drought is defined as the average time between two consecutive drought events. The drought frequency decreases with the increasing return period.

In this study, the following definitions of Cavus [20] are also considered:

(f) *Critical drought severity*: When more than one drought is recorded for any year, drought with the maximum severity is taken as the critical drought. No critical drought is assigned to a year in which drought is not observed.

(g) *Within-period drought*: Any drought with a duration shorter than the dry period length is called within-period drought. For example, in a dry period of 3 months, there are three 1-month droughts and two 2-month droughts. Similarly, there are two 1-month droughts in a dry period of 2 months (Figure 5).

(h) *Singular drought*: Drought that extends over the dry period length is called a singular drought. For example, there exists a 1-month singular drought in a dry period of 1 month; a 2-month singular drought in a dry period of 2 months; a 3-month singular drought in a dry period of 3 months and so on. The length of dry period becomes the same as the drought duration for singular droughts while the former is larger than the latter for within-period droughts.

(i) *No drought year*: Any year with no negative run of SPI is considered a year with no drought. Thus, the critical drought severity is not calculated for such years; a zero value is assigned to the critical drought severity instead.

It should be emphasized that drought is a process which is different than a dry period. It occurs any time when the value of the drought index (SPI in this study) takes a negative value. The drought can be as short as 1 month and as long as the dry period length. However, the critical drought concept introduced in this study considers the most severe drought of the year and eliminates all other milder droughts observed in the same year. The critical drought concept is meteorological station-based and therefore, area under the drought episodes are not considered [44].
<figure><img src="image_157.png" /><figcaption>**Figure 5.** Dry period length (L), drought duration (D), and drought severity (S). Dry period length (L) is determined as a fixed value for each dry period; drought duration (D) changes, however, from 1 month to L. Drought with duration L is called a singular drought while droughts with duration shorter than the dry period length are called within-period drought.</figcaption></figure>

3.4. *Precipitation Deficit*

A drought is defined as a period in which the SPI is continuously negative [41,45,46]. In other words, it begins when the SPI first falls below zero and ends with a positive value of SPI [41]. Thus, the retrospective analysis of drought events by using runs of SPI values may be useful to derive tangible information for precipitation required to overcome the drought [19].

Instead of the direct use of drought indices to develop SDF curves as in previous studies [10], the precipitation deficit is calculated in this study. This approach helps to better understand as the accumulated precipitation is used to define the drought event so that it can be easily identified by end-users such as farmers and decision-makers.

As part of the methodology in this study, the relationship between the precipitation and SPI is detected by regression analysis. In the drought analysis, when the drought duration (D month) and return period (T years) increase, precipitation is expected to decrease, and therefore, the precipitation deficit is expected to increase. Any function to be applied on the relationship between precipitation and the corresponding SPI should satisfy this physical expectation. Some types of functions such as the second and the third order polynomials were omitted as they might produce negative precipitation deficit values against this physical reality. On the other hand, some functions such as Gompertz were discarded, because it was discovered after trials that they could not properly fit SPI<sub>k</sub> time series of months with high number of zero values (such as SPI<sub>1</sub> in August–July). As a result, it was seen that the logistic function could be appropriate to choose among the functions tested due to the above expectation of the physical realization. The logistic regression equation was fitted to the relation between precipitation and the corresponding SPI values and therefore the logistic regression equation
was used to analyze data clusters. It describes a family of sigmoidal curves. The simple logistic function has the form of (Equation (16))

$$f(x) = \frac{a}{1 + be^{-cx}} \qquad (16)$$

in which a, b and c are parameters to be estimated through the use of data scatter.

The regression equation can be used to calculate the precipitation threshold value. For time scales 1, 3, 6 and 9 months, the relation between precipitation and SPI changes from month to month. That is, a particular function should be used for each month of the year. However, one curve exists for time scales of 12 and 24 months. Drought is classified depending on the value that the drought index takes [47]. Referring to Figure 6, the precipitation threshold ($P_{TH}$) was taken as precipitation at SPI = 0 for all time scales. Precipitation values at the boundary of drought classes ($P_{B,Extreme}$, $P_{B,Severe}$, $P_{B,Moderate}$, $P_{B,Mild}$) are determined based on the classification of McKee et al. [41] as shown in Figure 6. Also shown is the critical precipitation ($P_{C}$) which is expected to occur under a critical drought. The difference between the precipitation threshold and the critical precipitation is defined as the precipitation deficit and it is calculated by Equation (17)

$$P_D = P_{TH} - P_C \qquad (17)$$

for each drought of a given duration and return period. A flowchart of the steps that will be implemented for calculating the precipitation deficit is given in Figure 7.

<figure><img src="image_158.png" /><figcaption>Figure 6. Definition of precipitation threshold, boundary precipitation and critical precipitation.</figcaption></figure>
<figure><img src="image_159.png" /><figcaption>Figure 7. Step-by-step calculation of the precipitation deficit.</figcaption></figure>

## 4. Spatial Mapping

In this study, the Seyhan River basin in the Mediterranean region of Turkey was investigated for its drought characteristics based on the precipitation deficit by considering 19 meteorological stations. SPI was applied to monthly precipitation data at the stations at $k = 12$-month time scale. Critical drought severity was calculated from the SPI time series which were first implemented by frequency analysis after which critical drought severities were calculated for return periods of 2, 5, 10, 25, 50 and 100 years. From the critical drought severity, the precipitation deficit of 1-, 3-, 6- and 12-month drought durations and 2-, 5-, 10-, 25-, 50- and 100-year return periods were determined at $k = 12$-month time scale. The drought intensity values were also obtained as the ratio of the drought severity to its duration. Examples to describe the above analysis are given in Table 2 from which it is clearly seen that no drought exists in a few meteorological stations for longer drought duration and return periods as the likelihood of any drought decreases as its duration and return period increase. Also, it is seen from Table 2 that no drought was determined in station 6560 at $k = 12$-month time scale although the station has experienced drought at lower time scales. This is due to the short length of the precipitation time series that do not allow one to make a proper frequency analysis and to quantify the drought.

Spatial mapping becomes useful in obtaining information for stations with smaller size observation that prevents making a proper frequency analysis or no observation at all [40]. The resulting precipitation deficit corresponding to D month-drought duration and T year-return period at $k = 12$-month time scale was mapped using the inverse distance weighted (IDW) interpolation technique (Figure 8). Only the precipitation deficit calculated from the SPI was considered in the interpolation process to derive the spatial mapping of the drought over the basin.

Figure 8 presents boundary values of precipitation deficit changes for each D-month drought. As given in Table 2, the D = 1-month drought has its own boundary value that changes between 42.6 mm (the 2-year return period drought in meteorological station 17802) and 567.9 mm (the 100-year return
period drought in meteorological station D18M019). Similarly, for the drought of D = 3 month-duration, boundary values are between 31.2 mm (the 2-year return period drought in meteorological station D18M011) and 305.6 mm (the 100-year return period drought in meteorological station D18M012). This is also applied to D = 6 and 12-month drought durations for which the precipitation deficit intervals are 16.7 mm–231.2 mm and 22.1 mm–74.9 mm, respectively (Table 2). Therefore, it is important to emphasize that the maps in Figure 8 are only comparable for each of the drought durations, because the iso-contours of the precipitation deficit have been fixed for each of the drought durations separately. Darker colors in the maps of the same drought duration imply more severe drought. For example, it is seen that droughts become more severe with moving from shorter return periods to longer return periods for D = 1-month drought. This statement is correct for D = 3-, 6- and 12-month drought as well. However, when droughts with different durations are compared, it should be emphasized also that a darker color in a longer drought duration map does not necessarily mean that the drought is more severe than a drought with a lighter color in the shorter drought duration. Similarly, a lighter color does not necessarily mean the opposite; i.e., the drought is milder. Therefore, the discussion of results should be made through the joint use of Table 2 with Figure 8 to arrive at a conclusive discussion about the severity of the drought. It is clearly seen from Table 2 and Figure 8 that the drought severity becomes milder with the increasing longer return periods. This is an expected result of the fact that drought severity is absorbed along longer drought durations. This is a phenomenon quite similar to or even the same as the less intense precipitation of longer durations in the hydrological practice of precipitation intensity–duration–frequency curves.

The spatial distribution of D = 1-month drought duration indicates that more severe precipitation deficits tend to occur in the coastal parts of the basin for all return periods while the north-eastern part of the basin is prone to a lower precipitation deficit at the same return periods. In other words, the majority of the precipitation deficit that occurred in the coastal part was severe in D = 1-month drought duration. As the return period increases from 2 years to 100 years, the drought intensity increases and more severe intensities move towards the northern part of the basin. However, it is always lower in the north compared to the south. The intensity of precipitation deficit exhibits a more variable behavior over the basin when the return period increases.

At the D = 3-month drought duration, again more severe droughts are typically observed at the coastal part of the basin. Especially, the northern part of the Seyhan River basin exhibits a lower precipitation deficit. Nevertheless, as the return period increases, more severe droughts shift from south towards the north, as was the case for D = 1-month drought. It should be noted from Table 2 that for three meteorological stations (6204, 6560 and D18M019), no drought intensity is calculated. This is because the number of critical drought severities is less than 10 and the frequency analysis was therefore not applied on these particular meteorological stations. The number of such meteorological stations increases to 5 and 6 for D = 6 and 12 months, respectively. These stations are not indicated in the corresponding maps in Figure 8.

Another issue to discuss is the D = 12-month duration drought with 2-year return period (See blank column of T = 2-year return period in D = 12-month drought in Table 2 that corresponds to the blank lower-left cell of Figure 8). The mildest drought of D = 12-month has a return period longer than 2 years. In other words, once a 12-month drought is observed, it is as severe as a drought with a return period longer than 2 years. This is the case for 19 meteorological stations used in this study. Therefore, no map was created for D = 12-month drought at T = 2-year return period. Also, it is noticeable from Table 2 that this has been the case for five meteorological stations (6902, 17802, 17840, 17934, and 17936). Maps in Figure 8 have been created by using meteorological stations for which the precipitation deficit is calculated for a given drought duration and return period.
<table><caption>Table 2. Drought intensity based on precipitation deficit corresponding to 2, 5, 10, 25, 50 and 100-year return periods at k = 12-month time scale.</caption><thead><tr><th rowspan="2">Return Period</th><th colspan="10">D = 1 Month</th><th colspan="10">D = 3 Months</th><th colspan="10">D = 6 Months</th><th colspan="10">D = 12 Months</th></tr><tr><th>2</th><th>5</th><th>10</th><th>25</th><th>50</th><th>100</th><th>2</th><th>5</th><th>10</th><th>25</th><th>50</th><th>100</th><th>2</th><th>5</th><th>10</th><th>25</th><th>50</th><th>100</th><th>2</th><th>5</th><th>10</th><th>25</th><th>50</th><th>100</th><th>2</th><th>5</th><th>10</th><th>25</th><th>50</th><th>100</th></tr></thead><tbody><tr><td>Station no</td><td>6204</td><td>50.4</td><td>152.7</td><td>214.8</td><td>281.1</td><td>321.0</td><td>353.5</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>6560</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>6893</td><td>81.3</td><td>108.7</td><td>123.0</td><td>138.6</td><td>148.7</td><td>158.1</td><td>65.3</td><td>79.3</td><td>86.1</td><td>93.3</td><td>97.8</td><td>101.8</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>6992</td><td>174.3</td><td>280.5</td><td>320.4</td><td>354.2</td><td>371.5</td><td>384.3</td><td>96.4</td><td>199.7</td><td>218.4</td><td>230.4</td><td>235.4</td><td>238.6</td><td>131.3</td><td>137.6</td><td>141.1</td><td>142.5</td><td>143.4</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>17351</td><td>142.0</td><td>230.9</td><td>273.7</td><td>316.4</td><td>342.1</td><td>363.7</td><td>79.9</td><td>150.2</td><td>172.4</td><td>187.7</td><td>194.3</td><td>198.6</td><td>44.1</td><td>96.9</td><td>102.4</td><td>105.3</td><td>106.2</td><td>106.7</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>17802</td><td>42.6</td><td>113.4</td><td>156.4</td><td>199.8</td><td>224.6</td><td>243.7</td><td>31.4</td><td>81.4</td><td>99.4</td><td>114.8</td><td>122.5</td><td>127.9</td><td></td><td>56.2</td><td>63.8</td><td>67.8</td><td>69.0</td><td>69.5</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>17837</td><td>66.6</td><td>113.9</td><td>136.9</td><td>158.7</td><td>170.8</td><td>180.4</td><td>48.1</td><td>76.9</td><td>90.7</td><td>103.8</td><td>111.0</td><td>116.5</td><td>34.0</td><td>52.5</td><td>58.8</td><td>63.3</td><td>65.1</td><td>66.1</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>17840</td><td>69.7</td><td>136.4</td><td>170.9</td><td>207.8</td><td>231.4</td><td>252.2</td><td>38.6</td><td>94.3</td><td>113.4</td><td>130.4</td><td>139.6</td><td>146.7</td><td></td><td>66.6</td><td>75.4</td><td>81.3</td><td>83.7</td><td>85.2</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>17906</td><td>42.7</td><td>96.5</td><td>125.2</td><td>151.9</td><td>165.9</td><td>176.4</td><td>32.0</td><td>66.1</td><td>78.4</td><td>88.1</td><td>92.7</td><td>95.9</td><td>22.1</td><td>45.9</td><td>49.7</td><td>51.5</td><td>52.1</td><td>52.4</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>17934</td><td>146.1</td><td>242.9</td><td>294.1</td><td>348.3</td><td>382.5</td><td>412.4</td><td>98.4</td><td>154.5</td><td>178.1</td><td>199.0</td><td>209.8</td><td>217.5</td><td></td><td>98.9</td><td>107.6</td><td>113.7</td><td>115.7</td><td>116.6</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>17936</td><td>150.8</td><td>296.0</td><td>374.1</td><td>452.4</td><td>498.3</td><td>535.5</td><td>105.8</td><td>204.3</td><td>228.8</td><td>246.5</td><td>254.7</td><td>260.4</td><td>129.6</td><td>136.4</td><td>140.0</td><td>141.2</td><td>141.9</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>17961</td><td>149.7</td><td>270.4</td><td>325.1</td><td>377.9</td><td>408.8</td><td>434.0</td><td>80.4</td><td>179.0</td><td>201.9</td><td>218.4</td><td>226.0</td><td>231.2</td><td>80.4</td><td>179.0</td><td>201.9</td><td>218.4</td><td>226.0</td><td>231.2</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>D18M003</td><td>57.9</td><td>109.1</td><td>134.8</td><td>199.3</td><td>173.0</td><td>183.9</td><td>45.7</td><td>72.1</td><td>80.9</td><td>87.7</td><td>90.8</td><td>93.0</td><td>31.0</td><td>45.3</td><td>47.6</td><td>48.6</td><td>48.9</td><td>49.1</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>D18M004</td><td>129.2</td><td>288.8</td><td>294.0</td><td>305.7</td><td>340.6</td><td>372.8</td><td>89.4</td><td>141.5</td><td>161.9</td><td>181.1</td><td>190.7</td><td>196.8</td><td>58.9</td><td>95.0</td><td>100.8</td><td>104.3</td><td>105.7</td><td>106.5</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>D18M011</td><td>43.0</td><td>85.0</td><td>108.3</td><td>130.9</td><td>143.9</td><td>154.3</td><td>31.2</td><td>60.7</td><td>69.8</td><td>76.9</td><td>80.3</td><td>82.8</td><td>16.7</td><td>39.6</td><td>42.8</td><td>44.4</td><td>44.8</td><td>45.0</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>D18M012</td><td>152.6</td><td>247.5</td><td>312.2</td><td>396.4</td><td>459.6</td><td>521.3</td><td>70.3</td><td>165.4</td><td>216.2</td><td>264.3</td><td>286.9</td><td>305.6</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>D18M013</td><td>146.7</td><td>234.8</td><td>276.7</td><td>317.9</td><td>342.2</td><td>362.1</td><td>96.9</td><td>150.2</td><td>166.8</td><td>179.4</td><td>185.3</td><td>189.5</td><td>61.2</td><td>94.0</td><td>98.8</td><td>101.1</td><td>101.8</td><td>102.2</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>D18M018</td><td>149.2</td><td>286.5</td><td>350.0</td><td>411.9</td><td>448.4</td><td>478.1</td><td>72.7</td><td>189.1</td><td>220.0</td><td>242.3</td><td>252.3</td><td>259.1</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>D18M019</td><td>109.9</td><td>270.7</td><td>356.5</td><td>451.3</td><td>512.8</td><td>567.9</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>
<figure><img src="image_160.png" /><figcaption>**Figure 8.** Intensity maps for droughts of 1, 3, 6 and 12-month durations and 2, 5, 10, 25, 50 and 100-year return periods over the Seyhan River basin.</figcaption></figure>
Except for the northern part, the majority of the basin has more severe droughts for all return periods. A conclusive result is that the coastal part of the basin with higher precipitation (Figure 3) experiences more severe drought at all return periods while the northern part of the basin with lower precipitation is characterized by a milder drought. This can be explained by the fact that higher temperature in the southern coastal lowlands increases evapotranspiration that reduces the available precipitation substantially and gives an increase in the precipitation deficit to end up with more severe droughts. In the northern part of the basin with higher altitudes and lower temperature, droughts are milder compared to the southern part due to the net precipitation being reduced by the lower evapotranspiration. It shows also that the coastal parts of the basin are more likely to be affected from hydrological drought with a consequent loss in water resources.

**5. Conclusions**

This study was assessed as a presentation of a framework of methodologies for the analysis of the
spatial characteristics of drought by utilizing frequency analysis in Seyhan River basin in the eastern
part of the Mediterranean region in Turkey. The SPI calculated at the 12-month time scale was used as
an indicator of drought. Precipitation deficit was newly introduced and calculated using regression
analysis between SPI and the corresponding precipitation.

The precipitation deficit was calculated based on the SPI. Instead of the direct use of SPI, the severity/intensity is calculated using the precipitation deficit to characterize the drought to make the procedure more physically based for a better and simpler interpretation. This is because SPI is a technical tool and it is thus difficult to understand, at first glance, by end-users and decision-makers. Precipitation deficit in different durations and return periods is more useful and physically meaningful to the users. The direct information provided by the precipitation deficit allows planning and measures against drought to be taken in advance. Spatial analysis of drought might help decision makers to achieve better natural resources planning by considering the spatial drought vulnerability.

The spatial analysis indicates that the Seyhan River basin in the Mediterranean region of Turkey experiences droughts with quite different severities simultaneously. The spatial distribution would alter greatly with increasing return period and drought duration. While the coastal part of the basin is vulnerable to droughts at all return periods and drought durations, the northern part of the basin would be expected to be less affected by the drought. On the other hand, as the drought duration and return period increase, drought intensity based on precipitation deficit is expected to decrease. Another result reached in this study is that it could be common for one point in the basin to suffer from the drought, whilst surrounding points in the same basin experience normal or even humid conditions. This reinforces the importance of spatial analysis over the basin instead of the point-scale temporal analysis made in each meteorological station in the basin.

The drought characterization based on precipitation deficit completely changes at longer drought duration and return periods. The river basin experiences severe prolonged droughts, which means that, at *k* = 12-month time scale, the coastal part will suffer from severe hydrological drought.

**Author Contributions:** Conceptualization, H.A. and Y.C.; Data Curation, H.A.; Formal Analysis, H.A. and Y.C.; Investigation, H.A. and Y.C.; Methodology, H.A. and Y.C.; Software, Y.C.; Visualization, Y.C. and H.A.; Writing—Original Draft Preparation, Y.C.; Writing—Review & Editing, H.A. and Y.C.

**Funding:** This research was supported by Research Fund of the Istanbul Technical University. Project Number: 42141.

**Conflicts of Interest:** The authors declare no conflict of interest.

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<figure><img src="image_161.png" /></figure>

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
<figure><img src="image_162.png" /></figure>

Article

# A New 60-Year 1940/1999 Monthly-Gridded Rainfall Data Set for Africa

Claudine Dieulin ¹,*, Gil Mahé ¹,*, Jean-Emmanuel Paturel ¹, Soundouss Ejjiyar ²,
Yves Tramblay ¹, Nathalie Rouché ¹ and Bouabid EL Mansouri ²

¹ HydroSciences Montpellier, Université de Montpellier–CC 57, 163 rue Auguste Broussonnet, 34090 Montpellier, France; jean-emmanuel.paturel@ird.fr (J.-E.P.); yves.tramblay@ird.fr (Y.T.); nathalie.rouche@ird.fr (N.R.)

² Faculty of Sciences, Hydro-Informatic Section, Campus Maamora, Ibn Tofail University, Kenitra BP 133–14000, Morocco; ejjiyar-soundouss@hotmail.com (S.E.); b_elmansouri@yahoo.fr (B.E.M.)

* Correspondence: claudine.dieulin@umontpellier.fr (C.D.); gil.mahe@ird.fr (G.M.); Tel.: +33-467-149-025 (C.D.); Fax.: +33-467-144-774 (C.D. & G.M.)

Received: 23 November 2018; Accepted: 13 February 2019; Published: 22 February 2019

**Abstract:** The African continent has a very low density of rain gauge stations, and long time-series for recent years are often limited and poorly available. In the context of global change, it is very important to be able to characterize the spatio-temporal variability of past rainfall, on the basis of datasets issued from observations, to correctly validate simulations. The quality of the rainfall data is for instance of very high importance to improve the efficiency of the hydrological modeling, through calibration/validation experiments. The HydroSciences Montpellier Laboratory (HSM) has a long experience in collecting and managing hydro-climatological data. Thus, HSM had initiated a program to elaborate a reference dataset, in order to build monthly rainfall grids over the African continent, over a period of 60 years (1940/1999). The large quantity of data collected (about 7000 measurement points were used in this project) allowed for interpolation using only observed data, with no statistical use of a reference period. Compared to other databases that are used to build the grids of the Global Historical Climatology Network (GHCN) or the Climatic Research Unit of University of East Anglia, UK (CRU), the number of available observational stations was significantly much higher, including the end of the century when the number of measurement stations dropped dramatically, everywhere. Inverse distance weighed (IDW) was the chosen method to build the 720 monthly grids and a mean annual grid, from rain gauges. The mean annual grid was compared to the CRU grid. The grids were significantly different in many places, especially in North Africa, Sahel, the horn of Africa, and the South Western coast of Africa, with HSM_SIEREM data (database HydroSciences Montpellier_Système d'Information Environnementales pour les Ressources en Eau et leur Modélisation) being closer to the observed rain gauge values. The quality of the grids computed was checked, following two approaches—cross-validation of the two interpolation methods, ordinary kriging and inverse distance weighting, which gave a comparable reliability, with regards to the observed data, long time-series analysis, and analysis of long-term signals over the continent, compared to previous studies. The statistical tests, computed on the observed and gridded data, detected a rupture in the rainfall regime around 1979/1980, on the scale of the whole continent; this was congruent with the results in the literature. At the monthly time-scale, the most widely observed signal over the period of 1940/1999, was a significant decrease of the austral rainy season between March and May, which has not earlier been well-documented. Thus, this would lead to a further detailed climatological study from this HSM_SIEREM database.

**Keywords:** Africa; rainfall; monthly grids; database; inverse distance weighted
# 1. Introduction

On a global scale, the first climate observations began during the second part of the 19th century. Among these climate variables, rainfall data sets have been the most complete, since the beginning of the 1950s [1]. In Africa, the most well-known change in rainfall regime occurred in Sahel, but all of Western and Central Africa experienced an abrupt decrease in mean annual rainfall, at the end of the 1960s or towards the beginning of the 1970s [2–4]. To study rainfall changes at the scale of the whole African continent, rainfall grids can be downloaded from several institutions, but it is well-known that the African continent is less documented than other parts of the world [5,6]. This has led to some discrepancies between rainfall calculated from different databases, as has been shown for Western Africa, by Mahé et al. [7]. Among the available data sets, Climatic Research Unit of University of East Anglia, UK (CRU) [8,9] is used very often and seems to be the most accurate, compared to other sources. However, for instance, for Western and Central Africa, rainfall data are not very well-documented [10,11].

The first objective of the present work is to elaborate monthly grids of the best possible quality, over the African continent. For this, it was necessary to build the most exhaustive rainfall database to help improve understanding of the climatic processes and other applications, such as hydrological modeling. Hydrologists at the Institut de Recherche pour le Développement (IRD) or the French National Research Institute for Sustainable Development, previously ORSTOM (Office de Recherche Scientifique Outre-Mer), had historically participated in the acquisition of climatological data and built up a digital database, as early as the end of the 1960s. However, by the end of the 1980s, the IRD stopped collecting and managing rainfall data for Western and Central Africa. The researchers of the HSM decided to valorize this unique set of data for Africa, first, in the search of additional data to cover the whole continent, and to update the database up to 1999. This project was an opportunity to create a reference database, called HSM_SIEREM database, which is still managed by the HSM. The HSM_SIEREM [12] database has been used for sub-continental studies of climate change, like those by Paturel et al. [10,11], many regional studies of river-runoff relationships [13,14], and studies of the impact of climate change on hydrological regimes in Western Africa [15]. These data were first used at a continental scale, for a study of rainfall-runoff variability over the past decades [16].

In this article, we have presented, for the first time, the methods through which the data were selected and evaluated, the process leading to the creation of the monthly-gridded dataset over Africa (at a 0.5 × 0.5 degree resolution), and a first assessment of the content of this new database. In the second part, we have compared this new HSM_SIEREM grid, with the CRU grid and have pointed out the similarities and the differences to help improve the future uses of both bases. In the third part, we have presented a first study of rainfall variability, over the 1940/1999 time period, for the whole continent, to check the general quality of the dataset, by searching if the main rainfall signals were visible in large-scale regional averaged time-series. This first study will be followed by further detailed studies on the spatio-temporal variability of rainfall over the African continent. The monthly grids we created have been provided, for free, on the website of the HydroSciences Montpellier SIEREM project (http://www.hydrosciences.org/spip.php?article1387), as zipped ASCII grids and in the NetCDF format.

# 2. Materials and Methods

## 2.1. History of the HSM_SIEREM Database

Created in 1943, ORSTOM/IRD's mission was to promote scientific cooperation and education in Western and Central Africa. In this framework, hydrologists installed and managed most of the hydro-meteorological stations in French speaking countries—the former colonies. The records were noted in paper booklets and a long process of data entry began, as early as 1967, on punched cards, first, and were then transferred onto magnetic tapes of the CNRS (Centre National de la Recherche Scientifique) computers in Orsay/Paris. This transfer was taken into account to run a first control on
the data quality check for duplicate cards, removal of data for non-existent days (such as, 31st of April and 30th of February), before building up the first database. These cards were individually controlled and were modified, kept or removed. During this process, every measurement point was linked to only one data set. Due to the state of technology at the time the data was collected, some measurement stations had no geographical referencing, and it was added later, when possible.

A tripartite agreement between the Comité Interafricain d'Etudes Hydrauliques (CIEH) or the Interafrican Hydraulic studies Committee, the Agence pour la SECurité de la Navigation Aérienne (ASECNA) or Agency for the Safety of Aerial Navigation in Africa and Madagascar, and the ORSTOM, began in 1973, for the collection of data from thirteen countries of Western and Central Africa (Benin, Burkina Faso, Cameroon, Congo, Ivory Coast, Gabon, Mali, Mauritania, Niger, Central Africa, Senegal, Chad, and Togo) and ended in 1989, with the edition of the two rainfall data books, for each country, covering the years up to 1965 and 1966, to 1980 [5]. This agreement allowed to update the database at a daily time-step, up to 1980. Figure 1 [17] shows the benefits of this agreement in the evolution of the rainfall database. From this set of data, a map of the mean annual rainfall over Western and Central Africa was drawn by L'Hote & Mahé [18].

<figure><img src="image_163.png" /><figcaption>**Figure 1.** Distribution of data in the ORSTOM rainfall database from the origin of the observation to 1983 [17].</figcaption></figure>

The Water Assessment [19] financed by the World Bank, the UNDP (United Nations Development Program), the African Bank of Development, and the French ministry of Cooperation, initiated a program to collect monthly rainfall data over the whole African continent, for the period of 1981/1990. This program was carried out by Mott MacDonald International, the BCEOM (Bureau Central d'études pour les Equipements Outre-Mer) and the SOGREAH (Société Grenobloise d'Etudes et d'Applications Hydrauliques) offices for the Western and Central part of Africa, and the hydrologists of ORSTOM, who participated to the gathering of data in the Sub-Saharan area.

Up to 1999, the IRD Hydrology Laboratory in Montpellier gathered and managed all the climatological data archived in every country where ORSTOM hydrologists were present, mainly in Africa, as well as the rainfall data from all past programs. Since 2000, the HSM_SIEREM database was only used and enriched by the teams of HSM in Montpellier, with no institutional mandate for commitment to the management or the dissemination of this base. For Eastern and Southern Africa, HSM gathered data through its commitment in the IHP (International Hydrological Program) of UNESCO and mainly in the FRIEND programs (Flow Regimes from International Experimental and Network Data), as HSM managed the implementation and hosting of the databases of the different African FRIEND groups. Many other international programs that the HSM collaborated on in Africa, allowed the IRD hydrologists access to more recent data.

The CRU is specifically acknowledged, as they had developed a collaboration with the hydrologists of HSM which let them access the rainfall data in the areas where HSM did not have any access. However, both CRU and HSM databases where kept independent.

The HSM_SIEREM database, thus, contains rainfall data for more than 6000 stations across Africa.
### 2.1.1. The Reference Database

The development of a continuous, quality-checked and reliable long-term database of monthly rainfall across Africa, was a key component of this project. The database contained numerous records, due to the fact that any incoming rainfall datum was always stored. According to Rouché et al. [20], the result was that, for some stations, up to ten different data sets were stored (collected by different people, at different time-steps, data corrected for specific purposes, etc.). Therefore, it was essential to eliminate redundant sets and choose the better series.

#### Constitution of the Reference Data Set

The dataset was built for every African country. Records with missing geographical coordinates were removed, daily, and ten-day data were aggregated at the monthly time-step. Then, if several series for the same station still remained in the database, a "quality label" was given to the series (depending on the origin of the data, the reliability of the provider, and the length of the observation period), to select which one was to be kept. When the retained series had missing periods, it was filled with data from a different station of the same measurement point, when available.

The aim of this process was to keep only one station per measurement point. The graph in Figure 2 shows the number of sets before and after the creation of the reference database, for every country.

<figure><img src="image_164.png" /><figcaption>**Figure 2.** Result of the selection process between redundant time-series—in grey, the number of series at the beginning, and in black, the final number of reference series for the period 1900/1999, where each series corresponds to a unique station. Note: Due to insufficient data availability, Madagascar has not been processed in this project.</figcaption></figure>

#### Criticism of the Reference Data Set

For every country, patterns were established to define:

* the period of the rainy and dry seasons; and

* a range of monthly rainfall values (minimum and maximum) that were not to be crossed.

During this process, a series of automatic tests were run, and a flag was inserted if any value:

* exceeded the previously defined values for the country;

* was identical to the value of the same month of the previous or next year at the station; or

* was identical to the value of the previous month of the same year.

The flagged months were printed and checked by the HSM hydrologists, who determined if the flag led to the removal of the data or of part of the time-series. This step was the longest but the most
important for the quality of the database. Thus, the time-series of each station was both automatically
and manually checked, to reduce the risk of erroneous data in the reference database.

2.1.2. The Period

The HSM_SIEREM base was exported in the form of one file per country and per year, over the period 1900/1999. The first goal was to create monthly grids for the whole of the 20th century. However, when plotting the measurement points, the maps showed that before 1940 (Figure 3), the data were too sparse and too heterogeneously distributed to be spatialized over the whole continent. The decision was then made to limit the interpolation process to the period 1940/1999. Contrary to CRU, we decided not to create a reference period like the ones the CRU used to establish patterns to create interpolated rainfall measurement points [9]—even for the years where the distribution of data was heterogeneous or density of stations was low—enabling them to start the interpolated time-series in 1900. This limited our possibility to extend the grid to a period prior to 1940. However, this is also what makes a fundamental difference between the HSM_SIEREM and the CRU grids, i.e., in the HSM_SIEREM database, each month, the grid was calculated only from the available observed data.

<figure><img src="image_165.png" /><figcaption>**Figure 3.** Evolution of the number of measurement stations in 1900, 1920, and 1940, in the HSM_SIEREM rainfall database.</figcaption></figure>

All African National Meteorological services were then contacted to update the data series at a
monthly time-step, especially from the last 20 years of the 20th century, but only a few of them sent
recent monthly data. The number of months (Figure 4) with available data, therefore, varies from 4061
stations in 1975 to 1464 stations in 1999. This shows a decrease of the number of observed rain gauges;
this trend started as soon as the 1980s and has been observed worldwide, but it affects Africa more
severely. However, the HSM_SIEREM database still has a sufficient number of points, compared to
the Global Historical Climatology Network (GHCN) values—2500 gauges, worldwide, for the year
2000 [5], compared to a little less than 1,500 for Africa alone, in our database, for December 1999.
<figure><img src="image_166.png" /><figcaption>**Figure 4.** Number of monthly data observed in the HSM_SIEREM base, per year, from January 1940 to December 1999.</figcaption></figure>

The scarcity of rainfall data was a recurrent problem, whatever the purpose [5,9,21]. Due to its history, the HSM laboratory collected a large amount of data. However, it is not allowed, either by the National Meteorological Services or the WMO (World Meteorological Organization) to disseminate raw rainfall data, as they are kept as the property of these Services. It was then decided that interpolated grids would be created, which would be disseminated for free to the community as the HSM_SIEREM database. This database has already been compared to the CRU for hydrological modeling in Western and Central Africa, and had given better results [7].

## 2.2. Geographical Processing

### 2.2.1. Reference of the CRU Grid Constitution

Our references for rainfall grids over the African continent were the CRU grids. These were built at a resolution of 0.5 × 0.5 degree. They concern seven climatological variables, namely precipitation, wet-day frequency, mean temperature, diurnal temperature range, vapor pressure, cloud cover, and ground frost frequency. For all data types, the construction of a monthly surface climate over global land areas, excluding Antarctica, covers the 1901/1996 time period (period of the first version, later update extended this period up to the 21st century). An "anomaly" approach was used, this technique attempted to maximize the available station data in space and time. "In this technique, grids of monthly anomalies relative to a standard normal period (1961/1990) (...) were first derived. The anomaly grids were then combined with a high-resolution mean monthly climatology to arrive at fields of estimated monthly surface climate." [8]. Rainfall anomalies are expressed in percentage. The standard period was chosen because of the good quantity of data available for this period.

The consistency checks were somewhere similar to the ones we applied on our data sets, defining monthly minimum, mean, and maximum values, and checking the values out of these ranges for confirmation or removal.

The anomalies over the standard normal period of rainfall were calculated, the CRU used tiles; 29 tiles were defined worldwide, with 4 tiles being defined for the African continent: tile 17 (20°W to 20°E, 38°N to 0°), tile 18 (20°E to 52°E, 38°N to 0°), tile 19 (0° to 52°E, 0° to 20°S), and tile 20 (10° to 52°E, 20°S to 36°S).
Three global station datasets were compiled by the CRU, from the basis of the construction of the gridded anomalies of primary variables. The precipitations and mean temperatures were compiled by the CRU, the diurnal temperature range dataset was based on the GHCN maximum and minimum temperature data.

The interpolation method adopted to process the tiles was a thin-plate spline, a function of latitude, longitude, and elevation. "The technique is robust in areas with sparse or irregularly spaced data points. The main advantage of splines over many other geostatistical methods is that prior estimation of the spatial autocovariance structure is not required" [8].

The process was computed per tile at a step of 0.5 × 0.5 degrees, with the tiles overlapping by at least 5 × 5 degrees (the number of stations varied from 200 to 1000 per tile). On the overlapping areas, the grid values were calculated as a weighted average of the contributing tiles; the weights were simply grid points between a particular point and the edge of its tile. The grids were constrained to avoid unrealistic values for rainfall, and the negative values were converted to zero.

"A GCV and its square root (RTGCV) provide an estimate of the mean predictive error (and hence power) of the fitted surface and as such permit an assessment of the relative accuracy of a fitted surface" [8].

The second step was to collect mean temperature, diurnal temperature, and precipitation over the 1901/1996 period. Any series with less than 20 years of data during the 1961/1990 period were excluded from the analysis. Rainfall data were expressed in percentages. Each station time-series was converted to anomalies, relative to the 1961/1990 mean.

The interpolation of the monthly anomaly method chosen for the 1901/1996 series was ADW (Angular Distance-Weighted), using the eight nearest stations, except when there were more than eight stations within a single 0.5° grid cell (only three cells were used in this case). The ADW gridding employed in this study did not permit the inclusion of elevation as a predictor, it was, thus, not included in a tri-variate interpolation technique [9]. "To prevent extrapolation to unrealistic values, the interpolated anomaly fields were forced towards zero at grid points beyond the influence of any stations. This was accomplished by creating synthetic stations with anomaly values of zero in regions where there were no stations within a predefined distance chosen to be equal to the global-mean CDD"; (450 km for precipitation) [9].

### 2.2.2. Choice of the Interpolation Process Options

Compared to the CRU standard normal reference period, we had a sufficient number of observations for a longer period and could interpolate rainfall from the observed values at each monthly time-step (Figure 5).

One file per month was created in the period 1940/1999 (i.e., 720 files). All files of monthly data were imported in the ArcMap and a point shapefile was created. The following processes were computed, using the ArcInfo Workstation macro-command language (AML). This ensured a homogeneous process.

Considering the size of the region to process and the heterogeneity of the distribution of rain gauge stations, and taking advantage of the CRU experience, the IDW (Inverse Distance Weighted) method was chosen.

With the ArcInfo Workstation, the IDW interpolation method does not allow to take into account the altitude of the measurement points. A few things can be discussed about this point. Due to the very different geographical situations across Africa, the question of altitude influence on precipitation cannot be dealt with one simple approach for the whole continent. For instance, altitude has no influence on rainfall over Western Africa (except in few specific locations, like near the Atakora mountains and close to the Guinea Mountains) and in the Sahel, covering several thousands of kilometers, therefore, the spline method gives the same results as the kriging method [22]. The coast of Cameroon is particularly wet, and the highest rainfall amounts in Africa are recorded at the foot of the Mount Cameroon; the HSM_SIEREM database gives much higher rainfall in this area than the CRU one—an
example of the kind of differences between the two grids. In this area the rainfall decreases with altitude, from 10,000 mm per year on the coast of Debundsha (altitude 26 m), to 2000 mm near the top of Mount Cameroon (4040 m). The situation was roughly identical on the coast of the Guinean Mounts, with very high annual amounts on the coast, and decreasing values with elevation. Contrarily, the rainfall amount increased in other hilly areas, over the continent, like the Atlas Mountains in Maghreb, or in the hills of East Africa. In this context, it could be seen that the spatial gradient of precipitation with altitude was not homogeneous over the continent, and to consider altitude in the interpolation framework would require a more regional approach.

<figure><img src="image_167.png" /><figcaption>**Figure 5.** Density of the stations in the HSM_SIEREM rainfall database.</figcaption></figure>

### 2.2.3. Options for Spatialization

The IDW process with ArcInfo lets the user choose between different options of interpolation. Among these, the options used for this study were:

* The default power value was used: 2.

* Priority was given to the radius around the interpolated point, with a value of six times the cell size of the output grid (in this case, each cell was half a square degree; the radius is then three decimal degrees). All the points within the radius were used for the interpolation.

* A minimum of four observation points were required to spatialize. If this minimum was not filled, the radius was extended until the program found four points.

* The output grid cell size was half a square degree. This was the common resolution of the grids used for regional modeling.

* The crest line of the Atlas Mountains is known to stop the humid Atlantic winds. Therefore, a barrier-line was created along this crest line, which modified the spatialization so that the interpolation did not use the values of points that were beyond the barrier line, on both sides.
* The extent of the spatialization process was 18° west, 51.5° east, 37.5° north, and 35° south. If this option was not present, the spatialization stopped at the stations located at the four edges—west, east, north, and south.

The raster grids at a step of 0.5 degree were converted into vector and then intersected to a regular grid at the step of half a square degree. The ArcInfo covers were converted into shape files and the raster grids (the first step of the process) were exported as ASCII files. Both the shape files and the ASCII grids being the products that would be available as free downloads as well as NetCDF arrays.

### 2.2.4. Validation of the IDW Interpolation

To evaluate the robustness of the interpolation method considered here, the interpolation methods of inverse distance weighting (IDW) and ordinary kriging (OK) were validated, using a cross-validation, as in [23]. Each rain gauge was in turn removed and the monthly precipitation was estimated with the remaining stations using the different methods. Only stations with at least 30 years of monthly precipitation data, representing 2912 rain gauges, were considered to validate the methods, to ensure robust estimates of the validation metrics; the Pearson correlation coefficient (r) and the relative bias. For both methods, IDW and OK, a search neighborhood with a 300 km radius was considered to perform the validation. The variograms required for the OK method were fitted with a spherical variogram model for each time-step, when rain was measured for at least four stations, otherwise, the IDW interpolation was performed. The spherical variogram model is convenient for precipitation, as it is not a spatially continuous field like temperature, since it provides a value of the decorrelation distance [24].

The validation results indicated very similar performances between the IDW and the OK, with an average correlation coefficient of validation of 0.86 for the IDW and 0.85 for the OK. The relative bias over all stations was equal to 7.8% with IDW and 7.1% with OK. The spatial patterns of the validation results are very similar, as shown in Figure 6, for the correlation coefficients of the validation samples being obtained with IDW or OK. The areas with the lowest density of stations, such as East Africa or south of the Maghreb countries, had the lowest performances in validation. Therefore, the interpolated precipitation in these areas must be interpreted with care.

<figure><img src="image_168.png" /><figcaption>**Figure 6.** Validation results with independent station data for Inverse Distance (left) and Ordinary Kriging (right).</figcaption></figure>

This validation confirmed that in the presence of a dense network, different interpolation methods (such as IDW or OK) performed with a similar efficiency.
### 2.2.5. Comparison between the CRU and HSM_SIEREM Grids

The grids from the CRU are widely used in models. Paturel et al. [11] compared the results of the GR2M hydrological model, using the rainfall grids from CRU and the ones from the previous versions of the HSM_SIEREM, over Western and Central Africa. The HSM_SIEREM database gave better results. Two mean annual grids over the 1940/1999 period were built, one with the HSM_SIEREM database and one with the CRU grids. The CRU monthly historical climate database, converted to the ESRI ASCII raster format by Jawoo Koo (HarvestChoice/IFPRI—Raw data), was downloaded from http://badc.nerc.ac.uk/data/cru). Figure 7 shows that the main features of the African rainfall distribution were similar.

<figure><img src="image_169.png" /><figcaption>**Figure 7.** Mean annual Climatic Research Unit of University of East Anglia, UK (CRU) and HSM_SIEREM data base over the period of 1940/1999.</figcaption></figure>

We checked the differences between the two mean inter-annual grids of the African continent, over the period of 1940 to 1999 (Figure 8).

The complete grid contained 10,380 cells, among which:

* A total of 4402 cells (in brown and yellow), i.e., 43%, had values ranging from -10% to -15,640%, compared to the HSM_SIEREM grid. They were mostly located in the Sahara and overlapped with the Sahel. In this area, the CRU grid had wide areas with values of mean annual rainfall between 0 and 20 mm, while the HSM_SIEREM ranged from 10 to 50 mm.

* A total of 4708 cells (in white), i.e., 45%, had values between -10% and +10%, compared to the HSM_SIEREM grid, which is very close.

* A total of 1270 cells (in green), i.e., 12%, had higher values, between +10% and +92%, compared to the HSM_SIEREM grid cells, the highest values being located on the southern slope of the Atlas Mountains (due to the barrier-line created in the HSM_SIEREM spatialization) and in Eastern Egypt (Dubief [22] drew a 5 mm isohyet in this area for the 50 first years of the 20th century).

The map of differences in percent showed two main features—in sub-Saharan Africa, the difference between the two grids mainly ranged between -9% and +10%, which could seem quite low, while the difference was much greater over Sahara and most of Northern Africa. In sub-Saharan Africa, some areas of low rainfall also showed a greater difference, as in the South Western coast of Africa and most of the horn of Africa. The CRU grid showed a higher rainfall over the Guinean mounts, the central part of the Congo basin, and the South of Angola. The HSM_SIEREM grid showed a higher rainfall in the South of Ghana, most of Nigeria, Cameroon and Gabon, along the South Western coast of
Africa, and over most of East and South Africa. About the Sahara region, the CRU grid showed values very different from that of the HSM_SIEREM grid. In most of the Sahara the CRU grid showed near 0 values, while the HSM_SIEREM grid showed a significant amount of rainfall, which were coherent with the Dubief rainfall map [25]. The CRU grid also showed too high a rainfall over the Saharan areas, in Egypt, North Chad, South-East Algeria and the Saharan border of the Atlas Mountains.

<figure><img src="image_170.png" /><figcaption>**Figure 8.** Differences of the CRU grid minus the HSM_SIEREM grid, in millimeters on the left and in percent on the right, over the period 1940/1999.</figcaption></figure>

Even in the regions where the difference in percent was the lowest between the two grids (−9% to +10%), there were many areas where the difference was over 50 or 100 millimeters per year, which could have a significant impact on water-balance issues.

The differences between both grids could be due to the difference in the statistical approach of data processing and due to the density of observed stations. According to the figures given in Eklund et al. [26], the number of stations in Africa in the CRU data set was twice lower than that in the HSM_SIEREM data set, between 1940 and 1985, and three times lower after 1985. The maximum number of stations was over 4,000 for the HSM_SIEREM and less than 2000 for the CRU in the mid-seventies. It is quite likely that a greater number of stations will undergo a better precision in rainfall interpolation, at a grid scale.

### 3. Grid Production and Quality Assessment

The interpolation process was run over 720 months; every month was exported as an ASCII grid format file, containing one rainfall value at every 0.5 degree over the African continent; 6 zipped files, each containing 10 years of grids. Additionally, the NetCDF format array containing the 720 months of interpolated values, can be freely downloaded from the HSM website.

This first assessment of the HSM_SIEREM grid content by comparison with the CRU grid gives interesting details about where the use of HSM_SIEREM grid led to substantial differences with the CRU grids. This comparison would worth being completed by a first look at the rainfall variability over Africa, to check whether the main climatic signals are well-depicted by the HSM_SIEREM database. To assess the overall quality of this new dataset, we performed a first climatological analysis of rainfall over the whole continent, over the years 1940/1999.

#### 3.1. *Analysis of Rainfall Inter-Annual Variability*

The standardized precipitation index (SPI) (Figure 9) was calculated, both, with the observed data (Figure 9a) and gridded data (Figure 9b), over the 60-year 1940/1999 period. For the observed data, the SPI was calculated with all stations having at least one complete year of observations. The SPI was
calculated as SPI = (Xi-X)/S; where: Xi is the mean annual rainfall of year i, X is the mean annual rainfall over the reference period, and S is the standard deviation of the rainfall set over the reference period.

<figure><img src="image_171.png" /><figcaption>**Figure 9.** Mean annual rainfall for Africa, standardized precipitation index (SPI) on the observed data (a) and on the HSM_SIEREM gridded values (b).</figcaption></figure>

SPI calculated with both observed and gridded data sets gave very similar time-series variations. It showed that, for the whole period, 1940/1999, and the whole continent, the SPI was almost permanently negative after 1981. This was coherent with most previous results [2,27]. There were some minor discrepancies between the two signals during the 1940s and at the end of the 1990s, which could, first, be linked to the lower number of stations in Africa during these periods, and, second, to their uneven distribution across the continent.

## 3.2. Statistical Tests on Mean Annual Rainfall

These tests, checking the random character of the series, were gathered using the Khronostat software [28], which can be downloaded, free of charge, at http://www.hydrosciences.org/spip.php?article1000. The most widely used tests deal with the stationarity of the mean of the series, throughout its observation period [29]. This test applied on the observed data confirmed a rupture in the series at three confidence levels, 99%, 95%, and 90% (Table 1).

<table><caption>**Table 1.** Results of the Mann-Whitney test, modified by Pettitt [29].</caption><thead><tr><th>Observed Data</th><th>Gridded Data</th></tr></thead><tbody><tr><td>Null hypothesis rejected at confidence levels of 99%, 95%, and 90%</td><td>Null hypothesis rejected at confidence levels of 99%, 95%, and 90%</td></tr><tr><td>Probability of exceeding the critical value 1.93 × 10<sup>-6</sup> in 1979</td><td>Probability of exceeding the critical value 5.44 × 10<sup>-3</sup> in 1969</td></tr></tbody></table>

The results of the statistical analysis of the inter-annual observed rainfall time-series over Africa was slightly surprising, as the 1970s rupture in the rainfall time-series is well known and has been largely described in Western and Central Africa [2,30,31] This meant that what happened in Western Africa and pro parte in the Western part of Central Africa, was more or less specific to this area and not to the whole continent. This should inspire further research on regional variables to explain these climatic features. The fact that the rupture date was found to be in 1969, with the gridded, data might be due to two causes—the first being that the rupture in 1969 might not have been the most prominent signal everywhere in Africa, even if it was present in many places. The second points out the impact of the interpolation method in regions with a high heterogeneity of the distribution of stations, and also the impact of the difference of length of a time-series. Indeed, this rupture in 1969 on the gridded data might be mostly driven by the extension of this signal that was most prominent in Western and Central Africa, due to the lack of data in many other parts of sub-tropical Africa, after 1990. This has already been discussed by Singla et al. [32], for Northern Africa.
3.3. *Monthly Time-Series*

The previous results led to the construction of two new data sets of monthly rainfall for two distinct periods: 1940/1979 and 1980/1999. Then a graph was drawn for the three-monthly time-series (before and after the rupture of 1979, and the entire 60-year period) (Figure 10), for observed and gridded data.

For observed data (in the left, in Figure 10), the graph showed two rainy seasons, February/ay and June–October. The first rainy season occurred in the austral hemisphere and the second occurred in the boreal hemisphere. The second rainy season remained the rainiest and the differences between the three periods were limited. Concerning the first rainy season during the month of March, the monthly values reached during the years 1940/1979 were clearly much higher than those during the years 1980/1999—the mean monthly rainfall value decreased from 90 mm to 64 mm, by almost 30% for the observed values. For the gridded data set (in the right, in Figure 10), values showed the same interdecadal variability, but with lower monthly values and a lower difference between the periods. The difference for March was still significant, with a reduction of 12% after 1980, from 62 mm to 55 mm.

<figure><img src="image_172.png" /><figcaption>**Figure 10.** Seasonal variation of the rainfall for the periods 1940/1999, 1940/1979, and 1980/1999, for observed and gridded data.</figcaption></figure>

Several authors [21,33,34] have already noticed a substantial decrease in rainfall over the southern part of Africa, between the equator and 10° South, but none were found to be as significant as the one shown here.

This first result showed that rainfall had decreased on a continental scale, for Africa, for several decades, and that the monthly rainfall regime did not change much between June and September, i.e., it showed a boreal summer tropical rainy season but changed much more during November to April/May, with a strong decrease that took place during the austral summer and autumn tropical rainfall period. This meant that it was mostly the Southern Hemisphere that seemed to have been affected by this decrease. One must note, however, that due to the rupture being detected in 1979/1980 for the whole continent, the decrease of rainfall that occurred in the Western and in part of Central Africa, since 1969/1970, did not appear clearly at the scale of the continent average. It was nonetheless one of the strongest climatic signals ever recorded in Africa.

4. Discussion and Conclusion

This paper is the result of a long duration of work on the collection, criticism, and assessment of a
very rich and high-quality database of rainfall. This study could be carried out due to the quantity
of data collected during a long period, mainly in Western and Central Africa, by the ORSTOM and
the IRD, and due to the long and thorough criticism process that was applied to the data sets. It is
especially precious, as the African continent has a particularly poor observation network, compared to
the rest of the world.
The HSM_SIEREM rainfall gridded data set showed a very good correlation with the observed data, whatever the interpolation method used. It was compared to the widely used CRU rainfall grid, and their differences have been mapped, to help determine their best use for future studies. The HSM_SIEREM grid was built from twice the number of stations than the CRU grid, the number of stations was even three times higher for the period of 1985/1999, and the distribution of stations better covered the Sahara and some other parts of Africa. This gave the HSM_SIEREM grid more confidence in describing rainfall over Africa, with some regions showing very important differences, especially in the low-rainfall regions, in the Sahara and the Sahel, the Southwestern coast of Africa, and the horn of Africa.

The results confirmed that the African continent was seriously affected by a rainfall deficit and a change in the rainfall regime at the end of the 20th century. The first assessment showed that the two main periods of rupture in the time-series was between 1969/1970 and 1979/1980, which are depicted in the global African rainfall times-series issued from the whole data set, in both, the gridded and observed time-series. It seems that it was the first part of the year, from February to May, which registered the highest rainfall reduction, especially in the austral part of Africa (Figure 11). This reduction was several times higher than the rainfall reduction in the boreal part of Africa, at the same period. Even if part of these figures might be linked to the interpolation of unevenly distributed observed rainfall stations, this could have had strong incidences on the global climate of the area and requires further studies to analyze the regional variability of rainfall over the continent, between different databases.

<figure><img src="image_173.png" /><figcaption>**Figure 11.** Map of the anomalies, Globally Dry Period (1980/1999)–Globally Wet Period (1940/1979). Average period for the months of February to May.</figcaption></figure>

This study also showed that the use of direct observed data for interpolation gave different results and spatial representations than the use of rainfall patterns and anomalies to generate annual maps, which were used by the CRU and many other data centers. The comparison between the spatially-averaged rainfall data and observed data, over several regions of Morocco [32], showed that averaging the data over large surfaces led to a reduced visibility of climatic variability and the
main climatic signals, but the main signals remained visible whatever the size of the region used for
averaging. This supports the idea that the results visible with, both, observed and gridded data sets
are quite consistent.

All monthly grids built during this study are available for free on the HydroSciences Montpellier website and our aim is to contribute to the maintenance of an accurate database for climatic scenarios (http://www.hydrosciences.org/spip.php?rubrique1387/). These grids are already available on the Researchgate website: https://www.researchgate.net/project/Monthly-rainfall-gridded-data-set-for-Africa.

As it is very difficult to gather observed data covering the whole continent since 2000, it is improbable that an update of this study might be performed, knowing that, since then, a number of rainfall gridded product have been released on the basis of satellite-derived rainfall estimations [35], which help fill the gap between the needs of researchers for recent data and the unavailability of a free international observed rainfall database. The length of the time-series was, thus, adapted for a comparison of historical time-series and rainfall time-series from the GCM/RCM reanalysis, or for study of time-series for regions where data are difficult to obtain, even for historical periods.

**Author Contributions:** Conceptualization: C.D., G.M.; Methodology: C.D., J.-E.P., Y.T.; Software: C.D., Y.T., S.E.; Validation: G.M., Y.T., J.-E.P., Y.T.; Formal analysis: C.D., N.R.; Investigation: B.E.M.; Data curation: J.-E.P., S.E., N.R.; Witing—original draft preparation: C.D., G.M.; Writing—review and editing: C.D., G.M., Y.T., J.-E.P.; Project administration: G.M.; Funding acquisition: G.M.

**Acknowledgments:** We thank the CRU of the University of East Anglia for the exchange of raw data at the early stage of this study. We thank the IRD and the HSM Laboratory of Montpellier for the financial and technical support; and the FRIEND program (UNESCO/IHP) for their collaboration through the managers of the databases of the different regional programs in Africa (MEDFRIEND, FRIEND AOC, FRIEND Nile, FRIEND Austral Africa). We thank all the national meteorological services of African countries for their continuous work of data collection, although, we regret that only a few of them accepted to update the data series for the 1990s.

**Conflicts of Interest:** The authors declare no conflict of interest.

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<figure><img src="image_174.png" /></figure>

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
<figure><img src="image_175.png" /></figure>
Review

# Recent Budget of Hydroclimatology and Hydrosedimentology of the Congo River in Central Africa

Alain Laraque <fnref n="1" />*, Guy D. Moukandi N’kaya <fnref n="2" />, Didier Orange <fnref n="3" />, Raphael Tshimanga <fnref n="4" />,
Jean Marie Tshitenge <fnref n="4" />, Gil Mahé <fnref n="5" />, Cyriaque R. Nguimalet <fnref n="6" />, Mark A. Trigg <fnref n="7" />, Santiago Yepez <fnref n="8" />
and Georges Gulemvuga <fnref n="9" />

Received: 15 July 2020; Accepted: 10 September 2020; Published: 18 September 2020

**Abstract:** Although the Congo Basin is still one of the least studied river basins in the world, this paper attempts to provide a multidisciplinary but non-exhaustive synthesis on the general hydrology of the Congo River by highlighting some points of interest and some particular results obtained over a century of surveys and scientific studies. The Congo River is especially marked by its hydrological regularity only interrupted by the wet decade of 1960, which is its major anomaly over nearly 120 years of daily observations. Its interannual flow is 40,500 m<fnref n="3" /> s⁻<fnref n="1" />. This great flow regularity should not hide important spatial variations. As an example, we can cite the Ubangi basin, which is the most northern and the most affected by a reduction in flow, which has been a cause for concern since 1970 and constitutes a serious hindrance for river navigation. With regard to material fluxes, nearly 88 × 10⁶ tonnes of material are exported annually from the Congo Basin to the Atlantic Ocean, composed of 33.6 × 10⁶ tonnes of TSS, 38.1 × 10⁶ tonnes of TDS and 16.2 × 10⁶ tonnes of DOC. In this ancient flat basin, the absence of mountains chains and the extent of its coverage by dense rainforest explains that chemical weathering (10.6 t km⁻<fnref n="2" /> year⁻<fnref n="1" /> of TDS) slightly predominates physical erosion (9.3 t km⁻<fnref n="2" /> year⁻<fnref n="1" /> of TSS), followed by organic production (4.5 t km⁻<fnref n="2" /> year⁻<fnref n="1" /> of DOC). As the interannual mean discharges are similar, it can be assumed that these interannual averages of material fluxes, calculated over the longest period (2006–2017) of monthly monitoring of its sedimentology and bio-physical-chemistry, are therefore representative of the flow record available since 1902 (with the exception of the wet decade of 1960). Spatial heterogeneity within the Congo Basin has made it possible to establish an original hydrological classification of right bank tributaries, which takes into account vegetation cover and lithology to explain their hydrological regimes. Those of the Batéké plateau present a hydroclimatic paradox with hydrological regimes that are among the most stable on the planet, but also with some of the most pristine waters as a

<footnote n="1">¹ Géosciences Environnement Toulouse, Université de Toulouse, CNES, CNRS, IRD, UPS, 31400 Toulouse, France; alain.laraque@ird.fr</footnote>

<footnote n="2">² LMEI/CUSI/ENSP/Marien N’gouabi University, Brazzaville B.P. 69, Republic of the Congo; guymoukandi@yahoo.fr or guy.moukandi@umng CG</footnote>

<footnote n="3">³ University of Montpellier, Eco&Sols-UMR IRD/INRA/Cirad/Supagro, UMR210 IRD, Place Viala (Bt. 12), CEDEX 2, 34060 Montpellier, France; didier.orange@ird.fr</footnote>

<footnote n="4">⁴ Department of Physics, Faculty of Sciences (JMT), University of Kinshasa, B.P. 190 (JMT), Kinshasa XI, Democratic Republic of the Congo; raphtm@yahoo.fr (R.T.); jeanmarie12@gmail.com or Jeanmarie.tshitenge@unikin.ac.cd (J.M.T.)</footnote>

<footnote n="5">5 HydroSciences Montpellier, UMR IRD-CNRS-Université de Montpellier, Maisons des Sciences de l'Eau, 300 avenue Emile Jeanbrau, 34090 Montpellier, France; gil.mahe@ird.fr</footnote>

<footnote n="6">⁶ Geography Department, University of Bangui, Bangui, Central African Republic; cyrunguimalet@gmail.com</footnote>

<footnote n="7">⁷ School of Civil Engineering, University of Leeds, Leeds LS2 9JT, UK; m.trigg@leeds.ac.uk</footnote>

<footnote n="8">⁸ Departamento Manejo de Bosques y Medio Ambiente, Facultad de Ciencias Forestales, Universidad de Concepción—UdeC, Concepción 407374, Chile; syepez@udec.cl</footnote>

<footnote n="9">⁹ International Commission for Congo-Ubangi-Sangha basin (CICOS), Kinshasa B.P. 190, Democratic Republic of Congo; georges_gul@yahoo.fr or georges.gulemvuga@cicos.info</footnote>

<footnote>* Correspondence: alain.laraque@ird.fr; Tel.: +33-476-415-118</footnote>
result of the intense drainage of an immense sandy-sandstone aquifer. This aquifer contributes to the regularity of the Congo River flows, as does the buffer role of the mysterious "Cuvette Centrale". As the study of this last one sector can only be done indirectly, this paper presents its first hydrological regime calculated by inter-gauging station water balance. Without neglecting the indispensable in situ work, the contributions of remote sensing and numerical modelling should be increasingly used to try to circumvent the dramatic lack of field data that persists in this basin.

**Keywords:** hydroclimatology; hydrosedimentology; hydrogeochemical; Congo River Basin

# 1. Introduction

In terms of area and discharge, the Equatorial Congo River Basin is the second largest river in the world and the largest in Africa, but paradoxically remains one of the least known. This basin can still be considered as almost pristine because, given its size, it is relatively less anthropogenicized. This is at least partly due to the lack of transport infrastructure related to the inaccessibility of its large central swampy 'Cuvette' named 'Cuvette Centrale' covered by dense rainforest that occupies the heart of the basin. Shem and Dickinson (2006) [1] observed that, despite its crucial position as the third largest deep convection center in the world, the Congo Basin has not yet received adequate attention in the field of climate and hydrological research.

Considering the importance of the Congo Basin to global fluxes of water, energy, carbon, various suspended or dissolved elements to the ocean and to the atmosphere, and the recent renewed interest of the international scientific community, the purpose of this paper is to present a state-of-the-art review of existing studies and a synthesis of new results, especially on hydroclimatology and hydrosedimentology. Significantly, some passages that are useful for the international scientific community have been translated from previous publications, which unfortunately remain little consulted, despite their importance, mainly because they are written in French, the main spoken language in this basin.

Specifically, it presents and discusses: (i) an overview of the Congo Basin; (ii) the history of hydropluviometric networks in the Congo Basin; (iii) the rainfall and hydrologic features of the whole Congo Basin and four important sub-catchments, which have the longest multi-decadal chronology of data, and represent its mean hydro-ecosystems; (iv) the first indirect estimation of the hydrological regime of the mysterious 'Cuvette Centrale'; (v) water quality sediment transport dynamics on the Congo River from in situ data at its main Brazzaville-Kinshasa Station; and (vi) some hydrological and hydrogeochemical singularities in the Congo catchment. It ends by illuminating the modeling and remote sensing "studies" and their main results and it concludes with the current state of human use and exploitation and the need for a coherent policy for its future protection and management. In this review paper, the methodologies that were used for each parameter studied can be found in the references that are cited as the origin of the results presented.

# 2. Geographical Presentation of the Congo River Basin (CRB)

The Congo is the largest river in the African continent with a basin area of about $3.7 \times 10^6$ km² and a mean annual discharge of $40,500 m^3 s^{-1}$, calculated using 117 years of data, from 1902 to 2019 at its main hydrological station of Brazzaville-Kinshasa. This station represents 97% of the total basin area, is situated at an altitude of 277 m a.s.l., and is 498 km upstream of the river's mouth to the Atlantic Ocean (Figure 1a,e). It is the second-largest river in the world in terms of discharge and catchment area. The 4700-km long Congo River provides half of all the river waters discharged from the African continent to the Atlantic Ocean [2]. The source of the Congo is located at 1420 m a.s.l. in the village of Musofi, south of the Katanga region (southeast of the Democratic Republic of Congo).

In the Northern hemisphere, on the right bank, the Ubangi River is the second-most important tributary in terms of discharge [3], after the major tributary, the Kasai, located further downstream
on the left bank (Figure 1). Just upstream of the major hydrometric stations of Brazzaville-Kinshasa, the Congo flows through Malebo Pool (ex. Stanley Pool), which corresponds to a wide channel reach about 20–25 km long, characterized by numerous sand bars, which emerge during low flows. Between Malebo Pool and its mouth, the Congo falls around 280 m over a distance of about 500 km. Here the many narrow channels create the rapids known as the Livingstone Falls. It is within this section of the river that the only important hydropower plant (Inga) was constructed on the Congo River. The Congo Basin spans nine different countries (Angola, Burundi, Cameroon, Democratic Republic of the Congo (DRC), Central Africa Republic (CAR), Republic of Congo, Rwanda, Tanzania and the Zambia). It comprises several thousands of navigable waterways, depending on the hydrologic period and on the draft of the ships and their tonnage.

The form, relief, geology, climate, and vegetation cover of the Congo Basin (Figure 1a–d) are generally concentrically distributed. Surrounded by savannas on smoothly rounded hills from deeply weathered Mesozoic Precambrian formations [4], less than 44% of the catchment area is covered by rainforest, which promotes the capacity to recycle the basin’s moisture [5]. The basin can be divided into six main hydrologic units (Figure 1c), which are its main distinctive physiographic regions. Their areas and hydroclimatic features are listed in Table 1. Five of the six units are monitored by gauging stations.

The two main overlapping 60-year rainfall study periods are 1940–1999 and 1952–2012, and their data comes from the SIEREM database website (www.hydrosciences.fr/sierem) [6] and BRLi (2016) [7], respectively. This quite concentric shape of Congo watershed presents the following main physiographic characteristics.

Its northern and southern margins are dominated by a set of shield plateaus covered by shrub and tree savannah vegetation and are characterized by a humid tropical transition climate and annual rainfall between 1400 to 1800 mm year⁻¹. The more northerly part of the Congo Basin, which is drained by the Ubangi tributary, is a less humid region, well described by Runge and Nguimalet (2005) [8]. With a total rainfall of around 1500 mm year⁻¹, its watershed benefits from a transitional humid tropical climate with the Sudanese-Sahelian regions farthest north. In the south of the Congo Basin, the sub-basin of the Kasaï River receives rainfall of about 1460 mm year⁻¹.

In the lowlands center of the basin, under an equatorial climate marked by rainfall of 1600 to 1800 mm year⁻¹, the “Cuvette Centrale” is a topographic depression of about 1,176,000 km² [7]. Its floodplain (Figure 1d) covers about 30%, or 360,000 km² [9] of the total basin area. This area is characterized by very small topographical slopes (2 cm km⁻¹) on unconsolidated Cenozoic sediments [4], constituted of alluvial deposits. These formations are covered by rainforests, permanently or periodically flooded, according to the hydro-rainfall cycle. There are also extensive but shallow lakes such as Lac Tumba, Lac Maï Ndombe, etc.

In its northwestern part, the Sangha basin is mainly covered by dense rainforest, and under an equatorial climate receives a mean rainfall ranging of 1625 mm year⁻¹.

In the west, the Batéké plateau constitutes a high-altitude sandstone aquifer recharged by rainfall (1800 to 2000 mm year⁻¹), which controls the hydrological regime of the watercourses draining this aquifer. Both the runoff coefficient (50%) and the specific discharge (close to 35 L s⁻¹ km⁻²) are the highest in this region [10].

In the east is the highest point of the basin, the Karisimbi volcano with 4507 m a.s.l situated in the Virunga mountain chain of the East-African rift. This region is located in the Lualaba basin, which receives between 1110 and 1680 mm year⁻¹ depending on the location [7].

Straddling the Equator, the Congo River shows a poorly-contrasted equatorial regime, with low seasonal discharge variability of 3.3, measured as the ratio between the highest monthly mean discharge of 75,500 m³ s⁻¹ and the lowest of 23,000 m³ s⁻¹. The Congo River also has a relatively low inter-annual variability of 1.66, measured as the ratio between the lowest and highest known annual discharges. The hydrological year, begins in August and is marked by the alternation of two periods of high flows (October–January; April–May) and two periods of low flows (February–March; June–September) and presents an amplitude range of 3.65 m, which can arise 6.54 m at Brazzaville-Kinshasa.
<figure><img src="image_176.png" /></figure>

**Figure 1.** (a) Overview of Congo basin (adapted from Runge, 2007) [4], (b) Land cover on Congo basin [11], (c) Main drainage units studied in Congo basin: Lualaba at Kisangani St., Kasaï at Kutu Moke St., Sangha at Ouesso St., Ubangi at Bangui St., Batéké plateau and ‘Cuvette Centrale’, with their percentage of area of Congo basin represented by the BZV/KIN station. Legend: the dashed line encircles the general area of the ‘Cuvette Centrale’. The area with inclined dark hatching is that of the Batéké plateau, (d) Topography of the Congo Basin and of the flooded portion of the Central Basin. The topography comes from the SRTM [12] and the flooded portion of the Central Cuvette (the black polygon) is generalized by Bwangoy et al. (2010) [9], (e) Longitudinal profiles of the Congo River and its main tributaries (adapted from Runge, 2007) [4].
<table><caption>**Table 1.** The hydro-climatic characteristics in the Congo basin represented at its main hydrological stations [13].</caption><thead><tr><th rowspan="2"></th><th colspan="4">Geographical Coordinates</th><th colspan="4">Basin Area km<sup>2</sup></th><th colspan="4">Hydro-Climatic Characteristics</th></tr><tr><th>Latitude</th><th>Longitude E</th><th>Altitude m</th><th>Station</th><th>Outlet</th><th>Ratio st./ex.</th><th>% CRB at BZV/KIN</th><th>Rainfall mm</th><th>Average Discharge m<sup>3</sup> s<sup>-1</sup></th><th>Interannual Specific Discharge L s<sup>-1</sup> km<sup>2</sup></th><th>Seasonal Variation Flow</th></tr></thead><tbody><tr><td>Lualaba <sup>1</sup></td><td>Kisangani</td><td>00°30′11″ N</td><td>25°11′30″</td><td>373</td><td>974,140</td><td>974,140</td><td>1.00</td><td>26.62</td><td>1307</td><td>7640</td><td>7.8</td><td>1.9</td></tr><tr><td>Kasai <sup>2</sup></td><td>Kutu-Moke</td><td>03°11′50″ S</td><td>17°20′45″</td><td>303</td><td>750,000</td><td>897,540</td><td>0.84</td><td>20.49</td><td>1456</td><td>8070</td><td>10.8</td><td>1.9</td></tr><tr><td>Sangha <sup>3</sup></td><td>Ouesso</td><td>01°37′00″ N</td><td>16°03′00″</td><td>326</td><td>159,480</td><td>213,670</td><td>0.75</td><td>4.36</td><td>1625</td><td>1550</td><td>9.7</td><td>2.3</td></tr><tr><td>Ubangi <sup>4</sup></td><td>Bangui</td><td>04°22′00″ N</td><td>18°35′00″</td><td>345</td><td>494,090</td><td>650,480</td><td>0.76</td><td>13.50</td><td>1499</td><td>3660</td><td>7.4</td><td>2.9</td></tr><tr><td>Bateké Plateau <sup>5</sup></td><td></td><td></td><td></td><td>305</td><td>42,570</td><td>90,000</td><td>0.47</td><td>1.16</td><td>1900</td><td>1330</td><td>31.24</td><td>1.1–1.5</td></tr><tr><td>'Cuvette Centrale <sup>6</sup></td><td></td><td></td><td></td><td>305–335</td><td>1,192,190</td><td></td><td></td><td>32.57</td><td>1700</td><td></td><td></td><td></td></tr><tr><td>Congo <sup>7</sup></td><td>BZV/KIN</td><td>04°16′21.5″ S</td><td>15°17′37.2″</td><td>314</td><td>3,659,900</td><td>3,730,740</td><td>0.98</td><td>100</td><td>1447</td><td>40,500</td><td>11.07</td><td>1.7</td></tr></tbody></table>

The interannual modules are calculated at the different periods studied: ¹ 1951 to 2012, ² 1948 to 2012, ³ 1947 to 2018, ⁴ 1936 to 2018, ⁵⁶⁶ 1947 to 1994, and ⁷ 1903 to 2018. For the 1940–1999 period, the rainfall comes from the SIEREM database website [6], except for ⁵ & ⁶ from Laraque et al. (1998) [10]. Legend: st. = hydrometric station, ex. = outlet. The hydro-climatic characteristics of the Forest Central Basin and the Bateké plateau are derived from Laraque et Maziezoula (1995) [1] and Laraque et al. (1998 and 2009) [10,15], about right bank tributaries of Congo River represented by hydrologic stations. The coordinates X, Y, Z are those of the hydrological stations.
### 3. Brief History of Hydropluviometric Networks in the Congo Basin

The hydropluviometric networks originate from the colonial era, when Belgium occupied the left bank of the river, which is now the Democratic Republic of Congo (DRC) and France occupied the right bank, now known as the Central African Republic (CAR), the Republic of Congo, and part of Cameroon.

#### 3.1. Rainfall Network

ORSTOM (Office de Recherche Scientifique et Technique d'Outre Mer), known today as IRD (Institut de Recherche pour le Développement), participated in the management of the network of rainfall measurements on the right bank of the Congo river since the 1940s until the independence of the Republic of Congo, the Central African Republic, and Cameroon.

After the independence of these countries at the beginning of the 1960s, climate surveys were continued by ASECNA (Agency for Aerial Navigation Safety in Africa and Madagascar) and by the National Meteorological Departments.

In the DRC, due to the importance of the rainforest vegetation and the very few developed road networks, the density of the rainfall network is scarce in most of the central part of the basin, except on the Ubangi sub-basin area (Figure 2a). Observed rainfall data are also scarce in Angola. Observation data are a little more numerous in Zambia and Tanzania, but rainfall data are poorly available during and after the last years of the 20th century in most of these countries, due to a general decline in support of the networks. In CAR, the pluviometric database was completed through the PIRAT and PEGI programs [16] and then Callède et al. (2009) [17] produced a monograph on the Ubangi basin. In this basin, Nguimalet and Orange (2019) [3] studied a rainfall series from 1940 to 2015.

In the entire Congo Basin, a compilation of available and accessible rainfall data could only be carried out for the period 1940–1999, from the free access SIEREM database website [6] and the annual rainfall map of Africa (Figure 2b) [18] is today the last reference for this basin in addition to the older reference of Bultot (1971) [19].

<figure><img src="image_177.png" /><figcaption>**Figure 2.** (a) Density of observed rainfall stations over the Congo River Basin area [18], (b) Inter-annual averaged rainfall over the Congo River Basin, period 1940–1999, from the SIEREM database [6,20].</figcaption></figure>

#### 3.2. Discharges Network

In the entire Congo Basin, during the first half of the 20th century, there were more than 400 functional hydrometric stations (Figure 3a). Since the basin countries' independence, only fifteen are still operational today (Figure 3b). On the left bank, the hydrometric network was more or less
abandoned. It was maintained on the right bank tributaries until the end of the 20th century by ORSTOM (ex. IRD), which had created hydro-pluviometric networks during the 1940s. However, the National Inland Navigation Services, RVF (Régie des Voies Fluviales) on the left bank, and GIE-SCEVN (Groupement d'Intérêt Economique—Service Commun d'Entretien des Voies Navigables) on the right bank, have continued to this day the hydrometric surveys of some stations located on the navigable routes. The CICOS (Commission Internationale du bassin Congo-Oubangui-Sangha) is responsible for the IWRM (Integrated Water Resources Management) within the Basin. Cameroon is the only country to have created a real operational NHS (National Hydrological Service).

<figure><img src="image_178.png" /><figcaption>**Figure 3.** Hydrological network in Congo basin. (a) Past, before 1960 versus (b) Actual, after 1960 [7].</figcaption></figure>

An inventory of existing data series can be found on the GRDC (Global Runoff Data Center) [21] and on the SIEREM website [6,20]. Thanks to BRLi (2016) [7] with the authorization of the CICOS, the Observatory Service SO-HYBAM [22] presents some daily and monthly hydrometric data today realized by the GIE-SCEVN and the RVF, on the five key stations with regular measurements from the beginning and from the second half of the past century, which are chosen for this study:

(1) The main station of Brazzaville-Kinshasa (BZV/KIN) on Congo River represents 97% of the entire Congo Basin. Measurements began in March 1902 in Kinshasa on its left bank, while measurements at Brazzaville on the other bank, opposite of Kinshasa, began in 1947.

Observed data on the Congo River, at the Kinshasa left bank station (1902–1983) are available from the GRDC and in the Mateba 22 report (1984) [23].

These data have been used to extend the water-stage data series from the Brazzaville station on the right bank, back to 1902 [14,24] to produce a single time series discharge of 117 years from 1902 to today, available on https://hybam(obs-mip.fr/) [22].

(2) On the mainstream of the Congo River (Figure 1), apart from the main gauging station of Brazzaville-Kinshasa (BZV/KIN), there exists only the Kisangani gauging station with a good daily discharge data record, for the period from 1951–2012 [7]. From upstream of the Bayoma falls (ex. Stanley Falls) at Kisangani, the river is known as Lualaba River.

(3) There are very little discharge data for left bank tributaries, although at many former gauging stations, water heights have been acquired during several decades. However, they have no known discharge time series associated with them, due mainly to the lack of gauging and rating curves. Fortunately, daily discharge from the Kasai River, the largest tributary of Congo, are available at its main Kutu Moke station for the period of 1948–2012 [7].

The main right bank hydrometric stations that are still active, and with a long time series of daily discharges are:
(4) Bangui on the Ubangi River (the second largest tributary to the Congo River, available from 1936) and,

(5) Ouesso on the Sangha River, available from 1948, both on https://hybam obs-mip.fr/ [22].

A remarkable gap remains in understanding the hydro-climate processes in this region, thus leading to uncertainties and risks in decision-making for the major water resources development plans currently under discussion.

### 3.3. TSS, TDS, DOC Data

From 2005, with the continuous efforts of French and Congolese hydrologists, the SO-HYBAM (Observatory Service of larges rivers around the Atlantic intertropical ocean) was launched in the Congo Basin to preserve and continue acquisition of numerous varieties of data at the station of Brazzaville, such as daily discharge and monthly concentrations of total suspended solids (TSS), total dissolved solid (TDS), dissolved organic carbon (DOC), trace and rare earth elements, and also to ensure a free dissemination of data to the wider community [22].

This latest hydro-sediment-geo-chemical database at Brazzaville Station was completed with similar data coming from the previous PEGI/GBF (Programme Environnement Géosphère Intertropicale/opération Grands Bassins Fluviaux) Research program that monitored 10 stations on the right bank tributaries of the Congo River initially compiled and presented in the work of Laraque et al. (1995) [25]; Laraque et Orange (1996) [26]; Laraque et al. (1996 and 1996) [27,28], and Orange et al. (1996 and 1996) [29,30].

A non-exhaustive bibliographical summary of the work on the five categories of data mentioned above (Rainfall, flow and 'TSS, TDS, DOC') is presented in the next chapter.

## 4. State-of-the Art Hydroclimatological and Hydrosedimentological Studies of the Congo River Basin

### 4.1. Hydroclimatological Studies

Over the entire Congo Basin, the annual average of rainfall for the 1940–99 period is 1500 mm year⁻¹ [31,32]. The rainfall variability over the basin has been studied by many authors [18,33–38]. For example, Riou (1983) [39] studied the potential evapotranspiration in Central Africa. Samba Kimbata (1991) [40] quantified the water budget between 1951 and 1980 using monthly data, and Matsuyama et al. (1994) [41] studied the relationship between seasonal variations in the Congo Basin's water balance and the atmospheric vapor flow. From the Congo Basin's left bank data, Kazadi and Kaoru (1996) [42] proposed that the climatic variations over this basin can be related to solar activity. However, if the hydro-climatology of the basin presents similar cycles of 10–12 years, starting from the second half of the 20th century as shown by Laraque et al. (2013) [43], how can we explain their absence during the first half of this same century? Orange et al. (1997) [44] and Mahé et al. (2000, 2013) [45,46] linked the effects of the rainfall shortage since the 70s drought with the reduction of the groundwater reserves, leading to a decrease in the discharges in both humid and dry seasons. On the basis of the evolution of the regimes of major West and Central African rivers [47,48], some authors such as Olivry et al. (1993) [49], Mahé and Olivry (1999, 1995) [2,50] have noticed that the discharge reduction during the last decades is stronger in tropical Sudanian areas than in humid Equatorial areas, thus having an impact on the northern part of the Congo Basin.

Bricquet (1995) [51], categorized the main types of hydrograph that exist in the entire Congo Basin and proposed maps (Figure 4) to identify the hydrology contributions and times of water transit from upstream to downstream, according to different locations in the basin and during the different phases of the hydrological cycle of the Congo River at the Brazzaville-Kinshasa station. One of his maps shows the spatially contrasting values of specific flows, which can vary between extremes values of 0.5 (outlet of Lake Tanganyika) to 35 L s⁻¹ km⁻² (Batéké plateau).
<figure><img src="image_179.png" /><figcaption>**Figure 4.** Participation of the different hydrological zones of the Congo Basin in the flow observed in Brazzaville/Kinshasa at different periods of the hydrological cycle (modified from Bricquet, 1995) [51] in March (a), May (b), August (c), and December (d). The area of the circles is proportional to the flow, and the percentage specifies the relative contribution of each zone to the flow in Brazzaville. For each zone, the flow retained takes into account its transfer time at the outlet. The units values are in m³ s⁻¹.</figcaption></figure>

In their study of the inter-tropical regions of Africa during the 1950–90s period, Bricquet et al. (1997) [52] highlights, that since the 1970s there has been a hydrological step change, which is thought to be a memory effect of the drought in the flow of the large river basins, reflecting a change in contribution of base flow to the flood hydrograph. Bricquet et al. (1997) [52] specify that this effect is more accentuated in the Sudano/Sahelian region. In the equatorial area, where the example studied was the Sangha River in Congo Basin, the secondary flood (named also spring flood) was more affected than the annual principal flood (or autumn flood). The authors hypothesize that there is a specific stream drought, which could be referred to as a “phreatic drought”, in addition to the climatic drought.

Nguimalet and Orange (2019) [3] suggest that rainfall runoff ratios over the Ubangi Basin at Bangui do not vary in time and also comment on the impact of phreatic drought. They studied rainfall data from 1935 to 2015 over the basin and confirmed a single discontinuity in 1971 causing a decrease (<5%) in the rainfall data series (Figure 5). As Laraque et al. (2013) [42], they identified three hydrological discontinuities (1960, 1971, 1982), with the strongest decrease of 22% in 1982. At last, they underlined a probable change in the relationship between surface flows and groundwater flows, before and after 1970 and after 2000.

Moukandi N’kaya et al. (accepted and in press) [13] show that the high flow period of the 1960s is the major hydrological anomaly of the Congo River over its 116 years continuous record. They show that the hydropluviometric discontinuity in 1970 is common in most of the tributaries of the Congo River basin, accompanied by significant reductions in flows, depending on various factors (geographical location, vegetation cover, surface conditions and land use, etc.).
<figure><img src="image_180.png" /><figcaption>**Figure 5.** Discontinuities in the annual rainfall series for Ubangi basin, which comes from Nguimalet & Orange (2019) [3].</figcaption></figure>

Although this synthesis prioritizes field data, it is worth mentioning the complementary studies of Lee et al. (2011, 2014) [53,54] focused on terrestrial water dynamics on the "Cuvette Centrale" using gravimetry data from the GRACE satellite and also using satellite radar altimetry. Becker et al. (2014) [55] presents the largest altimetry dataset of water levels ever published over the entire Congo Basin. This dataset, based on a total of 140 water level time series extracted using ENVISAT altimetry over the period of 2003 to 2009, allows illustration of the hydrological regimes of various tributaries of the Congo River. This analysis reveals nine distinct hydrological regions, a number similar to the ten zones of Bricquet (1995) [51], based on fields measurement. The proposed regionalization scheme is validated and therefore considered reliable for estimating monthly water level variations in the Congo Basin.

## 4.2. *Hydrosedimentological and Biogeochemical Studies*

Here, we consider TSS measurement and its extension to the other main complementary parameters like TDS, DOC, pH and EC (Electrical Conductivity).

Within the Congo Basin, the first solid (TSS) and dissolved transport (TDS + DOC) analyses were carried out during the first half of the 20th century at the main hydrological station of BZV/KIN. Table 3 summarizes their results in chronological order.

For BZV/KIN, this paper presents the mean results of the study of Moukandi N’Kaya et al. (2020) [56], about the comparison of the only two longer multiannual monthly chronicles of available data (1987–1993 versus 2006–2017) issued respectively from two French-Congolese projects, i.e., 8 years (1987–1993) of PEGI and 12 years of SO-HYBAM (during 2006–2017 period), the latter still operating. The parameters studied are TSS, TDS, DOC, pH and EC.

# 5. Recent Results

## 5.1. *Spatially Interpolated Rainfall Data*

The collected rainfall data were stored, analyzed, and spatialized using the inverse distance weighted method to obtain a rainfall value for every half a square degree (Figure 2b). Over the 60 year period 1940–1999, the inter-annual averaged rainfall on the Congo Basin ranges between 1000 and 2000 mm year⁻¹. Over one-third of the surface of the basin receives more than 1500 mm year⁻¹ of
rainfall, mainly in the area of the “Cuvette Centrale”. The monthly rainfall regime (Figure 6) has changed slightly over time, as during the last 20 years of the time series, rainfall has decreased from February to April, while it has increased in July and August, and did not change much from September to December. This decrease of rainfall during the ‘Spring’ has provoked a decrease of the ‘Spring’ flood, which has also been observed in other equatorial rivers like the Ogooué River in Gabon and the Kouilou-Niari River in Congo [46,57], and the South Cameroon rivers [58].

<figure><img src="image_181.png" /><figcaption>**Figure 6.** Mean monthly rainfall regime on the Congo River Basin over two different hydroclimatic periods (1940–1979 versus 1980–1999) and over the whole 1940–1999 period.</figcaption></figure>

Two sets of gridded rainfall data have been used, from CRU and from SIEREM_HSM [32]; both time series are quite close, except for the 1940s where SIEREM is slightly higher, and the 1980s where the differences are a bit higher. These two periods correspond to low density of the observed stations, which can explain these differences when the interpolation exacerbates some smaller differences between raw data in each grid. However, a comparison with the Congo discharge time series shows that its variability closely follows the SIEREM rainfall time series. Therefore, for this study, we use the SIEREM gridded data, which are based on a larger number of observed stations.

## 5.2. Stationarity and Step Changes in Time Series and Wavelet Analysis of Rainfall Series

Statistical tests were performed on rainfall time series to detect segments, indicated by step changes, in the rainfall regimes [59]. Hubert’s segmentation of regionalized annual rainfall (Figure 7) shows four steps in the time series, which occurred in 1970, 1983, 1987, and 1990. The main change in time series over the 60 years is found in 1983, which corresponds to the worst drought year in West and Central Africa in the 20th century. The time series presented in Figure 7 shows an increase of annual rainfall in 1987 and 1988, which seems exaggerated, as this change is not observed in the discharge time series, even if the discharge of 1988 shows a slightly higher value than the other years of the 1980s. This difference is mainly due to the lack of data over large parts of the Congo basin since the end of the 1980s.
<figure><img src="image_182.png" /><figcaption>**Figure 7.** Regionalized annual rainfall over the whole Congo River Basin with phases of homogeneous rainfall (horizontal red lines). CRU (black) and SIEREM (green) gridded data set are compared, together with discharges time series (blue).</figcaption></figure>

Following the work of Moron et al. (1995) [60], linking rainfall variability in sub-equatorial America and Africa with STT (Sea Surface Temperature), it is interesting to operate wavelet analysis on this rainfall time series, to detect periods with higher energy and possible associated features. The spectrum analysis (Figure 8) reveals a frequency shift that occurs by the mid-1970s, from the 4–8 year band to the 8–16 year band, and in 1983, when the 4–8 year band re-appears while the 8–16 year band is still observed. This increase of the energy since the mid-70s in the 8–16 years band also corresponds to a high level of correlation between annual rainfall and SST in the South Atlantic [61]. Both CRU and SIEREM databases give the same period for this shift, around the mid-70s, but there is a difference after this date, i.e., the CRU rainfall does not show a signal in the 8–16 band, which is the case for the SIEREM base, and moreover, the SIEREM base also shows a return of a signal in the 4–8 band from the mid-80s onward.

<figure><img src="image_183.png" /><figcaption>**Figure 8.** Graph overlay of (a) Global wavelet spectrum and (b) wavelet spectrum of the regionalized monthly rainfall over the Congo river basin (from CRU (dark line) and SIEREMv1 gridded rainfall) (gray line) (1940–1999).</figcaption></figure>

### 5.3. Rainfall and Discharge Trends in the Congo River Basin

In Figure 9a,b, the graphs illustrate the synthesis of the study of Moukandi N’Kaya et al. (accepted and in press) [13] on the trends of multi-decadal rainfall records and the evolution of hydrological regimes during different homogeneous periods within the Congo Basin.
<figure><img src="image_184.png" /><figcaption>**Figure 9.** Graph discontinuities in the annual (a) rainfall and (b) hydrological chronicles of the five main drainage units studied from field data, including that of the entire Congo Basin. Legend: Values in italics are the interannual averages of the entirety of each series studied. (Rainfall series comes from www.hydrosciences.fr/sierem) [6] and Discharges series from https://hybam.obs-mip.fr/database [22].</figcaption></figure>

The multi-decadal rainfall and hydrological records were analyzed by various statistical tests available in Khronostat software [62,63]. The tests included in the software are largely taken from the technical note of the World Meteorological Organization [64]. The description of these tests is presented in Laraque et al. (2013 and 2001) [42,65] and Moukandi N’Kaya et al. (accepted and in press) [13].

This preliminary study by catchment area will then make it possible to compare their hydro-pluviometric behaviors for the purpose of better understanding the hydrological responses within the Congo River Basin (CRB).

It can be concluded for the rainfall records between 1940 and 1999 (except for Ubangi basin with data from 1936 to 2018), that:
(i) the rains have globally decreased on the Ubangi, Kasaï, and entire Congo Basin, with the main change around 1970. Nevertheless, for the Ubangi basin, Gnuimalet et Orange (under review) [66] note a resumption of rainfall from 2007 onwards (Figure 5);

(ii) the Lualaba basin shows high rainfall in the early 1960s and a decreasing rainfall trend around the 1980s;

(iii) in the Sangha basin, annual rainfall has not significantly changed;

(iv) for all the basins analyzed, the end of the 1980s and the beginning of the 1990s seem to have experienced relatively more rain, in a context of declining rainfall since 1970;

(v) trends and changes in the rainfall can also be seen in the runoff record, sometimes with a time lag of a few years.

In Equatorial Africa, rainfall has returned to previous annual total and the runoff variability does not show any long-term trend, compared to the high runoff decrease observed in West African rivers. After the 1980s' drought years, the runoff of the Central African rivers returns to average values. The Congo River presents a certain hydrological inertia due to its vast surface area distributed on both sides of the Equator, more than 40% of which is covered by rainforest under the influence of the Atlantic and Indian Oceans and continental southern Africa.

The statistical "sequencing" of river flow records available (Figure 9b), shows one to three discontinuities, depending on the river analyzed (1970 for the equatorial Sangha River at Ouesso; 1960, 1971, 1983 for Ubangi at Bangui; 1960, 1964 for Lualaba at Kisangani; 1980 and 1990 for Kasaï at Kutu Moke and 1960, 1970 for Congo River at BZV/KIN gauging stations. Unfortunately, due to the lack of in situ data, one cannot estimate with accuracy, how river flows are changing on the left-bank tributaries since 2012. However, for the Kasaï basin, the later occurrence of hydrologic discontinuities in comparison with the rainfall ones can be explained mainly by a lag in groundwater flows.

The first analysis of the 1902–1996 record of annual discharge of the Congo River at Brazzaville and of the Ubangi River at Bangui made by Laraque et al. (2001) [65] identifies four homogeneous periods: the first period before 1960 showed no significant trend; 1960–70 experienced high flows and was followed by two periods of successive flow drops, in 1971–81 and 1982–95, the periods of lowest flows ever recorded.

The most recent analysis of the longest hydrological series of the Congo River at BZV/KIN and the Ubangi River at Bangui until 2019 (Figure 9b), shows that the wet decade of the 1960s with an interannual discharge of 48,000 m³ s⁻¹ separates two periods of equivalent mean flows for Congo River, 39,710 versus 39,740 m³ s⁻¹ [13].

Indeed, since 1996, measured values are close to the overall mean annual discharge of 40,500 m³ s⁻¹, and a renewal of runoff is observed from 1990 onwards, after almost 15 years of deficit flow.

For the Ubangi River at Bangui, the same wet decade of the 1960s preceded a decline in flows in 1970, which in 1981 became more pronounced and remained stabilized until 2013; it seems to have a rising wave from this recent date.

In recent years, the presence of monthly extremes has been noted. For example, in 2011, the lowest levels for 65 years were observed in Brazzaville (with 23,550 m³ s⁻¹ in August) and in 2012, the Ubangi reached its lowest level for 100 years (with 207 m³ s⁻¹ in April), with new sandbanks appearing, which present fluvial navigation obstacles.

The end of 2019 was marked by an extreme rising stage on Ubangi River at Bangui, with a daily discharge of 12,400 m³ s⁻¹ in November, which correspond to a return period of a dozen years. This event seems to confirm a new humid period announced by the discontinuity in 2014 [3]. One month later, this flood reached Brazzaville-Kinshasa, where the maximum December discharge of the annual hydrologic cycle reached 70,000 m³ s⁻¹ for an mean monthly flow in this month of around 56,000 m³ s⁻¹. The high flow events caused catastrophic flooding and affected thousands of people in the three capitals of Bangui, Brazzaville, and Kinshasa, in addition to the damage suffered by others localities along the rivers' courses.
These hydropluviometric recoveries are found in other regions of Africa like in the endoreic basin of Lake Chad [67] and in East Africa [68].

### 5.4. Hydrological Regimes of the Congo River and the Special Case of Ubangi River

Mahé et al. (2013) [46] highlighted that in West Africa and in a part of Central Africa, climate has changed since 1970, with the beginning of an intense and long dry period, but this change is less active since the 1980s in Central Africa. Nevertheless, a change in the intra-seasonal rainfall distribution (a slight rainfall decrease from March to May) most probably led to one of the biggest changes in the hydrological regimes in Africa, mainly visible in the Ogooue, Kouilou-Niari, and South Cameroonian rivers [58], but also visible partly in the Congo River. This reduction of the spring flood since the 1980s is therefore visible at the sub-continental scale. It might be associated with a change in global weather regime, linked to ITCZ (Intertropical Convergence Zone) changes in seasonal variability.

Figure 10 shows the hydrological regimes at the main gauging stations of the six main drainage units of the Congo Basin and of the whole Congo Basin at BZV/KIN.

<figure><img src="image_185.png" /><figcaption>**Figure 10.** Graph Comparison of the hydrological regimes of the six main drainage units of the Congo Basin and of the whole Congo basin [13]. Legend: the dashed line encircles the 'Cuvette Centrale'.</figcaption></figure>

The hydrological regime of the remote, swampy 'Cuvette Centrale', which is very difficult to measure in situ, has been deduced from the budget between the BZV/KIN gauging station and the other main stations (Ouesso, Bangui, Kisangani, Kutu Moke, ...) with long hydrological records available, situated around its location. The results are in agreement with the reconstitution by hydropluviometric modelling made by BRLi (2016) [7]. The Congo River regime at BZV/KIN is the combination of the alternating inputs of its tributaries from both hemispheres. The local regime is strongly influenced and attenuated by the more regular equatorial regime of the Central Basin. Although its flows are much lower, the stable hydrological regime of the Batéké plateau (illustrated here with Nkéni R.) also mitigates low flows in the Congo River.

The evolution of hydrological regimes by periods of homogeneous flow for the studied drainage entities, according to their available flow records are presented in Moukandi N'kaya et al. (accepted and in press) [13]. The two longest records (Congo at BZV/KIN and Ubangi at Bangui) are now detailed.

The Congo River at BZV/KIN (Figure 11a) has a bimodal regime with its rising stage usually starting in October and ending in February. Afterwards, a large recession period begins and ends in
October. During this recession stage, a secondary smaller flood appears with a slightly rounded peak in May.

<figure><img src="image_186.png" /><figcaption>**Figure 11.** (a) Changes in mean daily hydrological regimes by homogeneous flow periods for Congo River at BZV/KIN, (b) and Ubangi at Bangui. The total periods of available daily Q are presented for each station.</figcaption></figure>

The variations of hydrological regimes by periods of homogeneous flows of the Congo River in BZV/KIN (Figure 11a) does not show any clear trend except for the 1960s humid period, with runoff during all the hydrological cycle always higher than that of the two other homogenized periods. During the wet phase from 1960 to 1970, the secondary flood greatly exceeded the interannual mean discharge calculated since 1903, but since 1970, it has fallen below this.

The Ubangi River is monitored by the Bangui gauging station in the northern hemisphere and flow peaks are in end November-early-December. The falling stage occupies the rest of the year and lasts seven months.

In contrast to Congo River at BZV/KIN (Figure 11a), the variations of hydrological regimes by periods of homogeneous flows of the Ubangi River at Bangui (Figure 11b) shows clear trend, except for the 1960s humid period, with runoff during all the hydrological cycle always higher than from the others ones. The volume of water flowing out of the Ubangi almost halved between 1936 and 2018. Since 1982, the current period presents an important decrease of total runoff. This is due to the reduction of the magnitude and duration of the floods, but also due to a reduction of low flows. It is this latter period that presents the most altered hydrological regime for the Ubangi River.

As a complement to the study of Bricquet et al. (1997) [52], which showed the increase of its baseflow recession constant since the 1970s' break, Nguimalet and Orange (2019) [3] carried out some analyses of one hydropluviometric time series from 1935 to 2015 for the Ubangi River.

The average rainfall-runoff relationship did not change during the four hydroclimatic periods already identified by Laraque et al. (2013) [43], suggesting that the hydrological functioning of the Ubangi has not changed during the climate disruption. However, this is not the same pattern seen from its annual depletion coefficients, which continuously increased from 1935 to 2015 with an emphasis in 1970, but has declined since (Figure 12a). Nothing in the recent evolution of the annual rainfall helps explain this change of trend. The recent changes in the baseflow recession constant indicate that the support of baseflows by groundwater inflows is no longer assured. For this river, Nguimalet and Orange (2019) [3] showed the parallel responses of the changes of daily flood flows, as well as mean and low annual flows (Figure 12b). Nevertheless, these authors find that its mean daily flow in Bangui fell by 30% between the wet (1959–1970) and dry (1982–2013) periods. On the other hand, the mean daily low water flow fell by about 60%, which probably underlines a significant drop in the contribution of the water table of the Ubangi basin.
<figure><img src="image_187.png" /><figcaption>**Figure 12.** (a) Interannual evolution of the baseflow recession constant of the Ubangi River at Bangui and of the volume mobilized by the groundwater contribution of its catchment, from 1935 to 2015 hydrological cycles [3]. (b) Comparative evolution of annual (Qyear), maximum daily flood (Qmax), and minimum daily flood (Qmini) of the Ubangi at Bangui, from 1935 to 2015 [3].</figcaption></figure>

5.5. *Main Features, Concentrations, and Fluxes of the Congo River*

On the Congo River, in the year 1980, African and French researchers continued the studies pioneered by Belgian scientists on TSS and TDS, by Van Mierlo (1926) [69] and Spronck (1941) [70] and then later on DOC by Eisma et al. (1978) [71]. Figure 13 presents their historical and recent results, also available in Table 3. These show that since 1967, a majority of authors, 12 versus 19, present TSS values with a relatively good convergence, between 20 and 30 mg L⁻¹. For TDS, it is similar, with 13 versus 16 authors presenting values between 30 and 40 mg L⁻¹ since 1968. For DOC, all the authors found values between 8 and 13 mg L⁻¹ for analysis carried out from 1982. For TSS and TDS, the measurement of pre-1968 data results from less accurate sampling protocols and laboratory analyses, with samples that do not regularly cover the entire hydrological cycle.

Moukandi et al. (2020) [56] show that in comparison with the PEGI data, SO-HYBAM data presents a slight increase (+7.5%) of interannual mean TSS concentrations, from 25.3 to 27.2 mg L⁻¹, a lower TDS concentration in the current period, i.e., -15% with a drop from 36.5 to 31.1 mg L⁻¹ and a DOC concentration increase (+28%) from 9.9 to 12.8 mg L⁻¹. These variations appear to be essentially due to the increase in flows (nearly 4.5%), from 38,000 to 39,660 m³ s⁻¹ (inter-annual average), respectively for the two study periods. Indeed, generally the increase in discharge causes an increase of TSS by basin leaching and a decrease of TDS by dilution effect. For DOC, always in the absence of significant anthropogenic impacts in the Congo Basin, we suspect that its relatively high increase is due to the leaching of the vast 'Cuvette Centrale' covered by flooded forests and swamps rich in organic matter, with more water during the recent period (2006–2017).

We present in Table 2 the main statistics of Congo River water quality parameters collected by the SO-HYBAM project at the BZV/KIN gauging station.

The Congo River at the BZV/KIN section is characterized by a slightly acid mean pH value of 6.8, a mean specific electrical conductivity of 28.4 µS cm⁻¹ at 25 °C, and a mean temperature of 27.7 °C. The total matter transported (TSS + TDS + DOC) is around 71 mg L⁻¹, with 38% TSS (27.2 mg L⁻¹) and 62% of Total Dissolved Matter (TDM = TDS + DOC = 43.8 mg L⁻¹). TDM comprises 29% of DOC (12.7 mg L⁻¹) and 71% of TDS (31.1 mg L⁻¹). The TDS comprises 20.2 mg L⁻¹ of ionic load (cations + anions) and 10.9 mg L⁻¹ of neutral mineral oxides, in which silica concentrations (10.5 mg L⁻¹) predominate. Consequently, the Congo River channel surface waters at Brazzaville contain relatively low concentrations of TSS and ionic load, but are rich in concentrations of SiO₂ and DOC with a mixed-bicarbonate geochemical facies.
<figure><img src="image_188.png" /><figcaption>(a)</figcaption></figure>

<figure><img src="image_189.png" /><figcaption>(b)</figcaption></figure>

<figure><img src="image_190.png" /><figcaption>(c)</figcaption></figure>

Figure 13. (a) TSS, (b) TDS, and (c) DOC concentrations versus time on the Congo River at BZV/KIN from different authors.

Unfortunately, POC (Particulate Organic Carbon) was not sampled during the SO-HYBAM project, but was studied by a few authors like Coynel et al. (2005) [72] and Spencer et al. (2012 and 2016) [73,74]. This last author carried out a monthly study for the period from September 2009 to November 2010 and found a mean POC concentration of 1.46 mg L⁻¹ with extreme low and high values of 1.01 and
1.97 mg L⁻¹, respectively. For comparison purposes, from 1992 to 1993, Seyler et al. (2006) [75] found an almost constant POC on the Ubangi River at Bangui, around 1.4 mg L⁻¹. More recently, based on 15-day sampling over the hydrological cycle (2011–2012), Bouillon et al. (2014) [76] found a lower average of 0.9 mg L⁻¹.

<table><caption>**Table 2.** Interannual Yearly discharge (Qy) and main surface water quality parameters for the Congo River at BZV/KIN gauging station, for the 2006–2017 period.</caption><thead><tr><th>Parameters & Units</th><th>Mean ± Std</th><th>Max</th><th>Min</th><th>Max/Min</th></tr></thead><tbody><tr><td>Qy (m³ s⁻¹)</td><td>39,660 ± 8.70</td><td>61,330</td><td>22,710</td><td>2.7</td></tr><tr><td>Temperature (T °C)</td><td>27.7 ± 1.6</td><td>31.7</td><td>20.0</td><td>1.6</td></tr><tr><td>pH</td><td>6.8 ± 0.7</td><td>8.9</td><td>5.1</td><td>1.8</td></tr><tr><td>EC (µs cm⁻¹ at 25 °C)</td><td>28.4 ± 4.98</td><td>36.0</td><td>20.0</td><td>1.8</td></tr><tr><td>Ionic load (mg L⁻¹)</td><td>20.3 ± 4.1</td><td>27.6</td><td>11.6</td><td>2.4</td></tr><tr><td>SiO₂</td><td>10.5 ± 1.1</td><td>14.4</td><td>5.7</td><td>2.5</td></tr><tr><td>TSS (mg L⁻¹)</td><td>27.2 ± 7.9</td><td>53.2</td><td>10.6</td><td>5.0</td></tr><tr><td>TDS (mg L⁻¹)</td><td>31.1 ± 3.8</td><td>39.5</td><td>18.2</td><td>2.2</td></tr><tr><td>DOC (mg L⁻¹)</td><td>12.7 ± 5.0</td><td>29.3</td><td>5.2</td><td>5.6</td></tr><tr><td>TOTAL (mg L⁻¹)</td><td>71.0</td><td>-</td><td>-</td><td>-</td></tr></tbody></table>

TOTAL = TDS + DOC + TSS; Std: Standard deviation.

<table><caption>**Table 3.** Chronological compilation of TSS, TDS, and DOC concentrations since the beginning of the studies on Congo River quality at Brazzaville/Kinshasa gauging stations.</caption><thead><tr><th>References</th><th>TSS<br>(mg L<sup>-1</sup>)</th><th>TDS<br>(mg L<sup>-1</sup>)</th><th>DOC<br>(mg L<sup>-1</sup>)</th></tr></thead><tbody><tr><td>(Van Mierlo, 1926) [69]</td><td>12</td><td></td><td></td></tr><tr><td>(Spronck, 1941) [70]</td><td>40.7</td><td>82</td><td></td></tr><tr><td>(Devroey, 1941) [77]</td><td>38.2</td><td></td><td></td></tr><tr><td>(Nedeco, 1959) [78]</td><td>50</td><td></td><td></td></tr><tr><td>(Gibbs, 1967) [79]</td><td>23</td><td></td><td></td></tr><tr><td>(Symoens, 1968) [80]</td><td></td><td>36.6</td><td></td></tr><tr><td>(Holeman, 1968) [81]</td><td>58</td><td></td><td></td></tr><tr><td>(Vanderliden, 1975) [82]</td><td>41</td><td></td><td></td></tr><tr><td>(Eisma et al., 1978) [71]</td><td>31.8</td><td></td><td></td></tr><tr><td>(Meybeck, 1978) [83]</td><td></td><td>38.25</td><td></td></tr><tr><td>(Meybeck, 1979) [84]</td><td></td><td>39.3</td><td></td></tr><tr><td>(Molinier, 1979) [85]</td><td>26.5</td><td>51.4</td><td></td></tr><tr><td>(Deronde & Symoens, 1980) [86]</td><td></td><td>28.93</td><td></td></tr><tr><td>(Grondin & Gac, 1980) [87]</td><td></td><td>31</td><td></td></tr><tr><td>(Eisma, 1982) [88]</td><td>32</td><td></td><td>8.5</td></tr><tr><td>(Meybeck, 1984) [89]</td><td></td><td>34.99</td><td></td></tr><tr><td>(Kinga-Mouzeo, 1986) [90]</td><td>27</td><td>27.93</td><td>9.8</td></tr><tr><td>(NKounkou & Probst, 1987) [91]</td><td></td><td>36.05</td><td></td></tr><tr><td>(Olivry et al., 1988) [92]</td><td>25.4</td><td>59</td><td></td></tr><tr><td>(Olivry et al., 1989) [24]</td><td>25.4</td><td>59</td><td></td></tr><tr><td>(Probst et al., 1992) [93]</td><td></td><td>34.18</td><td></td></tr><tr><td>(Moukolo et al., 1992) [94]</td><td>25.9</td><td>52.3</td><td></td></tr><tr><td>(Laraque et al., 1995) [25]</td><td>19.27</td><td>36.35</td><td></td></tr><tr><td>(Olivry et al., 1995) [95]</td><td>26</td><td>36</td><td></td></tr><tr><td>(Sondag et al., 1995) [96]</td><td></td><td>36</td><td></td></tr><tr><td>(Coynel et al., 2005) [72]</td><td>26.3</td><td></td><td>10.6</td></tr><tr><td>(Seyler et al., 2006) [75]</td><td>27.1</td><td></td><td>9.8</td></tr><tr><td>(Laraque et al., 2009) [15]</td><td>24.98</td><td>36.89</td><td></td></tr><tr><td>(Laraque et al., 2013) [97]</td><td>25.4</td><td>29.4</td><td>11.4</td></tr><tr><td>(Spencer et al., 2016) [74]</td><td>21.7</td><td></td><td>9.2</td></tr><tr><td>(Borges et al., 2019) [98]</td><td></td><td></td><td>11.9</td></tr><tr><td>(Moukandi N'kaya et al., 2020) [56]</td><td>27.13</td><td>30.92</td><td>12.78</td></tr></tbody></table>
Laraque et al. (2013) [97] explained the variations of TSS, TDS, and DOC, as follows: (i) the low monthly variation in TSS concentrations, a ratio of 5 (Table 2), during the hydrological year (Figure 14a) reflects the different timing and successive contributions of the different tributaries from one sub-basin to another, on each side of the Equator, within the overall Congo Basin; (ii) TDS concentration (Figure 14a) decreases during the rising stage of the annual flood. This decrease is due to the well-known TDS dilution effect during the rainy season. Conversely, TDS concentrations increase during the falling stage, due to the concentration effect; (iii) DOC concentrations show a ratio of monthly variation of 5.6 (Table 2) over the hydrological year. The highest DOC concentrations are generally observed during the main flood period (December–January). This logically comes from the leaching of the marshes and flooded forests in the ‘Cuvette Centrale’.

<figure><img src="image_191.png" /><figcaption>**Figure 14.** (a) Mean monthly discharge (Q), Total Suspended-Sediment (TSS), Total Dissolved Solid (TDS) and Dissolved Organic Carbon (DOC) concentrations and their (b) fluxes of the Congo River at Brazzaville (for the period 2006–2017) [56].</figcaption></figure>

In conclusion, TSS, TDS, and DOC (without correction for atmospheric inputs) exhibit low concentrations and limited seasonal variation for the Congo at Brazzaville, which is not always synchronous with discharge variations (Figure 14a). However, due to their weak concentrations, TSS, TDS, and DOC monthly fluxes are mainly controlled by the magnitude of the discharge and show temporal variations more like those of the hydrograph (Figure 14b).

As discussed by Laraque et al. (2013) [97], within the Congo catchment, the TSS load is lower than the dissolved load, due to the ancient and peneplaned landscape, protected by a dense rainforest cover. Moreover, the higher contribution of dissolved matter is due to the dominating geochemical weathering (TDS) and the biochemical contribution (DOC concentrations) produced by the equatorial rain forest.

The “Cuvette Centrale” has a strong influence on the biogeochemical characteristics of the rivers crossing it, diluting the TSS and TDS, increasing organic matter concentrations, and lowering the pH due to the release of acids from the forest and swamps. Although this is a gradual process, differences were observed in Dissolved Organic Matter (DOM) production load. They are lower for semi-terra firma belt (Figure 15a) around the central flooded “Cuvette”, illustrated respectively by the examples of the Likouala Mossaka River (Figure 15a), with DOM = 32 mg L⁻¹ and pH = 5 to 6, and by the Likouala aux Herbes River (Figure 15b), with DOM = 80 mg L⁻¹ and pH = 3.5 to 5 [15]. DOM corresponds to the difference between the weight of the dry residue at 105 °C and of the total dissolved solids (TDS = Σ(cations, anions, silica)) determined in laboratory.
<figure><img src="image_192.png" /><figcaption>**Figure 15.** Distribution of dissolved matter (mg L⁻¹) in the emerged (a) and flooded (b) part of the 'Cuvette Centrale' [15].</figcaption></figure>

These observations were confirmed by measurements along the river course of the Ubangi Rivers. During the scientific cruise of November 1992, at 350 km from its confluence with the Congo River, Figure 16 shows at the same time a progressive increase of TDM and a decrease of TDS. The difference is due to the increase of DOM when the river leaves a mixed zone of shrubby and woody savannah, before entering the dense forest covering the 'Cuvette Centrale'. This last system corresponds to a real DOM reservoir that releases almost 25 t km⁻² year⁻¹ of organic matter after slow maceration due to the soaking of the forest litter [15].

<figure><img src="image_193.png" /><figcaption>**Figure 16.** Evolution of dissolved matters during the Ubangi cruise in November 1992 [15].</figcaption></figure>

For material fluxes, nearly 88 × 10⁶ tonnes of material are exported annually from the Congo Basin to the Atlantic Ocean, composed of 33.6 × 10⁶ tonnes of TSS, 38.1 × 10⁶ tonnes of TDS and 16.2 × 10⁶ tonnes of DOC for the study period (2006–2017). These values are in accordance with the first TSS fluxes given by Gibbs (1967) [79] and with those obtained during the period 1987–93 by Olivry et al. (1989, 1988, 1995) [24,92,95], Coynel et al. (2006) [72] and Laraque et al. (2009) [15], taking account of the discharge differences between these two periods.

In the case of the Congo, chemical weathering (10.6 t km⁻² year⁻¹ of TDS) slightly predominates physical weathering (9.3 t km⁻² year⁻¹ of TSS), followed by biological production (4.5 t km⁻² year⁻¹ of DOC).

Finally, thanks to these previous in situ studies and the use of remote sensing, Mushi et al. (2019) [99] was able to carry out a first assessment of soil erosion at the scale of the Congo River
basin. They conclude that it is necessary to develop a program of high-frequency water and sediment collection at several strategic points in the basin, in order to understand its different transport dynamics.

5.6. *Hydrological and Hydrogeochemical Anomalies Congo River Basin*

The work of Laraque et al. (1998) [10] carried out on the right bank tributaries of the Congo river in Republic of Congo shows two close regions with very different hydrological regimes: the Batéké plateau and the ‘Cuvette Centrale’ (Figure 17), although they present similar annual rainfall amounts (1700 versus 1900 mm year⁻¹). The first region lies upon Tertiary sandstones formations of about 200 to 400 m in thickness, covered by savannah. The second one corresponds to the floodplain of the lowest sector of the wide Congo depression, according to Burgis and Symoens (1987) [100], and lies upon Quaternary alluvial deposits, covered with swamps and dense equatorial rain forests (Figure 1b).

<figure><img src="image_194.png" /><figcaption>**Figure 17.** Congolese tributaries of the Congo River and smoothed contours of the Batéké plateau and of the “Cuvette Centrale” on the right bank of the Congo River [10]”. Legend: ‘Plateaux Tékés’ = Batéké plateau’, ‘Principales stations hydrométriques’ = Main gauging stations; ‘Stations pluviométriques représentatives’ = Representative rainfall stations.</figcaption></figure>

The hydrological regimes of the Batéké rivers are a representative example of a hydrologic paradox. Indeed, their flow regimes are almost independent of the regional rainfall regime because of the great infiltration capacity of the thick aquifer, which regulates their flows. This region holds some of the most regular rivers of the world, as illustrated by Figure 18a. In contrast, in the “Cuvette Centrale”, the lower permeability of the soils, the interception of rainwaters, the evapotranspiration of the forest cover, and the direct evaporation on the floodplain areas as well as the swamps, lead to an important water deficit. The hydrological regime here is closer to the rainfall seasonality (Figure 18b).
<figure><img src="image_195.png" /><figcaption>**Figure 18.** Mean monthly typical rainfall histogram and hydrological regimes of the (a) Batéké Plateau and the (b) “Cuvette Centrale” (adapted from Laraque et al., 2001) [65].</figcaption></figure>

From a qualitative point of view, these two regions are also very distinct. On one side, after crossing an important sandy aquifer whose alterable minerals have been totally washed out, the Batéké rivers are ‘clear waters’ with remarkably few minerals, but relatively hypersiliceous. These waters, with dissolved inorganic matter ranging from 1 to 3 mg L⁻¹ (not taking into account the dissolved silica), have similar TDS concentrations to rainfall waters (Figure 19a). These are among the most dilute surface waters of the world. On the other side, waters outflowing from the “Cuvette Centrale” stay for a long time under the forest cover and passes through the swamp. These are very rich in organic matter (up to 44% of particulate organic carbon) and very acid (pH can be lower than 4) and correspond to the typical ‘black water rivers’. Their mineralization, although relatively low for rivers (11 to 30 mg L⁻¹, without dissolved silica), can be considered high when compared to the Batéké plateau waters (Figure 19b). These particularities point to the dominant roles of the geological formations and of the vegetation cover on the runoff of the concerned rivers, as well as on their water quality.

<figure><img src="image_196.png" /><figcaption>**Figure 19.** Composition of dissolved mineral phases (issue from mean concentration in mmol·l⁻¹, of (a) Batéké Plateau rivers versus (b) “Cuvette Centrale” rivers (adapted from Laraque et al., 1998) [10].</figcaption></figure>

For the Batéké rivers, the interannual runoff coefficient (ratio between the runoff and the rainfall) ranges between 45–60%, versus 20–30% for the “Cuvette Centrale”. The average seasonal variability of discharges (mean ratio between the highest and lowest monthly discharge for each studied year) ranges between 1.1 and 1.5 for the Batéké rivers, versus 2.5 and 5.5 for the “Cuvette Centrale” rivers. The specific discharges of the first region varies from 25 to 35 L s⁻¹ km⁻², versus 10 to 15 L s⁻¹ km⁻² for the second region. These considerations allowed Laraque et al. (1998) [10] to propose an original hydrological classification of the Congolese right bank tributaries, based on these two hydrological parameters. Their variability can be explained by the physiographic characteristics of the drained basin (Figure 20). In this last figure, the Kouyou River presents an intermediate hydrological regime. Indeed, it originates in the Batéké Plateau with important annual runoff coefficients due to a strong and rapid infiltration in the aquifer, which will smooth the seasonal variability of its flows. It ends its course in the ‘Cuvette Centrale’ where, in the absence of a buffer aquifer and in the presence of a significant forest cover favoring evapotranspiration, the annual runoff coefficient becomes weaker and the seasonal variability of its flows, higher.
<figure><img src="image_197.png" /><figcaption>**Figure 20.** Hydrological classification of the Congolese tributaries of the Congo River [10].</figcaption></figure>

Another anomaly in the “Cuvette Centrale”, emphasized by Lee et al. (2011; 2014) [53,54] using remote sensing, is the difference between water levels in the river mainstem and the floodplain, which are lower by 0.5 to 3.0 m than the adjacent wetlands. Moreover, the mean seasonal variation of water levels on the mainstem of the Congo river, 3.5, 2.5, and 2.5 m, respectively at Kinshasa, Mbandaka, and Kisangani, repeated from decades of stage value studies by O'Loughlin et al. (2013) [101] and Betbeder et al. (2013) [102], are not matched with the seasonal variation of water levels in the adjacent wetlands of only 0.5 to 1.0 m, measured by Envisat radar altimetry [53,54]. This range of about 1 m of amplitude (Figure 21), presented in Alsdorf et al. (2016) [103], was also observed by in situ observations on the Télé Lake on the right bank wetland by Laraque et al. (1998) [104]. When taking into consideration, these studies conducted using remote sensing and ground observations on shallow lakes of the “Cuvette Centrale”, such as the Mai Dombe and Tumba lakes [100,105] on the left bank and the Télé Lake on the right bank of Congo River, it appears that the vertical water exchange (rainfall versus PET) is dominant in the center of the Congo Basin. Laraque et al. (1998) [104], already stated that mainly meteorological contributions (~1650 mm year⁻¹) were largely compensated for (>80%) by evaporation (~1300 mm year⁻¹) in the wetland area of Télé Lake, where convective thunderstorms vertically recycle moisture. The “Cuvette Centrale” is filled mostly by rain (which accumulates in the depression) rather than by the overflowing of the river. In the low flow period, this place seems to drain slowly by gravity, through seepage, drainage, and desaturation of its spongy peat soils towards the mainstem river situated at a lower elevation.

<figure><img src="image_198.png" /><figcaption>**Figure 21.** Water surface elevations for the Congo River and immediately adjacent wetlands of the Cuvette Centrale at 17.54° E, 0.74° S. Elevations are from the EnviSat radar altimeter, pass number 973. Elevations are with respect to the EGM08 geoid model [103].</figcaption></figure>
Obviously, its water exchange with the river mainstem is different from those of other great rivers
with vast lateral floodplains like the Amazon River, where most exchanges are assured by several
interconnected channels [106–110], named locally as “furos”.

6. Hydrological Modeling in the Congo River Basin

In this basin, one of the most complete rainfall-runoff modeling approaches which is well calibrated with all the in situ parameters available, is the work of BRLi (2016) [7], which uses the GR4J 'rainfall-ETP-Q daily' and Mike Hydro Basin models.

However, it is necessary to recall that hydrological modeling is used to address many water resource management issues, as models can be used with limited data, but generate sufficiently reliable information for management purposes. Tshimanga (under review) [111] provides a comprehensive review of hydrological model applications in the Congo Basin during the two last decades. This review reveals that there has been a great deal of effort from the past modeling studies to address both data issues and process understanding, and to establish models that represent the basin's hydrological processes. The author argues that many hydrological models' development during the past two decades in the basin have been initiated mainly in an experimental phase, testing the models' performance in a new and data scarce environment, and not for solving the real hydrological and water resources management issues of day-to-day life. For this reason, it has been difficult to use the models in support of policy decisions for river basin management and development. There are still a number of issues and uncertainties that should attract the attention of future modeling studies—right modeling for the right reason. The author's findings collectively reveal that researchers need to change their mindsets and to look for adequate or novel approaches to model hydrology and generate knowledge at appropriate scales of policy decision and management in the Congo Basin.

As mentioned above, the review of hydrological modeling studies in the Congo Basin shows that both data issues and processes understanding have been at the heart of model’s development in the basin. With regard to data, and given a critical lack of in-situ data, there have been efforts made towards the use of global datasets, including global climate (both reanalysis and satellite-based) and physical basin properties datasets as well as satellite altimetry for prediction of water level and discharge. With regard to process understanding, a number of functions have been developed to improve existing model structures, most specifically to address the issues of modeling natural storages of wetlands and lakes that dominate the hydrology of the Congo Basin as well as the routing functions of the large river channels [112–115]. However, there have also been a number of problems that challenged the various modeling exercises. These problems relate to a lack of appropriate data as well as the lack of a thorough understanding of climate-hydrology processes, the lack of integration of this understanding in model structures and therefore, the lack of integrated and critical model assessment [111]. Currently, there is a number of studies that focus on model development to address some of these problems. Some of these studies include the use of the LISFLOOD-FP model to inform the wetland functions of the GW-Pitman model [116]; the use of SWAT (Soil and Water Assessment Tool) to highlight the attenuation functions of the ‘Cuvette Centrale’ and its hydrological budget [117]; the application of the MGB model to develop rating curves and derive river discharge in real time based on satellite altimetry [118]; and the development of a hydraulic and bathymetric HEC-RAS 2D channel model, which allowed Carr et al. (under review) [119] to create high-definition bathymetric maps of a complex and anastomosing section along a 120-km reach of the Congo River, downstream of the Ubangi confluence (Figure 22). This HEC-RAS model is a key step in the simulation of the river hydrodynamics necessary to facilitate and secure navigation on the main river “highway” in the basin [120].
<figure><img src="image_199.png" /><figcaption>**Figure 22.** Water surface output plot of flow velocity downstream of the Ubangi confluence, from the Hec-Ras 2D channel model, described by Carr et al. (under review) [119].</figcaption></figure>

Based on old navigational charts, made sometime between 1917 and 1931 and recent remote sensing images, these last authors were able to quantify river planform change on the same place over the last century.

On a longer timescale, a study by Molliex et al. (2019) [121] focused on a reconstitution of the water and sediment flows during the 155 ka BP, thanks to the cross-analysis of the evolutions of various proxies (geochemical and isotopic on the sedimentary cores of the submarine canyon of the Congo oceanic outlet) and by using the HydroTrend model calibrated in particular for the hydrological data of the last 116 years. Figure 23 presents the results focused on the last 23 ka. This study gives an idea of the long-term variations in the Congo's flows, according to the glacial and interglacial cycles that have affected our planet.

<figure><img src="image_200.png" /><figcaption>**Figure 23.** Water and suspended sediment simulation results focused over the last 23 ka. Gray curves represent annual data, while black curves are running means over 100 years. Three climatic periods (interglacial, post-glacial, and glacial) are individualized by the light gray/white bands in background. (A) Water discharge. (B) Mean suspended sediment load taking into account vegetation changes [121].</figcaption></figure>

# 7. Human Use, Integrated Management, and the Future

## 7.1. Human Use Exploitation

Some works such as Shem and Dickinson (2006) [1] relate to the alteration of the water cycle under the effects of deforestation in the Congo Basin, but until now, the most part of the Congo River basin hydrology is still natural and relatively little affected by a population, whose standard of life
is generally based on subsistence activities. Upstream of the Brazzaville-Kinshasa capitals, there are
only a few hydropower dams on relatively small rivers like Lobaye, Léfini, Nzoro. There is no bridge
over the course of the main river upstream or even between the two capitals, which are the nearest in
the world, separated by only 3 km of river width. There are very few large cities upstream and no
major industrial activities. However, there are some impacts from mining activities, especially for
gold and diamonds near Kisangani city and copper near the town of Lubumbashi in the Precambrian
massifs, which border the wide central sedimentary basin. There is also some wood exploitation,
but deforestation is limited for example in comparison with other intertropical catchments like the
Amazon basin. A large part of the Congo Basin is difficult to reach because of the lack of transport
infrastructure, and large insecure areas, especially in the North-East. With the absence of a substantial
road network, the fluvial navigation, even though precarious, is often the only way for the population
to circulate and exchange goods, especially between states. Until the end of the 20th century, the wood
was transported by floating rafts descending the river to Brazzaville to then be transported to the
Congo delta by one of the few railways line, the "Congo-Ocean", from Brazzaville.

Under these conditions, the human impact is still very weak and probably much lower than that
of climate changes.

We have already noted the longer duration of the recession stage, which reduces the fluvial
navigation, especially on the Ubangi tributary (Figure 11b), where the runoff deficit is responsible for
the degradation of the navigation conditions since 1975 [122]. This causes increases in sandbanks,
which need huge dredging operations. The length of the annual interruption period of the navigation
of large boats on the Ubangi river has increased from 9 days (period 1935–1977) to 84 days (period
1978–1991), and then to 108 days (period 1983–1991) according to the ATC, ACCF, SCEVN (2000) [123]
report, and has been more than 200 days since 2002. The same report indicates a critical situation for
the Sangha River, a right bank tributary on which the navigation was interrupted for 30 days per year
for the 2002–2004 period, when it was only 2 days a year during the years 1954–1977.

More recently, the Congo Basin Atlas of CICOS (2016) [124] pointed out that the severe low water
levels that have affected the Ubangi and Sangha rivers for several years have caused navigation to stop
for 4 to 6 months a year.

Fluctuations in discharges and TSS fluxes need to be monitored for the PANAV project of regional
navigation and for the next steps of the construction of the "Grand" Inga Dam on the Congo River
in RDC, downstream of Brazzaville and Kinshasa. This project could produce twice the electricity
generating capacity as the Three Gorges Dam [125].

## 7.2. *Need for a Coherent Policy for Future Protection and Management*

Despite its size and its importance and in the context of current climate change, the Congo River
is poorly known and monitored, since the almost abandonment of the hydrological network after 1960
for left bank tributaries and 1993 for right bank tributaries.

As a consequence, the countries of the Congo Basin need coordination and organization to
restore/create functional hydrological services, which have to reactivate operational hydrological
stations and collaborate in the management of shared databases. The hydrological and
hydrosedimentological information will serve in particular to improve the fluvial navigation, which is
crucial for the economy of the neighboring states.

For these reasons, CICOS was created in 1999, and has objectives to promote inland interstate
waterway transport and also to coordinate programs of regional importance like the Congo-HYCOS for
the Hydrological Cycle Observation System on the Congo Basin, under the WMO (World Meteorological
Organization) framework, AMESD for the African Monitoring of the Environment for Sustainable
Development, etc., to estimate and better manage the water resources of this vast catchment and
to strengthen capacity building of the NHS, especially with the use of the remote sensing. CICOS
has the mandate of the six riparian countries, occupying more than 80% of the basin, to promote the
integrated management of the water resources of the Congo Basin. This institution is also responsible
for coordination of the hydrological monitoring of the basin and could take advantage of the results of this publication in order to further improve its scheme for the development and management of water resources and update its management tools, especially its Hydrological Information System and Water Resources Allocation tools, with the help of other national institutions of each country.

## 8. Conclusions

As the longest river in Africa after the Nile, and the second-largest global river in discharge after the Amazon, the Congo represents half of the African water input to the Atlantic Ocean. The understanding of its hydro-meteorological functioning, as well as that of its solid and dissolved matter flows is essential for water, material, and energy transfer modeling on a global scale and also for the sustainable implementation of the world's largest hydroelectric dam, the 'Grand Inga', which has been in the project planning stage for the last few decades.

Nguimalet and Orange (2019) [3] show that it is on the Ubangi river that the drought is the strongest, especially in this last decade, where the maximum flow rates are the most affected.

It raises an increasing problem for inland navigation, as the annual duration of which is reduced due to the longer annual period of low flows. This is crucial because it is the key method of exchanging goods and persons between the nearby countries in the absence of any good road network.

These considerations also call into question the usefulness of the TRANSAQUA project for the construction of a channel between the northeastern Ubangi tributary of the Congo and the Logone-Chari Rivers to offset water deficits in Lake Chad and highlight the possible impacts of such a huge project [126]. The polemic on this pharaonic project is amplified by the return of the backfilling of Lake Chad, which has historically experienced several natural water level fluctuations [67]. It is a wave-like phenomenon of irregular frequency. In addition, the use and management of the resources of this ecosystem is complex because the issues are political, environmental, economic, and security-related [127]. Therefore, it is necessary to take into account the socio-environmental disruption, which the TRANSAQUA project could lead to, as this is one of world's last fairly 'natural' large inter-tropical hydro-eco-systems present in central Africa.

Within the Congo Basin, the wet decade of the 1960s was the most striking anomaly in the river's hydraulic behavior over the last 118 years, which separates two symmetrical periods (1902–1959 versus 1970–2017), with equivalent mean flows around 39,700 m³ s⁻¹ for the Congo River at BZV/KIN. As the results of the 2006–2017 period of the recent SO-HYBAM monthly survey correspond to this same mean interannual discharge, it can also be considered representative of these two periods, which cover 104 years. The recent budget over the last 12 years of the SO-HYBAM survey shows specific discharge of 10.8 L s⁻¹ km⁻², with specific fluxes of 9.3 × 10⁶ t km⁻² year⁻¹ (i.e., 33.6 × 10⁶ t year⁻¹) for TSS; 10.6 t km⁻² year⁻¹ (i.e., 38.1 × 10⁶ t year⁻¹) for TDS; 4.5 t km⁻² year⁻¹ (i.e., 16.2 × 10⁶ t year⁻¹) for DOC. These fluxes need to be constantly documented on a regular basis and at a more regional scale by taking into account technological improvements like remote sensing in data collection and possible changes due to climate and human impacts.

These results are consistent with those of past periods, illustrating the significant inertia of this basin by: (i) being located on both sides of the equator line, (ii) the virtue of its very large area which may mitigate further possible changes, and (iii) highlighting the buffering role of the vast forest cover and of the huge 'sponge' formed by the 'Cuvette Centrale', which helps to sustain flows. Even if the drought has been particularly severe in the northern part of its basin, this paper underlines that its hydrological functioning was not very impacted due to its large untouched 'cuvette', which is the opposite of the Amazon rainforest where the impact of deforestation on the basin and on the flows of the Amazon River had begun to be suspected by Callède et al. (2008) [128], who indicated a 5% increase in the mean annual discharge over the 22 years from 1981 to 2003 and a number of strong floods five time higher during this period than before. Since then, the situation has continued to worsen.

Of course, short-term observations (one century) on large basins such as the Congo, should not obscure the relativity of the hydrological standards that have been established on the concepts of flow
stationarity. This concept shows that our temporal myopia is moreover questioned [129,130] and must therefore be revised, as shown by the study of highly reactive sub-basins such as the Ubangi, which has evidenced accentuated hydropluviometric regime changes since 1970.

To face its future challenges like IWRM, the Congo Basin urgently needs the emergence of a true regional hydrological awareness to ensure the sustainable and balanced development of one of the last and rare regions of the world, which is still relatively pristine.

**Author Contributions:** Conceptualization, A.L., and G.D.M.N.; methodology, A.L., G.D.M.N., and D.O.; software, A.L., G.D.M.N., and J.M.T.; validation, A.L., D.O., R.T., and J.M.T.; formal analysis, A.L., G.D.M.N., R.T., and J.M.T.; investigation, A.L., G.D.M.N., D.O., R.T., J.M.T., G.M., C.R.N., M.A.T., and S.Y.; resources, A.L., G.D.M.N., D.O., R.T., J.M.T., G.M., C.R.N., M.A.T., S.Y. and G.G.; data curation, A.L. and G.D.M.N.; writing—original draft preparation, A.L.; writing—review and editing, all authors.; visualization, all authors; supervision, A.L. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Acknowledgments:** "On the eve of my retirement, and to use an African expression, I dedicate this summary to my ORSTOM "Dads", Jean Claude Olivry and Bernard Pouyaud, who entrusted me with the responsibility of the last hydrology laboratory at the ORSTOM Centre in Brazzaville. On the Congolese side, my "Dad" was Paul Bassema, an intrepid hydrologist technician, capable of solving everything in the bush. I also dedicate this paper to Bienvenu Maziezoula, endowed with the wisdom of a great Chief with whom I shared this direction. I cannot forget the generations of ORSTOM hydrologists who have succeeded one another in Congo and CAR since the 1940s to set up and manage their hydrological networks under epic and very difficult conditions. This synthesis has been made possible thanks to data from international projects such as PIRAT, PEGI/GBF & SO-HYBAM, (https://hybamobs-mip.fr/), SIEREM (www.hydrosciences.fr/sierem), and the ECHOBACO/AUF project, thanks to the international and inter-institutional collaboration and cooperation between various institutions such as BRLi, CICOS, the Directorate of Meteorology and Hydrology of CAR, GET, GIE-SCEVN, HSM, IRD (e.g., ORSTOM), RVF, UMNG, and UNIKIN. Special thanks go to L. Ayissou, A. Carr, S. Délichère, P. Datok, G. Mokando, A. Pandi, N. Rouché, A. Yambélé, and S. Yepez for their various collaborations. Finally, I would also like to warmly thank Doug Alsdorf, specialist in remote sensing at the Ohio State University, for his initiative to relaunch research in the Congo Basin. With the AGU Chapman Conference in Washington in 2018 and this special issue on the Congo Basin, which he is leading, he has been keen to involve the nationals of this basin, so that they can be actors in their own destiny".

**Conflicts of Interest:** The authors declare no conflict of interest.

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