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© 2020 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_124.png" /></figure>
Article

# Anthropization and Climate Change: Impact on the Discharges of Forest Watersheds in Central Africa

Valentin Brice Ebodé¹,*, Gil Mahé²,*, Jean Guy Dzana¹ and Joseph Armathé Amougou¹

¹ Department of Geography, University of Yaounde 1, P.O. Box 755, Yaounde, Cameroon; dzana1@yahoo.fr (J.G.D.); joearmathe@yahoo.fr (J.A.A.)

² International Joint Laboratory DYCOFAC, IRGM-UY1-IRD, BP 1857, Yaoundé (Cameroon) and UMR HSM, IRD-CNRS-University of Montpellier, 34090 Montpellier, France

* Correspondence: ebodebriso@gmail.com (V.B.E.); gil.mahe@ird.fr (G.M.); Tel.: +237-694-426-200 (V.B.E.); Fax: +33-467-144-774 (G.M.)

Received: 6 August 2020; Accepted: 15 September 2020; Published: 29 September 2020

**Abstract:** Climate change and anthropization are major drivers of river flows variability. However, understanding their simultaneous impact on discharges is limited. As a contribution to address this limitation, the objective of this study is to assess the impact of climate change and anthropization on the discharges of two watersheds of Central Africa (Nyong and Ntem) over a recent period. For this, the hydropluviometric data of the watersheds concerned were analyzed using the Pettitt test. Similarly, the dynamics of the main land use modes (LUM) have been assessed, through classifications obtained from the processing of Landsat satellite images of the watersheds studied on two dates. The results of this study show that in Central Africa, annual discharges have decreased significantly since the 1970s, and yet the decline in annual rainfall does not become significant until the 2000s. The discharges of the rainy seasons (spring and autumn) recorded the most important changes, following variations in the rainfall patterns of the dry seasons (winter and summer) that precede them. Winters experienced a significant decrease in precipitation between the 1970s and 1990s, which caused a drop in spring flows. Their rise, which began in the 2000s, is also accompanied by an increase in spring flows, which nevertheless seems rather slight in the case of the Nyong. Conversely, between the 1970s and 1990s, there was a joint increase in summer rainfall and autumn flows. A decrease of summer rainfall was noted since the 2000s, and is also noticeable in autumn flows. Maximum flows have remained constant on the Nyong despite the slight drop in rainfall. This seems to be the consequence of changes in land use patterns (diminution of forest and increasing of impervious areas). The decrease in maximum flows noted on the Ntem could be linked to the slight drop in precipitation during the rainy seasons that generates it. Factors such as the general decrease in precipitation during the winter and the reduction in the area occupied by water bodies could justify the decrease in minimum flows observed in the two watersheds. These findings would be vital to enhance water management capabilities in the watersheds concerned and in the region. They can also give some new elements to study and understand the seasonal variation and fresh water availability in downstream, estuaries and coastal areas of the regional rivers.

**Keywords:** climate change; urbanization; impervious area; Cameroon; runoff

## 1. Introduction

River flows are essentially variable over time. Their evolution is generally considered to be the result of their interactions with climate change and/or anthropization [1–6], although it is recognized that sensitivity of watercourses to these factors depends on the natural predispositions (size, slope system, type of soil, etc.) of their watersheds [7]. Apart from some interesting attempts, such as that of Dzana et al. [1], research studies in which the effects of these two factors are dissociated and evaluated
separately are rare. Most of the studies undertaken thus far around the world have generally focused on one or another of these interferences, as given in the results in West Africa, South Africa, Ethiopia and even Uruguay, linking land cover change/or climate change with river discharges [8–11].

In sub-Saharan Africa, the work devoted to the research of the driving factors of the flows variability has been carried out mainly since the 1980s. Those in which the authors correlate the precipitations and the flows are based on the detection of discontinuities in the hydropluviometric series. Their results confirmed, in the case of West Africa, that the 1970s appeared as the main period of discontinuity, marking the beginning of the hydroclimatic drought in this region [12–15]. In Central Africa, the most obvious fluctuations in discharges and precipitation have been observed with seasonal time steps [16–18]. Liénou et al. [19] demonstrated in the case of three equatorial rivers (Ntem, Nyong and Kienke), that the most important climatic variations leading to changes in flows result from variations in precipitation in dry seasons. These authors explain the sensitivity of watersheds to this precipitation variability by the fact that their reduction induces a significant deficit in soil and groundwater storage, resulting in a decrease in the runoff. Conversely, their increase keeps the soil wetter during the dry season, and therefore, improves runoff from the start of the rainy season. It seems, therefore, that the variability of the regimes of equatorial rivers can be better appreciated when these precipitations are taken into account. Regarding the impact of anthropic action (changes in land use modes (LUM)) on runoff, reference works are generally based on supervised classifications of satellite images on at least two dates, with a view to assessing the dynamics of land use and its possible impact on flows [20–26]. Their results generally confirm an increase in flows resulting from an increase in Impervious Area (IA) such as buildings, roads and cultivated areas. Paturel et al. [24] specifies, in the context of a Sahelian basin in West Africa (Nakambe in Burkina-Faso), that this increase in flows is linked to the reduction in the water capacity of the soils resulting from the increase of the impervious surface. However, Dezetter et al. [27] had noted, in a humid tropical climate under forest cover (in the case of the upper Niger River basin in Guinea), an insignificant impact of the increase in agricultural areas, to the detriment of the forest, on flows. According to them, evaporation (due to the increase in temperature) from this region would have a greater impact than that of changes in LUM on the hydrological balance of this region. These conclusions follow those of a former study by Fritsch [28]. This author noted earlier in the context of small Guyanese basins, also under equatorial forest cover, a considerable impact of deforestation on runoff; however this did not last long, as after two years the runoff coefficients returned to nearly past values on surfaces where vegetation was left wild or over plantations, while the runoff remained higher over grass lands (for cattle grazing). This led Fritsch to specify that the magnitude of the hydrological changes observed in the different cases depends on the type and importance of the developments carried out in these basins, together with the impact of the inter-annual variability of natural phenomena.

Few studies have been carried out on this type of basins in forested equatorial area, and there is still a large uncertainty on what are the main drivers of the variability of the hydrological balance in equatorial basins within the context of increasing temperature due to climate change.

The Nyong and Ntem watersheds offer us the opportunity to make possible progress on this issue, as these are two basins that have been observed for decades in this equatorial area, and for which there have already been several former studies on the hydroclimatological variability, which showed some questionable issues about the origin of the observed change in the river flow regime since some decades. These watersheds are forested, but since the early 1970s, climatic fluctuations have been observed there, as in the rest of sub-Saharan Africa [19]. Moreover, their population is increasingly growing [29]. This population growth generally has as a corollary the increase in IA, which can have repercussions on the hydrological functioning of these hydrosystems. However, unlike the West African watersheds, the Nyong and Ntem basins were the subject of a very small number of works after the 2000s, due to the absence of observation data in certain cases, and significant gaps in existing observation series in other cases. Likewise, the difficulty of acquiring satellite images of sufficient quality (without cloud) for processing over large areas would have led the authors to ignore the study
of the impact of anthropization (change of LUM) on discharges. Most of the studies cited above relate to West Africa, apart from those of Moffo [25] and Ebodé [26]. Despite these difficulties, it is nevertheless advisable to try to update the information available on these watersheds in the limited number of similar investigations that have focused on them before, but also to take note of certain aspects (land use) having important links with flows, which have been less studied in the region thus far, in connection with the dynamics of IA. Such a study seems fundamental for these rivers, which are among the main sources of water and fishery resources in the country, whose recent decreases are partly attributed to hydroclimatic changes. It is particularly useful in the case of Ntem, where the Memve'ele dam was recently built (in Nyabessan, downstream from Ngoazik station), with the aim of ensuring electricity production, replacing the old dam of Songloulou. It is hoped that the results of this study will reinforce the debate on the issue since the rationing in the electrical distribution of the southern Cameroon network is also generally attributed to climate change. In addition, the data and new inputs from this study would contribute to long-term planning of water demand and use, and improve future simulations of the flows of these rivers.

The main objective of this article is to assess the observed changes in streamflow of Nyong and Ntem watersheds, in response to current climate change and anthropization (land use/land cover change). For this, we propose to study long-term rainfall and discharges time series, and changes in land cover over time from satellite images. The links between rainfall and some sources of large-scale climate variability will also be checked.

## 2. Materials and Methods

### 2.1. Study Area

This study focuses on the Nyong (14,438 km²) and Ntem (19,563 km²) watersheds, respectively at Mbalmayo and Ngoazik outlets. These watersheds are located in Central Africa between latitudes 1°15′ N and 4°44′ N and longitudes 11°5′ E and 13°30′ E (Figure 1). The first is entirely included in Cameroonian territory, while the second is located between Cameroon, Gabon and Equatorial Guinea. They belong to the equatorial (Ntem) and sub-equatorial (Nyong) domains, with abundant precipitation. The annual precipitation oscillates around 1600 mm over the Nyong and 1800 mm over the Ntem, distributed over four seasons of unequal importance, including two dry and two rainy seasons (Table 1). The rainy seasons (spring and autumn) are generally very wet with record totals of around 900 mm and marked by numerous thunderstorms, which sometimes cause significant floods. The winter dry season is the only real dry season in the region, with average precipitation fluctuating around 90 mm and 180 mm, respectively, in the Nyong and Ntem watersheds (Table 1). The summer dry season tends to become more humid in the region since the 1980s [19]. The Nyong and Ntem watersheds are deeply gullied and cut into hills with convex slopes and wide marshy valleys [30]. On both sides of the main rivers, a topographic landscape in steps is observed, which seems to be the result of brittle tectonics. In the middle part of each watershed, there is a major valley (600 m above sea level on average), which represents the collapse ditch. It is a wide corridor, in the middle of which flows the main river. On the two watersheds, this unit is directly followed on each side of the main river by a large plateau with an average altitude of around 700 m, then large hills (800 m of altitude on average), which are bristling with a few inselbergs located mainly at the limit with neighboring watersheds, and whose altitude can sometimes reach 1200 m [30]. The geological substratum of these watersheds is made up of a granito-gneissic base on which ferralitic (on the tops and slopes) and hydromorphic (in the shallows) soils develop. The vegetation encountered on these basins is a dense semi-deciduous forest with Sterculiaceae and Ulmaceae strongly subjected to anthropic actions [31].
<figure><img src="image_125.png" /><figcaption>Figure 1. Location map of Nyong and Ntem watersheds.</figcaption></figure>

<table><caption>**Table 1.** Statistics relating to the evolution of precipitation and average discharges at annual and seasonal time steps over the study period, but also before and after the discontinuity. Cv: coefficient of variation.</caption><thead><tr><th rowspan="2">Periods</th><th colspan="2">Whole Period</th><th rowspan="2">Discontinuity</th><th colspan="2">Mean (m³/s)</th><th colspan="2">Cv (%)</th></tr><tr><th>Mean (m³/s)</th><th>Cv (%)</th><th>Before</th><th>After</th><th>Before</th><th>After</th></tr></thead><tbody><tr><td colspan="8" style="text-align: center;">Rainfall</td></tr><tr><td colspan="8" style="text-align: center;">Nyong</td></tr><tr><td>Annual</td><td>1627</td><td>9</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Spring</td><td>634</td><td>13</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Summer</td><td>274</td><td>28</td><td>1964-65</td><td>220</td><td>290</td><td>26</td><td>26</td></tr><tr><td>Autumn</td><td>625</td><td>11</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Winter</td><td>93</td><td>43</td><td>1975-76</td><td>116</td><td>78</td><td>29</td><td>48</td></tr><tr><td colspan="8" style="text-align: center;">Ntem</td></tr></tbody></table>
<table><caption>Table 1. *Cont.*</caption><thead><tr><th rowspan="2">Periods</th><th colspan="2">Whole Period</th><th rowspan="2">Discontinuity</th><th colspan="2">Mean (m³/s)</th><th colspan="2">Cv (%)</th></tr><tr><th>Mean (m³/s)</th><th>Cv (%)</th><th>Before</th><th>After</th><th>Before</th><th>After</th></tr></thead><tbody><tr><td>Annual</td><td>1810</td><td>7</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Spring</td><td>675</td><td>12</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Summer</td><td>256</td><td>29</td><td>1964–65</td><td>192</td><td>283</td><td>29</td><td>22</td></tr><tr><td>Autumn</td><td>693</td><td>10</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Winter</td><td>184</td><td>28</td><td>1976–77</td><td>208</td><td>169</td><td>20</td><td>32</td></tr><tr><td colspan="8" style="text-align: center; font-weight: bold;">Discharges</td></tr><tr><td colspan="8" style="text-align: center; font-weight: bold;">Nyong</td></tr><tr><td>Annual</td><td>139</td><td>20</td><td>1973–74</td><td>151</td><td>132</td><td>19</td><td>18</td></tr><tr><td>Spring</td><td>88</td><td>31</td><td>1973–74</td><td>105</td><td>76</td><td>28</td><td>27</td></tr><tr><td>Summer</td><td>111</td><td>31</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Autumn</td><td>243</td><td>23</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Winter</td><td>123</td><td>20</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td colspan="8" style="text-align: center; font-weight: bold;">Ntem</td></tr><tr><td>Annual</td><td>236</td><td>23</td><td>1970–71</td><td>269</td><td>215</td><td>26</td><td>18</td></tr><tr><td>Spring</td><td>222</td><td>34</td><td>1970–71</td><td>270</td><td>193</td><td>32</td><td>28</td></tr><tr><td>Summer</td><td>115</td><td>49</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Autumn</td><td>421</td><td>25</td><td>1990–91</td><td>444</td><td>324</td><td>22</td><td>18</td></tr><tr><td>Winter</td><td>156</td><td>29</td><td>1970–71</td><td>186</td><td>136</td><td>25</td><td>23</td></tr></tbody></table>

## 2.2. Data Collection

The data collected for this study are hydroclimatic series and spatial data (satellite images).

### 2.2.1. Hydroclimatic Data

Nyong discharge data series come from two sources. The gauging station, as well as the methods used for reading water levels and calculating flow rates, however, remain the same from one source to another. The series obtained from the Hydrological Research Center (CRH) cover the period 1951–1987. This center manages a hydrometric database, generally with a daily time step, which contains almost all the measurements carried out on Cameroonian territory since the early 1950s, for most stations. These measurements were carried out first by ORSTOM (Office de la Recherche Scientifique et Technique Outre-Mer/Office for Scientific Research in Overseas Territories, Paris, France) researchers, currently IRD (Institut de Recherche pour le Développement/Research Institute for Development, Marseille, France) and their predecessors from EDF (Electricité De France/France Electricity, Paris, France) and, since 1980, by CRH. No observation data is available on this river between 1988 and 1997. Indeed, after 1987, due to budgetary constraints, the hydrological service no longer guaranteed the continuity of observations. We note then the abandonment of a large number of stations observed, including those of Nyong and Ntem. Over the period 1998–2016, the series of discharges used are those of the ORE-BVET (Observatory for Environmental Research/Experimental Tropical Watersheds). Funded by the Ministry of Research and New Technologies, the National Institute of Universe Sciences, the Research Institute for Development and the Midi-Pyrénées Observatory, this observatory aims to understand the relative influence of climate variability and agriculture on water cycles and biogeochemical cycles in tropical areas. Based on long-term monitoring (>10 years) of meteorological, hydrological and geochemical variables in watersheds, it is implemented both in Cameroon and in India. In Cameroon, the observatory is interested in the Nyong watershed. The complete series of Ntem discharges comes from the CRH, and covers the period 1953–2015, but with gaps during the 1990s and 2000s. There are no observation data on this river between the intervals 1993–2001 and 2006–2009. All of the hydrological data used in this work were collected daily. The monthly, seasonal and annual modules were calculated thereafter.
The rainfall series comes from two sources. Over the period 1950–1999, the SIEREM (Système d’Informations Environnementales pour les Ressources en Eau et leur Modélisation/System of Environmental Informations on Water Resources and their Modeling) rainfall grids were used. SIEREM [32] is a database based on a generic scheme allowing the management of hydroclimatic but also environmental information on Africa. This database was developed by the HydroSciences Montpellier/HSM Laboratory, from a rainfall database (around 7000 gauging stations) from Africa, inherited among others from ORSTOM, and its experience in the management of hydroclimatic databases in the intertropical region [33,34]. Among its recovery products, SIEREM offers monthly rainfall grids over Africa for the period 1940–1999, at the 0.5° square degree, approximately 2750 km² of the area considered [35]. The space and time steps chosen seem to be the most appropriate resolutions to be able to appreciate, at the same time, the impacts of climate change and anthropic activities on water resources [36,37]. These grids are freely accessible in ASCII and NetCDF formats on the site http://www.hydrosciences.org/spip.php?article1387. Before use, they had to be validated first by comparison with rain gauges measurements from stations.

From 2000s, the choice was made for TRMM (Tropical Rainfall Measuring Mission) 3B42 V7 rainfall estimates. The TRMM platform was launched in 1997, built jointly by the United States and Japan. It offers several pre-calibrated data sets, including the daily product TRMM 3B42 V7 at a spatial resolution of 0.25° × 0.25°. These data combine syntheses of TRMM images and other satellite data [38]. Thus, microwave sensors from AQUA, NOAA (National Oceanic and Atmospheric Administration), DMSP (Defense Meteorological Satellite Program) and visible/infrared data from geostationary satellites are taken into account in precipitation estimation algorithms. These estimates are then adjusted by incorporating the monthly climate measurements on the ground of the GPCP (Global Precipitation Climatology Project) and CAMS (Climate Assessment and Monitoring System) networks to give the monthly products TRMM 3B43. The adjustment coefficients calculated for this product are finally applied to the daily data to give the final product TRMM 3B42, version 7 [38], downloadable free of charge from the website https://giovanni.gsfc.nasa.gov, in ASCII format. The monthly TRMM rainfall data were first validated through comparisons with the SIEREM rainfall over the years of common availability (1998 and 1999). The correction factors (essentially between 1.17 and 1.4) were subsequently established for the months for which the differences between the two sources were significant (July and August).

Four climatic indices (SOI: Southern Oscillation Index; DMI: Dipole Mode Index; NATL: North Tropical Atlantic sea surface temperature indices; SATL: South Tropical Atlantic sea surface temperature indices) representative of certain sources of large-scale variability (Pacific Ocean, Indian Ocean and Atlantic Ocean), likely to influence the climate of the region [39], were used in this study (Figure 2). These indices were collected at a monthly time step on the NOAA website (https://psl.noaa.gov/data/climateindices/list/). SOI and DMI were collected at the same intervals as the rainfall. The other two indices have only been available since the beginning of the 1980s.

<figure><img src="image_126.png" /><figcaption>**Figure 2.** Patterns in oceanic sea surface temperatures (SSTs) known to influence weather in the studied region (blue zones), according to Bush et al. [39]. The Pacific Ocean El Niño Southern Oscillation (ENSO); North and South Tropical Atlantic SSTs (NATL and SATL, respectively) and the Indian Ocean Dipole (IOD).</figcaption></figure>
### 2.2.2. Spatial Data

The spatial data used for the study of land use in the Nyong watershed are essentially the Landsat 8 satellite images from January 2015 and Landsat MSS from March 1973. Three Landsat scenes cover the entire watershed, corresponding to the path/row 185/57, 185/58, 184/57. The same study was carried out in the Ntem watershed, using four Landsat 8 scenes from January 2015 and Landsat MSS from February 1973, corresponding to path/row 184/58, 185/58, 184/59 and 185/59. All of these images are made available to the general public free of charge by the National Aeronautics and Space Administration (NASA), via the US Geological Survey website (https://earthexplorer.usgs.gov/), in GeoTIFF format. The downloaded images taken during the winter (December to mid-March) were preferred to that of the rainy seasons because they are less affected by cloud disturbances.

### 2.3. Data Analysis

#### 2.3.1. Hydroclimatic Data

The analysis of rainfall, average discharges and runoff coefficients was carried out using statistical discontinuity detection tests [40] at the significance level 95%. The Pettitt test seems to be the most suitable for the analysis of incomplete series such as ours because it separates the series only into two periods with an overall distinct behavior, which avoids the detection of false discontinuities as can sometimes be observed with other tests such as Hubert segmentation. Its choice at the expense of trend tests (Mann-Kendall test for example) is justified by the fact that it indicates a date from which the change becomes statistically significant (discontinuity), which allows a better appreciation of the variability. Its principle consists in dividing the studied series (of N size) into two sub-samples of sizes m and n, respectively. We then calculate the sum of the ranks of the elements of each sub-sample in the total sample. A statistical study is then carried out based on the two sums thus determined, then it is tested according to the hypothesis that the two subsamples do not belong to the same population. The Pettitt test is non-parametric and derives from that of Mann Whitney. The absence of a discontinuity in the series (Xi) of size N constitutes the null hypothesis. Its implementation supposes that for any instant T between 1 and N, the time series (Xi) 1 to t and t + 1 to N belong to the same population. The variable to be tested is the maximum in the absolute value of the variable Ut, N defined by:

$$Ut, N = \sum_{i=1}^{t} \sum_{i=t+1}^{N} D_{ij} \qquad (1)$$

where Dij = Sign (Xi - Xj) with: sign (x) = 1 if x > 0, 0 if x = 0 and -1 if x < 0.

If the null hypothesis is rejected, an estimate of the date of discontinuity is given by the instant defining the maximum in the absolute value of the variable Ut, N.

The correlation coefficient is the criterion used to assess the links between the rainfall indices and the climatic indices selected. Denoted by "r," the correlation coefficient is the ratio between the covariance (γ) of two variables (X and Y) and the product of their standard deviations. It indicates the strength and direction of the linear relationship between these variables. Varying between -1 and +1, it reflects a strong correlation if it is less than -0.5 or greater than 0.5:

$$r = \frac{\gamma(X, Y)}{\sigma X \sigma Y} \qquad (2)$$

where γ(X, Y) denotes the covariance of the variables X and Y; σX and σY denote their standard deviations.

To assess the behavior of extreme flows, the Indicators of Hydrologic Alteration (IHA) tool, version 7.1, developed by The Nature Conservancy was used. This tool offers the possibility of comparing the parameters characterizing the flow regimes under different conditions [41]. It uses daily flow values and produces several important statistics. We will only be interested in four of them, considered essential for this study, among which the average and the coefficient of variation (Cv) of extreme flows and the Julian date of the annual minimum and maximum. By dividing the series of values in the
period before and after the discontinuity, the tool calculates the change that occurred in the evolution of each of these parameters after the discontinuity. We can thus analyze not only the sign of change between the two periods but also the magnitude of this difference.

### 2.3.2. Spatial Data

The supervised classification by maximum likelihood of the downloaded Landsat satellite images, using SNAP software (free access), allowed to perform a diachronic analysis of the evolution of land use in the studied watersheds. This operation was preceded by operations of preprocessing and recognition of objects in the field by photography and GPS (Global Positioning System). The preprocessing of satellite images refers to all of the processes applied to raw data to correct the geometric and radiometric errors that characterize certain satellite images. These errors are generally due to the technical problems of the satellites and to the interactions between the outgoing electromagnetic radiation and atmospheric aerosols, also called “atmospheric noise.” These atmospheric disturbances are influenced by different factors that are present on the day of acquisition, including weather, fires and other human activities. They affect all the images acquired by passive satellites, including Landsat 4-5-7 and 8. The downloaded Landsat images are ortho-rectified, the preprocessing concerns the atmospheric correction of these images and their re-projection in the local system (WGS_84_UTM_Zone_32N). For this, neo-channels are created, to increase the readability of the data by enhancing certain less obvious properties in the original image, thus showing more clearly the elements of the scene. Three indices are therefore created, namely the Normalized Difference Vegetation Index (NDVI), the Brightness Index (IB) and the Normalized Difference Water Index (NDWI). These indices highlight respectively the vegetated surfaces, the sterile (not-chlorophyllin) elements such as the urban and the water bodies. Their formulas are as follows $NDVI = (NIR - R)/(NIR + R)$; $IB = (R^2 + NIR^2)^{0.5}$ and $NDWI = (NIR - MWIR)/(NIR + MWIR)$, where NIR: ground reflectance of the surface in the near-infrared channel; R: ground reflectance of the surface in the red channel and MWIR: ground reflectance of the surface in the mid-wave infrared channel. Due to the fact that the watersheds studied extend over several scenes, the enhancement operations were followed by the mosaic of the different scenes used on each date. It is an operation of joining two or more adjacent images into a single image. The use of Google Earth, as well as the spaces sampled from the GPS, made it possible to identify with certainty the IA (buildings, savannas, bare soils and crops), water bodies (large rivers, lakes and ponds) and the forests (secondary, degraded, non-degraded and swampy) of each mosaic. Before the classification operation, the separability of the spectral signatures of the sampled objects to avoid interclass confusion was assessed by calculating the “transformed divergence” index. The value of this index is between 0 and 2. A value >1.8 indicates a good separability between two given classes. The different classes used in this study show good separability between them, whatever the image considered, with indices >1.9 (Table 2). The validation of the classifications obtained was carried out using the confusion matrix, which made it possible to obtain treatment details, in order to validate the choice of training plots. After validating the land use/land cover maps produced, the statistical and spatial differences of each class between the two dates were studied.

<table><caption>**Table 2.** Indices of separability between the different classes considered by watershed and by image. TDI: Transformed Divergence Index and WB: Water Bodies.</caption><thead><tr><th colspan="4">Nyong Watershed</th></tr><tr><th colspan="2">Image from 1973</th><th colspan="2">Image from 2015</th></tr><tr><th>Classes Assessed</th><th>TDI</th><th>Classes Assessed</th><th>TDI</th></tr></thead><tbody><tr><td>Forest-IA</td><td>2</td><td>Forest-IA</td><td>2</td></tr><tr><td>Forest-WB</td><td>1.92</td><td>Forest-WB</td><td>1.98</td></tr><tr><td>WB-Forest</td><td>1.92</td><td>WB-Forest</td><td>1.97</td></tr><tr><td>WB-IA</td><td>2</td><td>WB-IA</td><td>2</td></tr><tr><td>IA-Forest</td><td>2</td><td>IA-Forest</td><td>2</td></tr><tr><td>IA-WB</td><td>2</td><td>IA-WB</td><td>2</td></tr></tbody></table>
<table><caption>Table 2. *Cont.*</caption><thead><tr><th colspan="4">Ntem watershed</th></tr><tr><th colspan="2">Image from 1973</th><th colspan="2">Image from 2015</th></tr><tr><th>Classes assessed</th><th>TDI</th><th>Classes assessed</th><th>TDI</th></tr></thead><tbody><tr><td>Forest-IA</td><td>2</td><td>Forest-IA</td><td>2</td></tr><tr><td>Forest-WB</td><td>1.97</td><td>Forest-WB</td><td>1.98</td></tr><tr><td>WB-Forest</td><td>1.96</td><td>WB-Forest</td><td>1.98</td></tr><tr><td>WB-IA</td><td>2</td><td>WB-IA</td><td>2</td></tr><tr><td>IA-Forest</td><td>2</td><td>IA-Forest</td><td>2</td></tr><tr><td>IA-WB</td><td>2</td><td>IA-WB</td><td>2</td></tr></tbody></table>

# 3. Results

## 3.1. *Changes in Discharges*

### 3.1.1. Average Discharges

The average discharges of Nyong and Ntem are analyzed through the annual and seasonal modules. The annual modules of these watercourses evolve statistically downwards (Figure 3). This decrease is accompanied by a reduction in Cv compared to the post-discontinuity period which seems negligible on the Nyong (−1%), but relatively significant on the Ntem (−8%) (Table 1). The Pettitt test shows discontinuities in the series of these rivers in 1973–74 for the Nyong and 1970–71 for the Ntem. The homogeneous sequence following this discontinuity recorded a deficit compared to the long-term average estimated at −5.6% in the first case and −8.9% in the second (Figure 3). However timidly started in the 1970s, the decrease in the annual modules of Nyong and Ntem increased considerably during the 2000s and 2010s (Table 3). These two decades are the driest of the entire observation period. The decadal deviations from the whole period average for each of them are greater than −5% whatever the watershed considered.

<figure><img src="image_127.png" /><figcaption>**Figure 3.** Results of statistical segmentation of Nyong (black) and Ntem (gray) annual rainfall, discharges and runoff coefficients, according to the Pettitt test. The thick horizontal lines indicate the averages of the whole period, while the thin horizontal lines indicate those of the different homogeneous periods. The thick and broken vertical lines indicate the years of discontinuity.</figcaption></figure>
<table><caption>**Table 3.** Deviations (%) of decadal annual and seasonal averages of rainfall, discharges and runoff coefficients compared to the whole period averages of the studied watersheds.</caption><thead><tr><th rowspan="2">Basins</th><th rowspan="2">Decades</th><th colspan="5">Deviations from the Whole Period Average (%)</th></tr><tr><th>Annual</th><th>Spring</th><th>Summer</th><th>Autumn</th><th>Winter</th></tr></thead><tbody><tr><td colspan="7" style="text-align:center; font-weight:bold;">Rainfall</td></tr><tr><td rowspan="10">Nyong</td><td>1950-59</td><td>0.2</td><td>3.2</td><td>-20.3</td><td>3.2</td><td>19.9</td></tr><tr><td>1960-69</td><td>2.2</td><td>0.9</td><td>-3.1</td><td>1.4</td><td>32</td></tr><tr><td>1970-79</td><td>-1</td><td>-1.8</td><td>-2.9</td><td>-0.1</td><td>3.6</td></tr><tr><td>1980-89</td><td>1.5</td><td>0.8</td><td>10.5</td><td>2.1</td><td>-24</td></tr><tr><td>1990-99</td><td>4.5</td><td>2.1</td><td>23.8</td><td>2.9</td><td>-16.1</td></tr><tr><td>2000-09</td><td>-5.1</td><td>-3.4</td><td>-0.3</td><td>-8.6</td><td>-10.5</td></tr><tr><td>2010-16</td><td>-3.1</td><td>-3.7</td><td>-9.4</td><td>0.9</td><td>-7.3</td></tr><tr><td>1950-59</td><td>-0.2</td><td>3.8</td><td>-30.5</td><td>3.5</td><td>12</td></tr><tr><td>1960-69</td><td>-1.7</td><td>2.2</td><td>-14.9</td><td>-5.6</td><td>15.9</td></tr><tr><td>1970-79</td><td>2.3</td><td>0.6</td><td>1.9</td><td>5.9</td><td>-4.6</td></tr><tr><td rowspan="6">Ntem</td><td>1980-89</td><td>0.5</td><td>2.3</td><td>15.9</td><td>-0.6</td><td>-23.7</td></tr><tr><td>1990-99</td><td>2.5</td><td>-1.2</td><td>30.2</td><td>-0.2</td><td>-14.5</td></tr><tr><td>2000-09</td><td>0.1</td><td>-2.8</td><td>6.8</td><td>-2.1</td><td>13.8</td></tr><tr><td>2010-15</td><td>-5.9</td><td>-7.7</td><td>-12.7</td><td>-2.2</td><td>-4.7</td></tr><tr><td colspan="7" style="text-align:center; font-weight:bold;">Discharges</td></tr><tr><td rowspan="10">Nyong</td><td>1951-59</td><td>-4.8</td><td>10.2</td><td>1.3</td><td>-14</td><td>-4.3</td></tr><tr><td>1960-69</td><td>17.7</td><td>27.3</td><td>22.5</td><td>13.1</td><td>14.9</td></tr><tr><td>1970-79</td><td>-2.6</td><td>0.1</td><td>-12.7</td><td>0.9</td><td>-6</td></tr><tr><td>1980-88</td><td>-0.7</td><td>-8.8</td><td>-11</td><td>8.1</td><td>-3.6</td></tr><tr><td>2000-09</td><td>-6.5</td><td>-14.7</td><td>0.8</td><td>-4</td><td>-7.8</td></tr><tr><td>2010-16</td><td>-5.7</td><td>-12.3</td><td>-8.1</td><td>-7.4</td><td>7.4</td></tr><tr><td>1953-59</td><td>-4.4</td><td>0.2</td><td>-27.6</td><td>-7.6</td><td>3.4</td></tr><tr><td>1960-69</td><td>27.8</td><td>38.1</td><td>34.4</td><td>17.1</td><td>29.3</td></tr><tr><td>1970-79</td><td>-2.2</td><td>-3.5</td><td>-8.1</td><td>-0.4</td><td>-5.2</td></tr><tr><td>1980-89</td><td>-2.7</td><td>-17.4</td><td>-0.4</td><td>11.5</td><td>-12.1</td></tr><tr><td rowspan="3">Ntem</td><td>1990</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>2000</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>2010-15</td><td>-9.1</td><td>0.2</td><td>-12.9</td><td>-15.7</td><td>-10.4</td></tr><tr><td colspan="7" style="text-align:center; font-weight:bold;">Runoff Coefficients</td></tr><tr><td rowspan="10">Nyong</td><td>1951-59</td><td>-41.3</td><td>5.8</td><td>24.7</td><td>-27.9</td><td>-51.2</td></tr><tr><td>1960-69</td><td>-34.2</td><td>20.7</td><td>12.5</td><td>12</td><td>-48.7</td></tr><tr><td>1970-79</td><td>-34.5</td><td>-3.9</td><td>-15.9</td><td>7.8</td><td>-44.7</td></tr><tr><td>1980-88</td><td>64.5</td><td>-8.1</td><td>-31.4</td><td>8.8</td><td>84.1</td></tr><tr><td>2000-09</td><td>36.1</td><td>-6.2</td><td>13.8</td><td>9.2</td><td>44.4</td></tr><tr><td>2010-16</td><td>25.1</td><td>-4.2</td><td>-1.3</td><td>-12.8</td><td>34.5</td></tr><tr><td>1953-59</td><td>-13.9</td><td>-1.6</td><td>8.8</td><td>-15.2</td><td>-21.7</td></tr><tr><td>1960-69</td><td>12.2</td><td>33.8</td><td>42.9</td><td>20.8</td><td>-7.3</td></tr><tr><td>1970-79</td><td>-11.2</td><td>-7.6</td><td>-14.1</td><td>-1.2</td><td>-15.7</td></tr><tr><td>1980-89</td><td>2.3</td><td>-20.8</td><td>-23.4</td><td>10.6</td><td>11.4</td></tr><tr><td rowspan="3">Ntem</td><td>1990</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>2000</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>2010-15</td><td>31.5</td><td>24</td><td>-14.3</td><td>-11.4</td><td>93.1</td></tr></tbody></table>

Regarding the discharges of the rainy seasons, a statistically significant decrease was noted in spring in both watersheds that the Pettitt test situated, in the same years as those of the annual modules. As in the annual timescale, the change in Cv of discharges for this season is slight on the Nyong (−1%), but more accentuated on the Ntem (−4%). The deficits of the periods occurring after the discontinuity are −13.6% for the Nyong and −13.1% for the Ntem (Figure 4). The decrease started since the 1970s seems to persist on the Nyong; however, on the Ntem, a considerable increase in the discharges is observed through decadal deviations. These deviations fell from −3.5% and −17.4% in the 1970s and
1980s to 0.2% in the 2010s (Table 3). In autumn, Nyong discharges decrease statistically insignificantly (Figure 4). However, there has been an important decline in them during the 2000s and 2010s, following the increase that began in the 1960s. Ntem discharges statistically decrease during this season since 1990–91 (Figure 4). This decrease is concomitant with a –4% change in Cv (Table 1). The deficit in the sequence following this discontinuity is –23% (Figure 4). The decadal deviations also show a significant decrease during the 2010s, after a long, generally wet period between the 1960s and the 1980s (Table 3).

<figure><img src="image_128.png" /><figcaption>**Figure 4.** Results of statistical segmentation of Nyong (black) and Ntem (gray) seasonal rainfall, discharges and runoff coefficients, according to the Pettitt test. The thick horizontal lines indicate the averages of the whole period, while the thin horizontal lines indicate those of the different homogeneous periods. The thick and broken vertical lines indicate the years of discontinuity.</figcaption></figure>

In dry seasons, during the summer, the discharges do not change statistically over the two watersheds according to the Pettitt test, although a slight decrease is observed (Figure 4). However, a general decrease since the 1970s can be observed through the decadal deviations, which increased considerably during the 2010s (Table 3). In winter, the Pettitt test does not show any discontinuity in the Nyong discharges, although an increase in them is noted in the 2010s, after the decrease started in the 1970s (Table 3). In the Ntem Watershed, winter flows have decreased statistically and continuously since 1970–71 (Figure 4). The deficit for the post-discontinuity period is –12.8% (Figure 4). Its Cv decreases by 2% (Table 1).

### 3.1.2. Extreme Discharges

The extreme discharges of the Nyong and the Ntem are analyzed through the maximums and minimums discharges on consecutive days (1, 3, 7, 30 and 90).
As regards the maximums first, it should be noted that in the case of the Nyong, the post-discontinuity period (1973–74 to 2016–17) corresponds to a dry phase. However, despite its dryness, the average maximums have remained essentially constant (Table 4). A slight reduction in variability of the maximums is also noted in this period, which seems more significant in the case of shorter-term ranges (1 day to 7 days), for which the Cv oscillate from 26% before discontinuity to 23% after (Table 4). The maximums appear earlier on the Nyong after the discontinuity. Their average Julian date of observation went from 318 before the discontinuity to 309 after (Table 4).

<table><caption>**Table 4.** Statistics relating to the maximum and minimum flows of Nyong and Ntem before and after the discontinuity. IHA: Indicators of Hydrologic Alteration.</caption><thead><tr><th rowspan="2">IHA Statistics</th><th colspan="2">Means (m³/s)</th><th colspan="2">Cv (%)</th><th colspan="2">Change</th></tr><tr><th>Before Discontinuity</th><th>After Discontinuity</th><th>Before Discontinuity</th><th>After Discontinuity</th><th>m³/s</th><th>%</th></tr></thead><tbody><tr><td colspan="7" style="text-align: center; font-weight: bold;">Nyong</td></tr><tr><td>Minimum flows</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>1-day minimum</td><td>25.5</td><td>17.9</td><td>30</td><td>41</td><td>-7.5</td><td>-29.7</td></tr><tr><td>3-day minimum</td><td>26.1</td><td>18.1</td><td>30</td><td>41</td><td>-8</td><td>-30.6</td></tr><tr><td>7-day minimum</td><td>27.8</td><td>19.3</td><td>30</td><td>38</td><td>-8.4</td><td>-30.2</td></tr><tr><td>30-day minimum</td><td>41.5</td><td>26.4</td><td>28</td><td>36</td><td>-15</td><td>-36.2</td></tr><tr><td>90-day minimum</td><td>82.9</td><td>58.5</td><td>31</td><td>29</td><td>-24.3</td><td>-29.3</td></tr><tr><td>Maximum flows</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>1-day maximum</td><td>366</td><td>366</td><td>26</td><td>23</td><td>-0.3</td><td>-0.08</td></tr><tr><td>3-day maximum</td><td>363</td><td>365</td><td>26</td><td>23</td><td>2</td><td>0.55</td></tr><tr><td>7-day maximum</td><td>361</td><td>362</td><td>26</td><td>23</td><td>1.3</td><td>0.36</td></tr><tr><td>30-day maximum</td><td>340</td><td>338</td><td>26</td><td>25</td><td>-2.6</td><td>-0.76</td></tr><tr><td>90-day maximum</td><td>274</td><td>271</td><td>25</td><td>25</td><td>-2.3</td><td>-0.84</td></tr><tr><td>Average Julian dates</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Of minimum</td><td>61</td><td>67</td><td></td><td></td><td></td><td></td></tr><tr><td>Of maximum</td><td>318</td><td>309</td><td></td><td></td><td></td><td></td></tr><tr><td colspan="7" style="text-align: center; font-weight: bold;">Ntem</td></tr><tr><td>Minimum flows</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>1-day minimum</td><td>48</td><td>34.4</td><td>51</td><td>61</td><td>-13.5</td><td>-28.3</td></tr><tr><td>3-day minimum</td><td>50.24</td><td>36.2</td><td>51</td><td>60</td><td>-14</td><td>-27.9</td></tr><tr><td>7-day minimum</td><td>53.1</td><td>38.9</td><td>51</td><td>56</td><td>-14.1</td><td>-26.5</td></tr><tr><td>30-day minimum</td><td>68.71</td><td>53.8</td><td>45</td><td>39</td><td>-14.8</td><td>-21.5</td></tr><tr><td>90-day minimum</td><td>128.4</td><td>107</td><td>33</td><td>29</td><td>-20.8</td><td>-16.2</td></tr><tr><td>Maximum flows</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>1-day maximum</td><td>793.6</td><td>662</td><td>23</td><td>29</td><td>-131</td><td>-16.5</td></tr><tr><td>3-day maximum</td><td>785.4</td><td>656</td><td>23</td><td>30</td><td>-128</td><td>-16.3</td></tr><tr><td>7-day maximum</td><td>767.4</td><td>640</td><td>24</td><td>30</td><td>-126</td><td>-16.4</td></tr><tr><td>30-day maximum</td><td>685.7</td><td>567</td><td>22</td><td>32</td><td>-118</td><td>-17.2</td></tr><tr><td>90-day maximum</td><td>516.7</td><td>426</td><td>22</td><td>29</td><td>-90</td><td>-17.4</td></tr><tr><td>Average Julian dates</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Of minimum</td><td>147</td><td>102</td><td></td><td></td><td></td><td></td></tr><tr><td>Of maximum</td><td>291</td><td>275</td><td></td><td></td><td></td><td></td></tr></tbody></table>

As for the Nyong, the post-discontinuity period (1970–71 to 2015–16) is dry on the Ntem. It is characterized by a decrease in the average values of the different ranges of maximum discharges (Table 4). There are no remarkable differences between the rates of change recorded from one range to another after the discontinuity since they are all between -16.37% (for the 3-day maximum discharge) and -17.4% (for the 90-maximum discharge). The variability of these discharges ranges is modest overall but increases slightly after discontinuity. Their Cv oscillate between 22% and 24% before discontinuity, and between 29% and 32% after (Table 4). The Julian date of the maximums is early—16 days on average after the discontinuity (Table 4).
Concerning the minimum discharges, their evolution differs from that of the maximums for the Nyong. All the different ranges taken into account decreased considerably after the discontinuity, regardless of the flow duration. The rates of change recorded after the discontinuity are fairly close from one range to another, varying between -29.3% and -36.2% (Table 4). The 90-day minimum discharge is the only one for which there is a slight reduction in variability after discontinuity. For the other ranges, an increase is noted, which seems more significant for shorter-term minimums (1-day and 3-days). Their Cv went from 30% before the discontinuity to 41% after (Table 4). The minimums have occurred slightly later on the Nyong after the discontinuity. Their average Julian date of appearance increased from 61 before the discontinuity to 67 after (Table 4).

The minimum discharges of the Ntem follow the same trend as the maximums. The means of the five ranges decrease after the discontinuity. The rates of change recorded are more or less close (oscillating between -16.2% and -28.29%). However, a slight decrease in the magnitude of these changes can be noted with increasing flow duration. The decrease is greater for shorter-term minimums (1-day to 7-days). Overall, the variability is greater for shorter-term minimums. Their Cv have increased slightly after the discontinuity, while those of the longer-term minimum have decreased (Table 4). The minimums have appeared on the average 45 days earlier after the discontinuity (Table 4).

The results of this study corroborate those obtained by Liénou et al. [19] in the case of the Ntem, but differ in that of the Nyong. These authors indicated an absence of discontinuity on the Nyong annual modules between 1950s and 1990s, yet this study highlights a statistically significant decrease since 1973–74. This attests to a considerable decrease in discharges from this watershed in recent decades.

## 3.2. Analysis of the Main Forces Having Important Links with Discharges

### 3.2.1. Rainfall Variability

#### Annual Rainfall

The variability of annual rainfall is very moderate in the Nyong and Ntem watersheds (Table 1). A slight downward trend seems to be emerging in both cases, but the Pettitt test does not highlight any major discontinuity in their respective series (Figure 3). However, this absence of discontinuity does not necessarily imply more or fewer long dry and wet sequences in these watersheds. Analysis of the decadal deviations shows that after the slightly dry 1950s and 1960s on the Ntem, there followed three slightly wet decades, most of which had surpluses above the whole period average of around +2% (Table 3). The 1950s and 1960s were wet on the Nyong, then followed by a dry decade and two wet decades, respectively. The beginning of the 2000s marks the return of a dry period on the two watersheds, characterized by a decrease in precipitations amplified during the decade 2000 in the case of Nyong and 2010 in that of Ntem. For these decades, there are negative deviations from their average over the whole period greater than -5% (Table 3). Such deviations had never been recorded before in these watersheds.

#### Seasonal Rainfall

Rainfall in dry seasons changes in opposite trends (Figure 4), with an increase in summer but a decrease in winter. The Pettitt test highlights discontinuities in the respective time series of summer rainfall in Nyong and Ntem in 1964–65 and 1968–69. The resulting rates of increase from the whole period average are +5.8% and +10.5% (Figure 4). The variability of the rainfall in this season remains intact on the Nyong, while it decreases on the Ntem (Table 1). Although there has been a general increase in precipitation in these watersheds during this season since the 1960s, there is still a stabilization beginning in the 2000s, which was reinforced throughout the 2010s (Table 3).

Concerning winter rainfall, a downward discontinuity is detected in the Nyong and Ntem time series, respectively, in 1976–77 and 1977–78, with significant deficits of -16.1% and -9.7% depending on the watersheds (Figure 4). This decrease is synchronous with a change in Cv of 19% on the Nyong
and 12% on the Ntem (Table 1). Though declining since the 1970s, winter precipitations seem to have increased slightly again since the beginning of the 2000s in both watersheds (Table 3).

Unlike the two dry seasons for which there are contrary trends, the rainfall of the rainy seasons (spring and autumn) show similar trends to that of the annual rainfall (Figure 4). The Pettitt test does not indicate discontinuities in their time series. There has been no continuing downward or upward trend for the entire period studied, although some dry sequences have been observed, particularly in the last three decades. The deviations observed are generally small, but slightly more pronounced in the spring, especially during the 2010s (Table 3).

These results are similar to those observed on the Ogooue further south by Mahé et al. [42], which are also found partly in the Congo Watershed [43], and on the rivers of southern Gabon and northern Congo such as Nyanga and Kouilou [19].

## Relationships between Rainfall and Some Potential Sources of Variability

The changes in precipitation described above (slight decrease in annual rainfall and rainy seasons, cross-trend evolution in dry season rainfall) motivate us to the examination of the links between the rainfall in these basins and certain relevant sources of large-scale climate variability (SOI, NATL, SATL and DMI), which may influence precipitation in the region [39]. Table 5 presents the correlations between the anomalies of the representative climatic indices of these sources of variability and those of rainfall in the basins studied at annual and seasonal time steps. This analysis shows significant correlations in the two basins between the anomalies of the NATL indices and those of rainfall during spring but also between the anomalies of these same indices and those of rainfall in winter. Important links have also been demonstrated between the rainfall anomalies of these watersheds and those of the SOI in winter.

<table><caption>**Table 5.** Correlation between rainfall indices and climatic indices at annual and seasonal time steps. Significant correlations are in bold.</caption><thead><tr><th>Watersheds</th><th>Indices</th><th>Annual</th><th>Spring</th><th>Summer</th><th>Autumn</th><th>Winter</th></tr></thead><tbody><tr><td rowspan="4">Ntem</td><td>DMI</td><td>-0.05</td><td>-0.15</td><td>0.22</td><td>-0.1</td><td>-0.06</td></tr><tr><td>NATL</td><td>-0.03</td><td><strong>-0.52</strong></td><td>-0.04</td><td>-0.02</td><td><strong>0.58</strong></td></tr><tr><td>SATL</td><td>-0.06</td><td>-0.05</td><td>0.06</td><td>-0.2</td><td>0.43</td></tr><tr><td>SOI</td><td>0.03</td><td>-0.17</td><td>0.12</td><td>-0.09</td><td><strong>0.51</strong></td></tr><tr><td rowspan="4">Nyong</td><td>DMI</td><td>-0.23</td><td>-0.09</td><td>0.09</td><td>-0.23</td><td>-0.02</td></tr><tr><td>NATL</td><td>-0.17</td><td><strong>-0.54</strong></td><td>-0.09</td><td>-0.11</td><td><strong>0.52</strong></td></tr><tr><td>SATL</td><td>0</td><td>-0.08</td><td>0.14</td><td>-0.12</td><td>0.36</td></tr><tr><td>SOI</td><td>0.21</td><td>-0.04</td><td>0.1</td><td>0.16</td><td><strong>0.53</strong></td></tr></tbody></table>

NATL indices are negatively correlated with rainfall during spring (Table 5). This implies that warming (cooling) of the Tropical North Atlantic is associated with the low (heavy) precipitation over these watersheds during this season (Figure 5). This configuration was sometimes taken by default on the two watersheds, in particular in 1986–87, 1990–91, 1993–94, 1996–97, 2010–11, etc. Opposite signs of NATL indices and the rainfall anomalies were, however, noted for more than 70% of the years studied in each of these watersheds (Table 6).

<table><caption>**Table 6.** Frequency of years having the same/opposite signs of ocean indices and rainfall anomalies. NY: Number of years; NYOS: Number of years with opposite signs; NYSS: Number of years with same signs.</caption><thead><tr><th rowspan="2">Watersheds</th><th colspan="3">NATL&Rainfall (Spring)</th><th colspan="3">NATL&Rainfall (Winter)</th><th colspan="3">SOI&Rainfall (Winter)</th></tr><tr><th>NY</th><th>NYOS</th><th>%</th><th>NY</th><th>NYSS</th><th>%</th><th>NY</th><th>NYSS</th><th>%</th></tr></thead><tbody><tr><td>Nyong</td><td>34</td><td>24</td><td>71</td><td>34</td><td>23</td><td>62</td><td>65</td><td>35</td><td>54</td></tr><tr><td>Ntem</td><td>33</td><td>23</td><td>70</td><td>33</td><td>20</td><td>61</td><td>64</td><td>42</td><td>66</td></tr></tbody></table>
<figure><img src="image_129.png" /><figcaption>**Figure 5.** Comparison of climatic indices and normalized rainfall anomalies.</figcaption></figure>

The NATL indices, on the other hand, are positively correlated with winter rainfall (Table 5). An increase in the temperature of the Tropical North Atlantic would be concomitant with abundant rainfall in the watersheds studied during this season and conversely (Figure 5). A different situation was nevertheless observed in these watersheds in 1990–91, 1996–97, 1997–98, 2004–05, 2015–16, etc. The proportion of years for which the same sign of anomalies was observed between these two variables during winter is 62% in the Nyong basin and 61% in that of Ntem (Table 6).

As in the previous case, the SOI is positively correlated with the rainfall in the watersheds studied in winter (Table 5). This implies that a warming of the Pacific Ocean is synchronous with the generally abundant rainfall during this season (Figure 5). This situation was not, however, observed for certain years (1950–51, 1963–64, 1968–69, 1970–71, 1984–85, 1990–91, 1996–97, etc.). The percentage of years having recorded the same sign of anomalies between rainfall and SOI in winter is 54% for the Nyong and 66% for the Ntem. Correlation between rainfall and SOI anomalies is good in both watersheds in winter, but the proportion of years with the same sign of anomalies does not give enough clear information in the case of the Nyong.

The fact that winter rainfall is correlated with both the temperatures of the Tropical North Atlantic and the Pacific Ocean indicates a probable simultaneous control of these two oceans on the precipitation of the whole region during the winter.

Bogning et al. [44] recently demonstrated that an important relationship exists between the March-April-May (MAM) rainfall and the tropical Atlantic Niño. According to them, the negative temperature anomalies in the south-eastern equatorial Atlantic are at the origin of the positive anomalies of the MAM rainfall in the Ogooue watershed. The negative correlation coefficient between these two variables is high ($r = -0.74$). This study also highlights a relatively strong link ($r = -0.52$ and $-0.54$) between the spring rainfall of two equatorial watersheds located further north of the Equator and the
temperatures of the Atlantic ocean, with the difference that the link with the rainfall of this season has been established this time with the temperatures of the Tropical North Atlantic of the same season.

### 3.2.2. Land Use Dynamics in the Studied Watersheds

A diachronic analysis of the classifications carried out on two dates (1973 and 2015) shows for both watersheds changes in land use marked not only by an increase in IA but also by a decrease in forests and water bodies (Figure 6). Between 1973 and 2015, forests and water bodies decreased by 6% and 42%, respectively, in the Nyong watershed. Their decreases on the Ntem between 1973 and 2015 are 1% and 32%. Between these same years, the increase in IA reached 771% in the Nyong watershed and 451% in the Ntem watershed (Table 7). Figure 6 also shows that the disappearance of the forest cover is localized around urban centers, mainly located in the west, near Ngoazik in the case of Ntem. For the Nyong, deforestation is certainly continuous throughout the watershed, but the change is more significant in the west, with the important boom in the city of Yaoundé, not far from Mbalmayo.

<figure><img src="image_130.png" /><figcaption>Figure 6. Spatial distribution of the main land use patterns in the studied watersheds at two dates.</figcaption></figure>

<table><caption>Table 7. Evolution of the main land use modes in the Nyong and Ntem watersheds.</caption><thead><tr><th rowspan="2">Basins</th><th rowspan="2">Land Use Modes</th><th colspan="2">Area Occupied in the Basin (km²)</th><th colspan="2">Change</th></tr><tr><th>1973</th><th>2015</th><th>km²</th><th>%</th></tr></thead><tbody><tr><td rowspan="3">Nyong</td><td>Forest</td><td>14,174</td><td>13,326</td><td>-848</td><td>-6</td></tr><tr><td>Water bodies</td><td>146</td><td>84</td><td>-62</td><td>-42</td></tr><tr><td>IA</td><td>118</td><td>1028</td><td>910</td><td>771</td></tr><tr><td rowspan="3">Ntem</td><td>Forest</td><td>19,469</td><td>19,301</td><td>-168</td><td>-1</td></tr><tr><td>Water bodies</td><td>53</td><td>36</td><td>-17</td><td>-32</td></tr><tr><td>IA</td><td>41</td><td>226</td><td>185</td><td>451</td></tr></tbody></table>
Moffo [25] and Ebodé [26] obtained similar results, respectively, in the Mfoundi (Mefou sub-watershed) and Mefou (Nyong sub-watershed) watersheds.

### 3.3. *The Impacts of Environmental Forces on the Discharges*

#### 3.3.1. Rainfall

The impact of rainfall is perceptible in the evolution of flows. In general, the annual precipitation of the two watersheds studied does not decrease significantly, while the flows register downwards discontinuities during the 1970s. The stationarity of the hydrological regime and that of the precipitations of the watersheds at this time step thus departs from the general logic, which admits that hydrological discontinuities occur following rainfall discontinuities. However, for both watersheds, the analysis of the decadal deviations from the long-term average of the two compared variables shows a synchronous evolution between them over the last two decades. For the latter, there is a concomitant decrease in rainfall and flows (Table 3).

During the rainy seasons, primarily in the spring, precipitation has changed moderately, so that no sudden mean inflexion point has been detected in their series. In return, the discharges and the runoff coefficients for this season have decreased significantly since the 1970s. Similarly, the examination of the deviations from the mean of these variables generally shows significant decreases for the discharges and the runoff coefficients, while the rainfall does not decrease or decreases very slightly (Table 3). The decline in winter rainfall since the 1970s appears to be closely related in amplitude to that of spring flows. Although the correlations between them are not significant (r < 0.32 on both catchments), the rhythm of evolution of these two variables over the decades is fairly constant. This is observed during the 2010s, for which a small drop in winter rainfall led to a similar evolution in spring runoff coefficients and discharges (Table 3). This influence of winter precipitation has already been suggested by Liénou et al. [19]. These authors wrote that from the 1970s, the decrease in winter rainfall created a greater water deficit (evaporation and soil water reserve) in the watershed area at the start of the first rainy season in spring. This aridification of winter means that a greater part of the precipitation received during the spring first contributes to filling this water deficit, and therefore, the fraction that generates runoff is reduced. This then translates into lower runoff volumes for the same average depths of precipitation during spring, which explains the drop in the runoff coefficient (Figure 4).

In autumn, rainfall and flows decrease very slightly over the entire study period. However, between the 1970s and 1980s, flows and runoff coefficients increased, while the rainfall did not vary much overall. This increase would be due to the increase in summer rainfall. The impact of Summer rainfall on autumn flows is more noticeable during the 2000s and 2010s, for which there is a joint decrease in summer rainfall, discharges and runoff coefficients of autumn, despite a sometimes-negligible variation in autumn rainfall in the different watersheds (Table 3). In view of these developments, it appears that the increase in summer rainfall during the decades from 1970 to 1990 considerably reduces the deficit of evaporation and soil water reserves at the beginning of the autumn, favoring runoff [19]. The part of the precipitation that actually participates in the runoff increases, hence, there is an increase in the autumn runoff coefficient during these decades (Table 3). The decrease in summer rainfall during the 2000s and 2010s has the opposite effect, which is why there has been a reduction in runoff coefficients during these decades, responsible for a decrease in discharges. This is all the more probable since the analysis of the average monthly flows shows that the variations of the summer rainfall are generally felt more on the flows of the first month (September) of autumn. The model developed for this purpose is significant, with a fairly correct quality of fit (r > 0.6) on the two basins (Figure 7). It could be used to predict fairly accurately the runoff in early autumn (September) based on the rainfall recorded during the summer.
<figure><img src="image_131.png" /><figcaption>**Figure 7.** Regression between September (first month of autumn) discharges and summer (July and August) rainfall.</figcaption></figure>

In the summer, we notice that rainfall increases statistically, while flows evolve according to varying magnitudes downwards. These opposite trends could be explained by the fact that the summer flows are partly linked to the volumes precipitated during the spring, which occurs just before, even if the correlation between them is not significant ($r < 0.35$ on both basins). This is why we can note, in the evolution of summer flows, dry decades even when the rainfall was abundant. This was the case in the 1980s, when there was a surplus above average for the summer rainfall, but shortages for both the spring rainfall and the summer flows. However, the variations in summer precipitation themselves seem to also be related to the flows in the season. They participate in the event of a moderate evolution of the spring rainfall, in the maintenance of flows in some cases, or in their reduction in others. This last scenario is observed during the 2010s, where spring rainfall did not vary considerably, but where a decrease in summer rainfall is concomitant with a decrease in flows in the different watersheds (Table 3).

In winter, precipitation and flows decrease according to different magnitudes. The stationarity of these two variables does not reveal an apparent link in the case of Nyong. Additionally, the correlations between them are weak ($r < 0.39$). However, the analysis of the deviations from the average shows clearer links between these variables, since the beginning of the drought started during the 1970s in these two watersheds (Table 3). The succession of deficit rainfall sequences that were observed between the 1970s and 1990s also led to deficits in terms of flows. Similarly, the resumption of rainfall observed during the 2000s caused an upsurge in the flows, which was interrupted again in the two watersheds studied during the 2010s, following the relapse of the rainfall (Table 3).

Several common and divergent points can be noted in the evolution of rainfall and its impact on discharges during the different seasons in the two studied watersheds. Spring discharges have decreased since the beginning of the 1970s, more markedly for the Ntem in the South of Cameroon close to the Equator, while rainfall has not decreased significantly, even though since 2005, several years of slightly weaker spring rainfall have been recorded. In summer there is a net increase in rainfall in the two watersheds from the early 1980s to the late 2000s, then a return to less abundant rainfall. Summer discharges do not seem to follow a variation related to this variability in rainfall. In autumn the rainfall does not drop on the Ntem, except for a few years of less abundance during the most recent decades, while for the Nyong the rainfall has decreased slightly and fairly regularly since the end of the 1980s. Diminution in these watersheds would result from their respective belonging to the equatorial (Ntem) and sub-equatorial (Nyong) domain, which is already at the origin of the significant
differences on the duration of each season on these. The discharges seem to have decreased slightly for the Nyong, since the 1990s, in apparent link with the slight decrease of the rainfall, while the discharges of the Ntem seem to decrease also since the middle of the 1990s, without the rainfall having decreased significantly. It should be noted that the period of the possible change in discharges also corresponds to a period of significant gaps in discharge data series, which does not allow great significance for these analyses. In winter the series of rainfall of the two watersheds show a discontinuity in 1976–77, which separates the wet period from the recent drier period. Although during the 2000s the rainfall became close to the totals for the wet period, the discharges of the two rivers followed the same variation, with values generally lower after 1970—even in the 2000s, when the rainfall apparently increased.

The results, obtained in this study from recent data, corroborate the first observations of change in the hydrological regime in the region [19]. These changes attest to the visible and lasting impact of a change in the rainfall regime which began in the late 1970s, and which have major repercussions on hydrological regimes and the seasonal availability of surface water resources.

### 3.3.2. Changes in Land Use Patterns

The changes in land use patterns seem to have more or less significant links with certain hydrological modifications observed. The slightly early arrival of the maximum noted at the outlets of the two watersheds (Table 4), in a context where rainfall is decreasing slightly, seems to be related to the increase in IA (+771% for the Nyong and +451% for the Ntem) and the diminution in forests (–6% for the Nyong and –1% for the Ntem). These changes could reduce the time it takes for the maximums to appear by increasing the runoff they cause. The stability observed for the Nyong maximums after the discontinuity (Table 4) could also be a consequence of these same developments. Indeed, in a context where the rainy seasons rainfall, which generate the maximum flows, decrease, the most logical would have been to see the maximums decrease, which is, however, not the case. The current modest urbanization rate in this watershed seems to be the most relevant factor to justify this lack of observed trend. In this case, the decrease in precipitation appears to have been offset by the increased runoff. The annual runoff coefficients of this watershed have increased significantly since the 1980s (Table 3), following an accelerated urbanization phase in the western part of this watershed (Yaoundé region) over the 1980s and 1990s, following the subdivision operations undertaken since the end of the 1970s by the municipal authorities [45]. The decrease in the water bodies noted could have contributed to that of the minima in the two watersheds. When there are in large number, water bodies contribute to the supply of rivers during low water periods and contribute to the maintenance of low flows [46]. However, otherwise, their support for the flows decreases, which causes a decrease in minimum flows.

The impact of the increase in the IA on runoff has already been demonstrated in other forest environments (South America), particularly in the cases of certain watercourses such as the Amazon [47], Upper Xingu [48] and Parana [49]. The discharges of these watercourses have indeed increased, following a reduction in the forest area of their watersheds, which seem relatively greater than that observed in the Nyong and Ntem watersheds. The rate of deforestation in the entire Amazon basin, for example, reaches 30% [47].

Certainly, there is reason to wonder whether such small changes in land use could cause changes in extreme flows as visible as those observed in the watersheds investigated. Since the evolution of these extremes does not agree with other phenomena that have a proven impact on flows, in this case the decrease in precipitation highlighted in this study and the rise in temperatures postulated by Sighomnou [50] and Amougou et al. [51] in the region, which act rather in the direction of a reduction in flows and a late observation of the maximums, it seems appropriate to think that the changes in land use, although modest overall, might be the main cause of these evolutions. Their action on flows could have been considerably amplified by their location in the watersheds (to the west part, near the outlets of the watersheds studied), which would favor the rapid routing of rainwater bodies with the least possible losses to the outlets, thereby erasing the size and forest cover effect of these watersheds.
If this situation persists, there is every reason to believe that there will be an increase in maximum flows in the Nyong within a few years, despite the decrease in precipitation.

To be complete, a further study should also analyze the evolution of daily rainfall and dry days within the rainy season, and even the possible changes in the frequency and intensity of rainfall events, which may have significant impact on runoff, as is the case in the Sahel, for instance [52].

# 4. Discussion: Analysis of Results in the Regional Context

The results of this study have several points in common with similar studies already carried out in Central and West Africa. This study highlights a slight decrease in annual rainfall in the watersheds of Central Africa, rainy seasons and significant changes, but in opposite phases of rainfall in the dry seasons (decrease for winter and increase for summer). The same observation has already been made in other studies carried out in the equatorial region [19]. Discontinuities marking a drop in discharges were highlighted in this study through statistical tests. This corroborates other studies in the region in which the authors have reported discontinuities in the hydrological time series of the Ntem in 1970 [19] and the Ogooue in 1977 [53]. The variability of the flows highlighted for these rivers on a seasonal level reveals a different evolution from the spring and autumn floods. Spring floods decrease significantly compared to those of autumn, which vary little overall. Similar changes have been highlighted by Mahé et al. [42] on Ogooue and Kouilou. In general, this decrease in spring and autumn floods in the equatorial zone is associated with a decrease in rainfall during these seasons which would itself result, not only from anomalies in ocean surface temperatures [54–56], but also local zonal wind anomalies [55,56].

This study reveals changes in land use patterns over time in studied watersheds, which are mainly marked by a decrease in forest and water bodies, and conversely an increase in IA (built, bare soil and agricultural areas). Similar observations had already been made by other authors in Central [26] and West Africa [57–59]. One of the main results of this work reveals that the observed deforestation is greater around urban centers. The same remark was made in Niger by Amogu et al. [23]. They claim in their study that the capital Niamey and its surroundings have experienced more deforestation than the rest of the study area. They add that the causes of this deforestation are essentially linked to the cutting of wood for domestic use.

A few studies in Central Africa [25,26] and several in West Africa [21–24,60] have shown an increase in average discharges, following an increase in IA in the watersheds of certain rivers. This is not the case in this work. Despite the increase in IA, average discharges remain low. This could be linked to the size of the catchments, but also their large forest cover. However, Li et al. [61] stated in their study that IA could not influence the flow rates of a watershed until it reached 50% of its total area. This may be in disagreement with the observation made in this study. Urbanized areas occupy less than 10% of the total surface of the studied watersheds, but their probable effects are still perceptible on extreme flows. The proximity of land-use changes to the outlets of that watersheds could explain the impact of such modest urbanization rates on flows. As ground observations data are sparse and mostly lacking for many rivers in the region, further improvements could come from satellite derived data like for the Ogooue [44] and from a better and exhaustive track of human sources of water abstraction as in the case of the Logone River in Central Africa [62].

# 5. Conclusions

In this paper we studied the possible impact of climate change and anthropization on river flows of two basins of Central Africa (Nyong and Ntem in South Cameroon), from long-term rainfall and discharges time series and changes in land cover over time from satellite images. Considering the rainfall, the 1970s and 1980s marked the start of a major change in the rainfall pattern during the dry seasons in Central Africa, characterized by an increase in rainfall totals in summer and a decrease in winter. These changes, which prevailed until the end of the 1990s, led to a change in the flows of the rainy seasons that directly follow them. Thus, there has been an increase in autumn flows and a
drop in winter flows over the same period. The beginning of the 2000s was marked by a reversal of trends. In the more recent years, there is now a significant drop in rainfall in summer accompanied by a decrease in autumn flows, and conversely, an increase in winter rainfall concomitant with an increase in spring flows, which nevertheless seems rather small on the Nyong. The combined effect of the decrease in rainfall (–16% for Nyong and –8% for Ntem) during the winter and water bodies (–42% in the case of Nyong and –20% in that of Ntem) are the most relevant elements that can be associated to the decreases observed in minimum flows. Considering the survey of the land cover change, the increase of 771% in IA to the detriment of the forest seems to have contributed to the maintenance of maximum flows in the Nyong watersheds, although in a context marked by a deficit in rainfall. Such changes favor an accentuation of the runoff, which modifies the rhythm of the flows, especially those of short duration. This particular aspect could be further investigated by studying the changes in daily rainfall. Eventually, the fluctuations of the average discharges of the rivers of Central Africa depend for the moment mainly on rainfall; however, those of the extreme flows are the result of the combined effect of the two forces analyzed (precipitation and anthropization).

**Author Contributions:** Conceptualization: V.B.E., G.M., J.G.D. and J.A.A.; methodology: J.G.D., J.A.A. and G.M.; software: V.B.E.; validation: G.M., J.G.D. and J.A.A.; formal analysis: V.B.E. and G.M.; investigation: V.B.E.; data curation V.B.E.; writing—original draft preparation: V.B.E. and G.M.; writing—review and editing: V.B.E. and G.M.; project administration: G.M.; funding acquisition: G.M. All authors have read and agreed to the published version of the manuscript.

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

**Acknowledgments:** The authors wish to thank the Cameroonians authorities and national services of Meteorology and Hydrology, for providing data to the Joint International Laboratory (LMI) DYCOFAC. The authors also thank the National Observing System M-TROPICS (Multiscale TROPICAL CatchmentS) from France, which subsidizes observations in experimental tropical watersheds. The authors warmly thank the agents of the IRD (Research Institute for Developing countries) Représentation in Cameroon and to the direction of the LMI DYCOFAC, at Yaounde, for their administrative and financial support. The authors also wish to address a memoriam to Ms. Claudine Dieulin, from the IRD, who passed away late January, and who spent years in developing the open-source database HSM-SIEREM, dedicated to the African hydrology and climatology, and which data have been used partly in this study.

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

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<figure><img src="image_132.png" /></figure>

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Article

# Expanding Rubber Plantations in Southern China: Evidence for Hydrological Impacts

Xing Ma<sup>1,2</sup>, Guillaume Lacombe<sup>3,*</sup>, Rhett Harrison<sup>4</sup>, Jianchu Xu<sup>2</sup> and Meine van Noordwijk<sup>5</sup>

1. Yunnan Institute of Environmental Sciences, Kunming 650034, China; maxing@yies.org.cn

2. World Agroforestry Centre (ICRAF), East Asia, Kunming 650204, China; j.c.xu@cgiar.org

3. International Water Management Institute (IWMI), Vientiane PO BOX 4199, Laos

4. World Agroforestry Centre (ICRAF), East & Southern Africa Region, 13 Elm Road, Woodlands, Lusaka 999134, Zambia; r.harrison@cgiar.org

5. World Agroforestry Centre (ICRAF), Southeast Asia, Bogor 16001, Indonesia; m.vannoordwijk@cgiar.org

* Correspondence: guillaume.lacombe@cirad.fr

Received: 21 January 2019; Accepted: 23 March 2019; Published: 29 March 2019

**Abstract:** While there is increasing evidence concerning the detrimental effects of expanding rubber plantations on biodiversity and local water balances, their implications on regional hydrology remain uncertain. We studied a mesoscale watershed (100 km²) in the Xishuangbanna prefecture, Yunnan Province, China. The influence of land-cover change on streamflow recorded since 1992 was isolated from that of rainfall variability using cross-simulation matrices produced with the monthly lumped conceptual water balance model GR2M. Our results indicate a statistically significant reduction in wet and dry season streamflow from 1992 to 2002, followed by an insignificant increase until 2006. Analysis of satellite images from 1992, 2002, 2007, and 2010 shows a gradual increase in the areal percentage of rubber tree plantations at the watershed scale. However, there were marked heterogeneities in land conversions (between forest, farmland, grassland, and rubber tree plantations), and in their distribution across elevations and slopes, among the studied periods. Possible effects of this heterogeneity on hydrological processes, controlled mainly by infiltration and evapotranspiration, are discussed in light of the hydrological changes observed over the study period. We suggest pathways to improve the eco-hydrological functionalities of rubber tree plantations, particularly those enhancing dry-season base flow, and recommend how to monitor them.

**Keywords:** agroforestry; catchment hydrology; humid tropics; hydrological modeling; impact assessment; land-cover change; Montane Southeast Asia; rubber; trend detection; water balance

## 1. Introduction

Over recent decades, commercial plantations of rubber trees (Hevea brasiliensis) have expanded rapidly across tropical areas around the world, especially in the uplands of Southeast Asia. These plantations have largely encroached on lands that were covered by forests, including land used for annual cropping as part of rainfed rice-based shifting cultivation systems [1,2]. Between 2003 and 2010, about 15,000 km² of land were converted to rubber plantations in Cambodia, Southern China, Thailand, and Vietnam [3]. This massive land-cover conversion is threatening biodiversity [4–7], resulting in the loss of ecosystem services [1,8–12]. One important environmental effect of land conversion on rubber plantations is the modification of local and regional water balances [13–18]. Hevea brasiliensis is an equatorial species indigenous to the Amazon rain forest. Rubber trees are bigger water consumers than native forest species in Southeast Asia, due in part to their large xylem vessels [19] and an extended root system allowing a wide part of the soil to be explored for water uptake [20–22]. Depending on the season and the zoning of soil moisture, the tree is able to shift from the shallow to the deep soil layer to extract water from where it is most abundant at that point in time, indicating significant plasticity in
sources of water uptake [18–23]. These key features enable rubber trees to thrive through the period of greatest water demand. As opposed to the original primary or secondary forest, which exhibit diverse timing in phenology, rubber trees show strong synchronicity for flushing and shedding [17].

The effect of rubber plantations on water balance has been assessed at the plot level [13]. Using measured soil moisture profiles and above-canopy radiations, the authors showed that the rates and timing of water consumption by rubber trees and traditional land-cover types (tea, secondary forest, and grassland) are drastically different. Deep root water uptake of rubber during the dry season is controlled by leaf phenology (leaf loss and renewal), which is determined by day-length, whereas the increased water demand of native vegetation starts with the arrival of the first monsoon rainfall. At the basin level, simulations with a rubber evapotranspiration (ET) model suggested greater water losses through ET from rubber-dominated landscapes than from traditional vegetation cover [14]. Fifteen years of paired watershed rainfall and runoff measurements and one year of paired eddy covariance water flux data from a primary tropical rain forest (51.1 ha) and a tropical rubber plantation (19.3 ha) in the Xishuangbanna prefecture in Southern China helped assessing how rubber plantations affect local water resources [15]. The mean annual ET from the rubber plantation (about 1130 mm) surpassed that of the indigenous forest (about 950 mm), despite a similar rainfall input. A drying up of the streamflow was observed downstream of the rubber plantation in the dry season, while the stream coming from the tropical forest remained perennial. When soil water is limited, rubber trees can regulate stomatal conductance to prevent excessive xylem cavitation [24]. This adaptive capacity to cope with a temporary water shortage enhances the aptitude of rubber trees to deplete soil water resources when they become available again, at the beginning of the wet season. However, the replacement of native forest vegetation with rubber can increase run-off variability and the magnitude of storm streamflow. This situation is observed in tree plantations subject to soil compaction that reduces the soil permeability, despite a greater potential of root water uptake [16]. This analysis indicates that the hydrologic impact of tropical forest conversion to rubber plantation is controlled not only by the different phenology of the trees, but also by the land management, which can strongly influence soil hydraulic properties and lead to greatly modified soil infiltration capacity, as has been observed elsewhere in Southeast Asia [25].

The effects of land-cover change on watershed hydrology are often compounded by climate variability [26]. Several methods exist to separate the relative contributions of climate variability and land-use/land-cover change to watershed hydrological changes, with the most robust ones combining modeling and statistical approaches [27]. Two main techniques are usually applied in small- to medium-size watersheds (i.e., <1000 km²). Paired watershed studies establish the statistical relationships for outflow variables between two neighboring watersheds, ideally similar in geomorphology, area, land cover, and climate. Following calibration, land-cover treatments are applied to one watershed, and changes in the statistical relationships are indicative of the land treatment effect on hydrology. Important limitations of this approach are the relatively few samples used for model development, and the spatial variability of rainfall events between the two watersheds [28]. A second approach involves the calibration of a rainfall-runoff model in one single watershed. The model is first calibrated before a land-cover treatment occurs. The model is then used as a virtual control watershed, along with the rainfall observed after the land-cover treatment, in order to reconstitute runoff as if no change in the watershed had occurred. An underlying assumption for this approach is that the watershed behaviour is stationary in each of the pre-treatment and post-treatment periods. This assumption is seldom tested. In addition, very few studies have tested the statistical significance of observed hydrological changes [28].

Our objectives were twofold: (a) verify if the conversion of forest to rubber tree plantations in a second-order tributary of the Mekong River in Southern China actually reduced seasonal streamflow, as can be expected from local water balances and larger-scale predictive modeling studies, and (b) assess how this relationship is influenced by land use and geomorphological heterogeneities. For these aims, the following steps were undertaken:
1. Select a watershed (about 100 km²) which has been subject to intense land conversion to rubber tree plantations during a long period, with available rainfall and streamflow records,

2. Map land-cover types and their changes over time and according to elevations and slopes,

3. Quantify the spread of rubber trees and the recession of other land-cover types, and see how this is influenced by geomorphology,

4. Model the rainfall-runoff relationship, assess the statistical significance of its temporal variations, and verify if they are consistent with observed changes in land cover, particularly those related to the spread of rubber tree plantations,

5. Interpret the correlations between observed land-use changes and hydrological changes.

## 2. Materials and Methods

### 2.1. Study Site

In the Xishuangbanna prefecture, about 80% of rubber plantations are located at elevations below 1000 m, on slopes varying between 8 and 25 degrees [29]. More recently, rubber trees were planted in the less suitable remaining areas, with steeper slopes at a higher elevation [30].

The watershed of the Nan'a River (95.1 km²) in the Upper Mekong Basin, is located in the southwestern part of the Xishuangbanna prefecture, Yunnan Province, China, about 11 km from Myanmar. The elevation and annual average temperature range from 590 to 2174 m [31] (Figure 1) and from 30.4 to 22.2 °C, respectively. According to the hydrometeorological data recorded by the Xishuangbanna Water Resources and Hydrological Bureau, mean annual rainfall (1992–2005) varied from 1373 mm in the lower part of the basin to 1767 mm in the upper part; over the same period, mean annual streamflow yielded 754 mm. About 85% of annual rainfall occurs in the wet season (May–October). The mean lowest flow (26.1 mm·mo⁻¹) and highest flow (132.3 mm·mo⁻¹) are generally observed in April and August, respectively. In 2013, the watershed included six villages inhabited by 2860 persons whose main income originated from rubber. Before rubber plantations started expanding in the watershed in the late 1980s, farmland in the lower part of the watershed included paddy rice and vegetables. Farmland in the upper part of the watershed included rainfed rice and corn. Most of the farmland in the lowland was converted to banana plantations in the 2010s, following a drop in rubber price. No major water infrastructures, such as dams, existed in the watershed during the study period.

<figure><img src="image_133.png" /><figcaption>**Figure 1.** Location of the Nan'a watershed and measurement devices. Contour lines were derived from the ASTER Global Digital Elevation Model [31].</figcaption></figure>
## 2.2. Hydrometeorological Data

The two rain gauges installed by the Xishuangbanna Water Resources and Hydrological Bureau are located in the downstream and upstream parts of the watershed (Figure 1) and have been recording daily rainfall since 1992. The watershed-scale daily areal rainfall was derived from the point measurements using the Thiessen polygons method. The daily stream water level has been measured at the outlet of the watershed since 1992 with a water level recorder at 10-minute time intervals (Figure 1). Due to the stability of the river cross section at the watershed outlet, the rating curve (relationship between water level and discharge) was determined only once, when measurements were initiated in 1992. The streamflow gauging station was abandoned in 2006. These hydrometeorological data underwent quality control by the Yunnan Water Resources and Hydrological Bureau. Inter-annual means of areal monthly reference evapotranspiration (ET₀) were derived from the Climate Research Unit database [32].

## 2.3. Land-Cover Mapping

A visual interpretation of three Landsat products (TM1992, ETM2002, and TM2007) with 30 m resolution [33], and one RapidEye product from 2010 with 5 m resolution [34] resampled to 30 m resolution allowed the mapping of six major land-cover types inside the watershed: forest, rubber, grassland, farmland, settlement, and open water bodies. The overall accuracy of this classification was assessed by calculating an error matrix applied to 59 ground control points for the 2010 land-cover map and by computing the Kappa coefficient. Land-cover transitions between 1992 and 2002 and between 2002 and 2007 were quantified using the intersection method available in ArcGIS [35,36]. The spatial distribution of rubber plantations across elevations and slopes in 1992, 2002, and 2007 was analyzed using a 30 m digital elevation model [31] and standard algorithms available in ArcMap 10.2.

## 2.4. Assessment of Hydrological Changes

Inter-annual variation in the rainfall-runoff relationship was quantified by producing cross-simulation matrices using the model GR2M [37]. GR2M is a monthly lumped conceptual water balance model that was empirically developed using a sample of 410 basins under a wide range of climate conditions. GR2M requires areal rainfall and ET₀ to estimate streamflow. The model includes a soil moisture accounting production store and a routine store that empties following a quadratic function. The two parameters of the model determine the capacity of the production store and the value of an underground water exchange coefficient applied to the routine store. Compared with several widely used water balance models, GR2M ranks amongst the most reliable and robust [37]. For this analysis, like in most hydrological analyses performed in the Mekong Basin, each hydrological year starts in April and ends in March of the following year, so as to group rainfall and related streamflow in the same year. The model was repeatedly calibrated over each hydrological year of the study period. The first year (April 1992–March 1993) was dedicated to model warming for the adjustment of the initial water levels in the model reservoirs. Thirteen model calibrations were produced from April 1993 to March 2006. Over each hydrological year, the model was calibrated by maximizing the Nash–Sutcliffe efficiency criteria (NSEq) applied to streamflow q (mm·mo⁻¹) for the evaluation of hydrological changes during the wet season. The Nash–Sutcliffe efficiency criteria calculated on the logarithm of q (NSElnq) was used for the evaluation of hydrological changes during the dry season [38]. While each of these two efficiency criteria are calculated with the 12 monthly flow values of each 1-year calibration period (including wet- and dry-season streamflow), NSEq and NSElnq give more weight to high- and low-flow values, respectively. The constraint of a less than 10% bias on cumulated streamflow over each sub-period was applied to the calibrations. Each model Mj calibrated over the hydrological year j (1 < j < 13) was run in simulation mode over the whole study period, April 1993–March 2006, to produce a cross-simulation matrix (Figure 2). This process was performed twice to produce two matrices: one matrix of wet season streamflow (cumulated from May to October)
simulated with models optimized with NSEq, and one matrix of dry season streamflow (cumulated from November to April) simulated with models optimized with NSElnq.

<figure><img src="image_134.png" /><figcaption>**Figure 2.** Cross-simulation matrix. $M_j$ ($j \in [1 - n]$) defines the set of model parameters calibrated over the hydrological year j. $P_i$ ($i \in [1 - n]$) defines the rainfall that occurred over the hydrological year i. Streamflow $q_{ij}$ is simulated by model $M_j$ with rainfall $P_i$.</figcaption></figure>

Streamflow $q_{ij}$ is simulated with the model $M_j$ capturing the watershed behaviour of hydrological year j, using rainfall from the hydrological year i ($i \in [1 - n]$). In a given row i, streamflow variations between columns are due to non-climatic changes (e.g., land-cover changes). In a given column j, streamflow variations between rows reflect inter-annual rainfall variability under stable watershed conditions. Variations in simulated streamflow between the columns of the matrices were plotted against time to illustrate temporal changes in the hydrological behaviour of the watershed. In these simulations, the monthly rainfall input to the model is used in loop each year and corresponds to the rainfall of the hydrological year April 1996–March 1997, exhibiting median annual depth over the studied period. Similar modeling framework was successfully performed in a previous study [25].

To assess the statistical significance of changes in the rainfall–runoff relationship over any multi-year sub-period, the non-parametric test proposed by [39] was applied to the corresponding cross-simulation matrix. Following the prescribed methodology, the cross-simulations matrices (one for wet season streamflow and one for dry season streamflow) were re-sampled by permuting columns. The statistic S was calculated for the original matrix and for each re-sampled one using Equation (1):

$$S = \sum_{i=1}^{n} \left[ \sum_{j=1}^{i-1} (q_{ii} - q_{ij}) + \sum_{j=i+1}^{n} (q_{ij} - q_{ii}) \right]. \quad (1)$$

Under the null hypothesis $H_0$ of an absence of unidirectional trend in the hydrological behavior of the watershed over the studied period, the value of S associated to the original matrix should be close to zero. A negative (resp., positive) value denotes a decreasing (resp., increasing) trend in the basin water yield. The p-value of a negative (resp., positive) trend is assessed by calculating the non-exceedance (resp., exceedance) frequency of the original S values compared to the range of S values derived from the permuted matrices.

## 3. Results

### 3.1. Land-Cover Change

The land-cover classification was found to be reliable, with an overall accuracy of 89.1% and a Kappa coefficient of 0.85. Figure 3 shows spatial and temporal changes in the spatial distribution of land-cover types between 1992 and 2010. Temporal changes in the cumulative areal percentages of each land-cover type are displayed in Figure 4. Rubber plantations appeared first in the downstream part of
the watershed nearby the six main villages and gradually spread upstream, replacing farmland and forest. From 1992 to 2010, the areal percentage of rubber tree plantations in the watershed increased from 10% to 44%, while forest area declined from 47% to 21% and farmland declined from 26% to 12%. Changes in grassland area (from 16% to 21%) and settlement area (from 0.7% to 1.2%) were moderate.

<figure><img src="image_135.png" /><figcaption>**Figure 3.** Land-cover change in the Nan'a watershed, based on Landsat (years 1992, 2002, and 2007) and RapidEye (year 2010) imagery [33,34].</figcaption></figure>

<figure><img src="image_136.png" /><figcaption>**Figure 4.** Areal percentages of each land-cover type in the Nan'a watershed.</figcaption></figure>

Figure 5 quantifies land-cover transitions. From 1992 to 2002, the forested area declined drastically from 4517 ha to 2132 ha. About 42%, 30%, and 28% of the deforested area was converted to farmland, rubber plantations, and grassland, respectively. While this land conversion increased the farmland area by 1000 ha of fertile land, another 880 ha of more degraded farmland were planted with rubber trees as a strategy to maintain the soil productivity. Over the same period, the grassland areas gained 665 ha from deforestation and lost 240 ha allocated to farmland. Land-cover dynamics were different from 2002 to 2007. Rubber tree plantations continued expanding at the expense of other areas, primarily farmland, and secondarily forest. About 300 ha of farmland reverted to grassland as a result of a land-use policy targeting slopping land for vegetation regeneration [40]. Over the whole period 1992–2007, rubber tree plantations and forest constantly expanded and declined, respectively.
<figure><img src="image_137.png" /><figcaption>**Figure 5.** Land-cover conversions in the Nan'a watershed. Numbers refer to areas in hectares. "+" = gain. "-" = loss. Histograms quantify the areas in the first year (left bar) and last year (right bar) of each period.</figcaption></figure>

Figure 6 shows the temporal changes in the spatial distribution of rubber tree plantations in the watershed, according to elevation and slope. From 1992 to 2007, rubber plantations first occupied the most suitable and convenient land near the villages and roads in the lower part of the watershed, on gentle slopes and flat land. As the plantations continued expanding, the most suitable land became rarer and new rubber trees were planted on higher and steeper land. Between 1992 and 2007, the average elevation of rubber tree plantations increased from 657 m to 740 m (Figure 6a). Over the same period, the mean slope of rubber tree plantations increased from 12 to 17 degrees (Figure 6b).

<figure><img src="image_138.png" /><figcaption>**Figure 6.** Distribution of rubber trees according to elevation (a) and slope (b) in the Nan'a watershed. Boxes: 40–60% quantiles. Whiskers: 20–80% quantiles. Black dots: averages.</figcaption></figure>
## 3.2. Hydrological Change

Figure 7 shows the temporal variation in annual rainfall, streamflow, and the runoff coefficient in the Nan'a watershed from 1993 to 2005. Inter-annual variability of rainfall exhibits 3- to 6-year cycles of gradual increase, with maximums in 1995 (1893 mm) and 2001 (2114 mm, the wettest year), and minimums in 1993 (1250 mm), 1997 (1285 mm), and 2003 (1063 mm, the driest year). While annual streamflow correlated with annual rainfall ($r^2$ = 0.85, *p* = 0.00) (Figure 8), no correlation ($r^2$ = 0.00) was observed between annual rainfall and annual runoff coefficient, yielding an inter-annual mean of 0.49.

<figure><img src="image_139.png" /><figcaption>Figure 7. Annual rainfall, streamflow, and runoff coefficient in the Nan'a watershed.</figcaption></figure>

<figure><img src="image_140.png" /><figcaption>Figure 8. Relationship between annual rainfall and annual streamflow in the Nan'a watershed. Slope of the regression line is equivalent to the inter-annual mean of the annual runoff coefficient.</figcaption></figure>

The annual values of NSEQ and NSEInQ averaged over the whole study period are high: 88.8% and 90.2%, respectively. The lowest values were obtained for hydrological years 2004 (NSEInQ = 81.7%) and 1994 (NSEQ = 80.9%). All these values exceed 0.75, confirming that the performance of GR2M is very good in the studied watershed [41].

Figure 9 depicts the temporal variation in streamflow, simulated under stable rainfall conditions. Unlike Figure 7, where streamflow variation is mainly caused by inter-annual rainfall variability, the variation shown in Figure 9 is caused by non-climatic factors, such as land-cover changes. Wet season
and dry season streamflow follow the same patterns, with a reduction from 1993 to 2002 (–196 mm and –65 mm, respectively) followed by an increase from 2002 to 2005 (+80 mm and +44 mm, respectively). The cross-simulation test [39] applied to seasonal streamflow over the whole study period revealed no significant hydrological change at the 90% confidence level. However, a significant decline was observed from 1993 to 2002, with p-values of 0.09 and 0.01 for wet season and dry season streamflow respectively. The same test applied to any other sub-periods yielded higher (i.e., less significant) p-values except for the period 1993–1998, which exhibits the most significant trend of decline (p-values of 0.06 and 0.03 for wet season and dry season streamflow, respectively). In contrast, no significant change was observed from 2002 to 2005.

<figure><img src="image_141.png" /><figcaption>**Figure 9.** Wet season and dry season streamflow simulated with GR2M calibrated over each hydrological year (indicated on x-axis) and run with the same rainfall input (hydrological year April 1996–March 1997).</figcaption></figure>

# 4. Discussion

## 4.1. Relationships between Hydrological Changes and Land-Cover Changes

Between 1993 and 2002, the basin water yield significantly decreased by 30%. The wet season and dry season streamflow simulated under stable rainfall conditions reduced by 196 mm and 65 mm, respectively (Figure 9). Over the same period, the areal percentage of the rubber tree plantations increased by 1626 ha—equivalent to 17.1% of the watershed area—at the expense of forest and farmland, mainly in the lower part of the watershed (Figures 3–6). As in the rest of the region [42], the resulting loss of food-productive land was compensated by converting forest to farmland at elevations higher than 800 m, where rubber trees are less productive and more prone to frost damage. The 30% streamflow reduction likely reflects an increase in ET, caused by greater root water abstraction in relation to the replacement of natural forest and farmland by rubber trees [22]. Indeed, compared to natural forest and farmland, due to a denser and deeper root system, rubber trees have the ability to absorb a larger amount of water in the soil [15,18,23], ultimately reducing watershed streamflow [14]. The transpiration of rubber trees is greater in the wet season than in the dry season [43]. However, the decreased dry season streamflow quantified in the present study could be caused by the ability of rubber trees to shift from the shallow to deep soil layer to extract water where it is the most abundant [18,22,23]. The lower part of the watershed, where rubber trees were planted between 1992 and 2002, is where the water table is likely the shallowest. This increase in ET may have been partially compensated by the concurrent conversion of forest to farmland in the upper part of the basin. This conversion to a sparser vegetation cover, limiting root water uptake, likely increased streamflow
production. However, this counteracting change could not fully compensate the hydrological effect of
rubber expansion for the following two reasons: (1) Forest conversion to farmland occurred at a higher
elevation in the watershed (Figure 3) where the water table is deeper, thereby limiting root water
uptake. This means that any change in the vegetation water demand for evapotranspiration would
have a smaller effect on the local water balance than the land-cover changes happening in the lower
part of the basin. (2) The conversion of forest to farmland involved an area half that of the conversion
of forest to rubber tree plantations (Figure 5).

Between 2002 and 2006, the simulated wet and dry season streamflow increased by 80 mm and 44 mm, respectively (Figure 9). These changes were found to be statistically insignificant at the 90% confidence level. Over the same period, the area of rubber tree plantations increased by 716 ha (about 7.5% of the watershed area) at the expense of farmland and forest (Figure 5). On average, these new rubber trees were planted at elevations about 83 m higher and on slopes steeper (by about 5 degrees) than existing rubber trees. Concurrently, about 300 ha of farmland were converted to grassland. These two types of land conversion correspond to a theoretical increase in ET, resulting from a land conversion to a denser vegetation cover with a deeper root system. However, both wet and dry season streamflow simulated under stable rainfall conditions increased during this period, although insignificantly. This hydrological change is likely to happen when soil permeability is reduced, despite a greater evapotranspiration water demand. This situation typically happens on steep slopes exposed to the erosive power of incident rainfall [44]. This is the case where soils under the canopy of tree plantations are cleared and not protected from throughfall by any understory [25,45]. In this environment, enhanced throughfall accelerates soil surface crusting, soil erosion, and surface runoff. Greater rates for these processes were observed under rubber trees planted on steeper slopes in an experimental site nearby the study area [45]. Similarly, a lower soil infiltration capacity is observed where grassland forms on eroded soils [46], as observed in the study area. Although terracing practiced on steep hillslopes under rubber plantations aims to promote runoff infiltration, in some instances such interventions led to soil compaction, reducing soil permeability and ultimately leading to increased runoff [16]. Further field investigations in the studied watershed are required to verify these hypotheses.

## 4.2. Limitations of the Approach and Recommendations

This study relied on two types of data: continuous hydro-meteorological records collected from one river water level gauging station and two rain gauges, and four land-cover maps at different dates, partly overlapping the period of streamflow records. While plausible explanations could be proposed to explain the succession of two opposite hydrological changes, they could not be ascertained for several reasons. The first one is intrinsically linked to the nature of observed land-cover changes and to their variations according to the watershed geomorphology. The attribution of hydrological trends was complicated by concurrent counteracting changes in vegetation cover. In addition, successive land-cover changes occurred in different parts of the watershed, where surface-groundwater interactions were likely influenced by variations in slope, elevation, and other soil and geological features not reported in this study. The effects of this heterogeneity on the watershed hydrology could not be captured by the limited available data, which provided snapshots of land-cover patterns for a few years only. However, the figures and literature review suggest a continuous increase and decrease in areas of rubber tree plantations and forest, respectively [1-3], with a gradual shift of the land-cover changes toward areas at greater elevations and with steeper slopes [30], supporting our interpretations.

In this study, hydrological modeling was performed at a monthly time step. It could be argued that a daily time step would have provided more precise results. As shown in previous modeling studies performed with a conceptual-lumped rainfall-runoff model in the Mekong Basin [25,47,48], GR2M has several advantages compared to daily models. Aggregating daily rainfall and streamflow in monthly steps avoids the risk of shifting errors between daily rainfall and streamflow time series. This temporal aggregation results in Nash coefficients of GR2M calibrations (>80%) greater than those
obtained with models working at a daily time step [47]. This high performance enables GR2M to
capture inter-annual variations of the catchment behavior for comparison with long-term changes in
land cover, which was the main aim of this study and others [25,47,48].

Future investigations of the hydrological impact of rubber tree plantations at watershed scales should pay particular attention to their effect on streamflow during the dry season. Indeed, when the monsoon recedes, streamflow often becomes the only available water resource that sustains biodiversity and enhances ecosystem services, such as water supply for domestic uses. Rubber plantations may lead to either a reduction or an increase in dry season streamflow, depending on the previous land cover, and the effects of the land conversion on soil permeability and root water uptake. If the gain in infiltration surpasses the increase in ET, groundwater recharge and the related base-flow will increase [49,50]. The effectiveness of rubber plantation in promoting water infiltration will vary according to the management of its understory. Enhanced levels of soil organic matter from litter inputs improve soil structure, enhance aggregate stability, and promote faunal activity, leading to higher macro porosity, thereby creating preferential pathways for infiltration [51]. Permitting understory vegetation to grow increases litter input and reduces over-land flow rates. Roots also create preferential pathways for water infiltration. In contrast, a systematic clearing of vegetation under the tree canopy reduces litter input and increases soil exposure to rainfall intensity, promoting soil crusting that will, in turn, reduce infiltration and increase runoff during the rainy season [45], ultimately leading to a reduction of dry season streamflow [25]. Moreover, increased runoff accelerates soil detachment, erosion, and soil organic carbon depletion, which compromises crop yield over the long term [52]. Another factor influencing the infiltration trade-off is tree density. At a low to intermediate tree cover density, each new tree can improve soil hydraulic properties beyond its canopy edge, which means that the infiltration gains can be proportionally higher than the additional losses from increased transpiration and interception [53]. This process is upper bounded by a tree density, over which any additional tree will further increase ET, while infiltration has already reached a maximum [54]. Optimal tree density is therefore not only a question of yield maximization, but also an ecological consideration.

To improve infiltration under the canopy of monoculture rubber tree plantations, intercropping
with understory species is another solution with potential economic return [55]. Before the fourth
year of the rubber plantation, young trees are intercropped with fruit species (e.g., pineapples,
mangosteen, mangos, and durian), which contribute to maintaining soil organic matter, faunal activity,
and permeability. After the closure of the rubber tree canopy, intercrops are replaced by shade-tolerant
varieties, such as tea, coffee, cacao, or slow-growing timber species [56,57]. Over time, timber-based
systems could become highly diverse and are analogous to advanced secondary forests [58].

Future research should pay particular attention to the trade-off between infiltration and ET, and its relationship to plantation management. Proper measurement devices should be put in place accordingly. They should include microplots to survey soil surface structures and permeability [59], piezometers, river water level gauging stations, and rain gauges. Most importantly, the size of the gauged watershed should be small enough to capture homogeneous land-cover changes. Ideally, it should be positioned downstream of a small headwater watershed likely to experience homogenous land conversion to rubber tree plantation.

**5. Conclusions**

The capacity of rubber trees to deplete groundwater resources was previously evidenced locally. But the regional hydrological effects of the rapid expansion of rubber tree plantations across southern montane China have not been quantified. In an attempt to address this shortfall, our investigations focused on a 100 km² gauged watershed where rubber tree plantations increased from 10% to 44% between 1992 and 2010. While it was possible to isolate hydrological changes from those caused by rainfall variability, their attribution to land-cover changes, and specifically to rubber tree plantations, was compounded by the heterogeneity of land-cover transitions occurring in the watershed, and their variations across a contrasted geomorphology. The processes that explain the lack of unequivocal
linkage between tree rubber planting and watershed hydrological change were reviewed, and we formulated practical recommendations in terms of monitoring devices and data collection to improve the strength of trend attribution in future studies. While the spread of rubber tree plantations is likely to continue in the humid tropics—although at a pace moderated by market fluctuations—it is important that the largest number of tree growers adopt sustainable practices. Such techniques should aim at maximizing rainwater infiltration on planted hillslopes, while maintaining biodiversity and diversifying crop productions. Related ecosystem services include the provision of dry season water resources in the form of stream base-flow. Promoting intercropping in rubber tree plantations appears to be a suitable technique to reach these objectives.

**Author Contributions:** Conceptualization, X.M. and G.L.; methodology, X.M. and G.L.; validation, X.M., G.L. and R.H.; formal analysis, X.M.; investigation, X.M.; resources, X.M.; data curation, X.M. and G.L.; writing—original draft preparation, G.L. and X.M.; writing—review and editing, G.L., R.H., M.v.N., and X.M.; visualization, G.L. and X.M.; supervision, G.L., J.X., M.v.N. and R.H.; project administration, R.H.; funding acquisition, R.H and J.X.

**Funding:** This research was funded by the CGIAR research program on Integrated Systems for the Humid Tropics, the ICRAF Post-doctoral Program (2012–2014) and the Bundesministerium für Wirtschaftliche Zusammenarbeit und Entwicklung/Deutsche Gesellschaft für Internationale Zusammenarbeit research program that supported the Green Rubber project No. 13.1432.7-001.00.

**Acknowledgments:** We are grateful to the Xishuangbanna Water Resources and Hydrological Bureau for the provision of Hydro-Meteorological data.

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

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<figure><img src="image_142.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_143.png" /></figure>
Article

# Recent Trend in Hydroclimatic Conditions in the Senegal River Basin

Ansoumana Bodian<fnref n="1" />,*, Lamine Diop<fnref n="2" />, Geremy Panthou<fnref n="3" />, Honoré Dacosta<fnref n="4" />, Abdoulaye Deme<fnref n="5" />, Alain Dezetter<fnref n="6" />, Pape Malick Ndiaye<fnref n="1" />, Ibrahima Diouf<fnref n="7" /> and Théo Vischel<fnref n="3" />

Received: 21 December 2019; Accepted: 20 January 2020; Published: 6 February 2020

**Abstract:** Analyzing trends of annual rainfall and assessing the impacts of these trends on the hydrological regime are crucial in the context of climate change and increasing water use. This research investigates the recent trend of hydroclimatic variables in the Senegal River basin based on 36 rain gauge stations and three hydrometric stations not influenced by hydraulic structures. The Man Kendall and Pettitt's tests were applied for the annual rainfall time series from 1940 to 2013 to detect the shift and the general trend of the annual rainfall. In addition, trends of average annual flow rate (AAFR), maximum daily flow (MADF), and low flow rate (LFR) were evaluated before and after annual rainfall shift. The results show that the first shift is situated on average at 1969 whereas the second one is at 1994. While the first shift is very consistent between stations (between 1966 and 1972), there is a significant dispersion of the second change-point between 1984 and 2002. After the second shift (1994), an increase of annual rainfall is noticed compared to the previous period (1969–1994) which indicates a not significant, partial rainfall recovery at the basin level. The relative changes of hydrologic variables differ based on the variables and the sub-basin. Relative changes before and after first change-point are significantly negative for all variables. The highest relative changes are observed for the AAFR. Considering the periods before and second shifts, the relative changes are mainly significantly positive except for the LFR.

**Keywords:** trends; Senegal River Basin; rainfall shift; hydroclimatic variables; streamflow; climate change

## 1. Introduction

Rainfall is a major factor that conditions food production, is mainly derived from rain-fed agriculture, and plays a central role in the availability of surface and groundwater water resources [1].

<footnote n="1">¹ Laboratoire Leïdi "Dynamique des territoires et développement", Université Gaston Berger (UGB), BP 234-Saint-Louis, Senegal; ndiaye.papa-malick@ugb.edu.sn</footnote>

<footnote n="2">² UFR S2ATA Unité de Formation et de Recherche des "Sciences Agronomiques de l'Aquaculture et des Technologies Alimentaires", Université Gaston Berger (UGB), BP 234-Saint-Louis, Senegal; lamine.diop@ugb.edu.sn</footnote>

<footnote n="3">³ IGE Institut des Géosciences de l'Environnement, Université Grenoble Alpes, 38000 Grenoble, France; geremy.panthou@univ-grenoble-alpes.fr (G.P.); theo.vischel@univ-grenoble-alpes.fr (T.V.)</footnote>

<footnote n="4">⁴ Département de Géographie, Université Cheikh Anta Diop, BP 5005 Dakar, Senegal; dacosta.honore@gmail.com</footnote>

<footnote n="5">5 Laboratoire LSAO "Laboratoire des Sciences de l'Atmosphère et de l'Océan", Université Gaston Berger (UGB), BP 234-Saint-Louis, Senegal; abdoulaye.deme@ugb.edu.sn</footnote>

<footnote n="6">⁶ HydroSciences Montpellier, Univ Montpellier, IRD, CNRS, CC 057, 163 rue Auguste Broussonnet, 34090 Montpellier, France; Alain.Dezetter@ird.fr</footnote>

<footnote n="7">⁷ NOAA Center for Weather and Climate Prediction, 5830 University Research Court, College Park, MD 20740, USA; ibrahima.diouf@noaa.gov</footnote>

<footnote>* Correspondence: bodianansoumana@gmail.com or ansoumana.bodian@ugb.edu.sn; Tel.: +221-77-811-7553</footnote>
Therefore, several studies have focused on how climate variability affects rainfall regime in West Africa over the last century [2,3]. A common feature of these studies is the identification of a succession of drought episodes [4]. Since the beginning of observations in 1854, three main dry periods have been recognized [2]: 1911–1913, 1940–1943 and 1968 to recent decades. Due to its duration and intensity, the latter has been recognized as the greatest drought of the last century [5]. The significant decline in annual rainfall generally led to the depletion of water resources [6,7], the amplification of water deficits [8] and the modification of natural ecosystems and socio-economic systems [4]. It had particularly negative effects on human activities and environment. The drought and its hydrological impacts have been well investigated until the 1990s [3,9–19]. However, the number of studies on hydroclimatic evolution in West Africa over the last three decades has decreased significantly because of the decline of in-situ observation networks and the difficulty for academic structures to access data from national meteorological and/or hydrological services.

Recently, some authors [18,20,21] have questioned whether a few years of excess rainfall of the 1990s could be the sign of a gradual recovery of rainfall leading to more favorable hydroclimatic conditions. Similarly, Diello [22] showed that the area affected by drought has shrunk since 1994 in the Sahelian area. However, other studies have suggested that severe drought conditions were persisting in the Sahel at the end of the 1990s [12,23]. Thus, the question of the persistence or end of drought was discussed in the scientific community in early 2000. Vischel et al. [24] investigated the scientific literature on recent rainfall trends in West Africa. They pointed out that after the severe drought in the Sahel in the late 1960s, the last decade of the 20th century saw a partial recovery of rainfall, particularly in the central part of the Sahel, but without returning to the wet conditions of the 1950s and 1960s. In addition, recent studies have shown that the relative increase in rainfall accumulation was mainly linked to an increase in the intensity of rainfall events, while the occurrence of rainfall events remains at an average level close to that of periods of extreme drought [25,26]. Therefore, the terms “recovery” or “return to wet conditions” often used to characterize rainfall over the past two decades has to be considered with caution.

In this respect, Descroix et al. [27] argued that the end dates of the drought of the 1970s may also vary region to another. Lebel and Ali [16] then Panthou et al. [25] have shown that there are contrasts between the Western Sahel which seems to start a later recovery in annual precipitation than the central Sahel. Nkrumah et al. [26] showed that contrast also exists between sub-regions with in the Southern West Africa with a more marked increase in coastal than inland regions. It is also what Diop et al. [28] have shown in a recent study in Senegal where rainfall recovery in recent decades is significant in the coastal strip of Senegal. All these recent studies push for further analyze recent rainfall trends in West Africa at the sub-regional scale. While climatic zones or country-based approaches dominate the literature, there is a lack of analyses at the scale of regional basins in the West African region. The basin scale is a key scale, not only for analyzing climatic regional contrasts, but also to understand their impacts on water resources. In the context of climate and global changes and increasing water demand, it is a major challenge to jointly analyze rainfall and hydrological changes to improve water resource management [29].

## 2. Materials and Methods

### 2.1. Study Area

The Senegal River basin extends from the Sahara in the North to the humid tropical region of Guinea in the South [30]. It drains an area of 300,000 km² [31] with four riparian states, from upstream to downstream, Guinea, Mali, Senegal and Mauritania (Figure 1). The Senegal River basin has experienced climate change effects since the 1970s [31]. To remedy the effects of adverse climatic conditions, the Senegal River Basin Development Organization (in French, Organisation pour la Mise en Valeur du Fleuve Sénégal, OMVS) built three dams (Figure 1) in 1986 (Diama), 1988 (Manantali) and 2013 (Felou). The main functions of the Diama dam are to improve irrigation in the Senegal River
valley and delta and to facilitate water supply. Manantali is a multi-usage dam with a storage capacity of 11 billion m³ of water, an energy production of 800 GWh/year and an irrigation capacity of 255,000 ha. Felou is a run-of-river dam with a production capacity of 350 GWh/year. These various hydraulic structures have artificialized the Senegal River regime in some places. From the climate stand point, rainfall in the basin is related to the displacement of the Intertropical Convergence Zone (ITCZ) from the south to north, inducing the penetration of the West African monsoon governed by the thermal contrast between the sea and the continent [32]. Based on the latitudinal distribution of precipitation, four climatic zones have been defined in the Senegal River basin by Dione [30]: Guinea (mean annual precipitation, P > 1500 mm); Southern Sudan (1000 < P < 1500 mm); Northern Sudan (500 < P < 1000 mm); and the Sahel (P < 500 mm).

<figure><img src="image_144.png" /><figcaption>**Figure 1.** Senegal River Basin, hydrographic network, river flow stations, rainfall gauges, and main hydraulic infrastructures.</figcaption></figure>

## 2.2. Data

In this study, in-situ annual rainfall and daily observed stream-flows data are used.

### 2.2.1. Annual Rainfall data, Selected Gauges and Periods of Study

The rainfall data used in this study come from the OMVS database that contains 80 rainfall stations (Figure 1) covering the period 1940 to 2013. For each station, the percentage of gaps in the data series has been calculated. Only stations with less than 50% of missing years were first selected, leading to retain 48 stations. Then, two criteria were used to select the final stations for the study: (i) the coefficient of determination (Figure 2a) between the observed annual rainfall and the calculated one by the Regional Vector Method [33–35] and (ii) stations with no missing values (Figure 2b). Thus, for this study, stations (36) with zero gaps or a coefficient of determination of 0.5, which is an acceptable value in linear regression [36], were selected (Figure 2c). Figure 3 shows the available annual rainfall data over the period 1940–2013. For the stations with gaps, the regional vector method was used to fill the gaps. The regional vector method has already been evaluated by Bodian [37] and has shown to be robust enough to fill missing values in annual rainfall series.
<figure><img src="image_145.png" /><figcaption>**Figure 2.** (a) Coefficient of determination between the observed annual rainfall and calculated by the regional vector method, (b) percentage of gap in the data series by station, (c) stations selected for the study.</figcaption></figure>

<figure><img src="image_146.png" /><figcaption>**Figure 3.** Available annual rainfall data over the period 1940–2013. (a) missing values by station; (b) number of available gauges data per year.</figcaption></figure>

### 2.2.2. Hydrological Data

For this study, three streamflow stations not influenced by hydraulic structures were chosen (Figure 1). Table 1 provides the available hydrometric data from the OMVS database.
<table><caption>Table 1. Available discharge data.</caption><thead><tr><th>River Basin</th><th>Stations</th><th>Surface (km²)</th><th>Start of Record (dd/mm/yyyy)</th><th>End of Record (dd/mm/yyyy)</th><th>% Gaps</th></tr></thead><tbody><tr><td>Bafing</td><td>Dakka Saidou</td><td>15660</td><td>27/05/1952</td><td>21/04/2016</td><td>1.02</td></tr><tr><td>Faleme</td><td>Gourbassi</td><td>16264</td><td>02/01/1954</td><td>24/03/2016</td><td>0.17</td></tr><tr><td>Bakoye</td><td>Oualia</td><td>104479</td><td>01/06/1954</td><td>24/03/2016</td><td>1.90</td></tr></tbody></table>

## 2.3. Methods

### 2.3.1. Hydroclimatical Variables Trend Analysis

Two methods, namely the Mann-Kendall and Pettitt's tests, were used to detect the annual rainfall trends and shift over the entire Senegal River basin. The Mann-Kendall test [38,39] is a non-parametric test often used to detect the presence of a monotonic trend in a chronological series. The Pettitt's test [40] was used to detect the change-point in the considered series. It is based on the Mann-Whitney two-sample test and allows the detection of a single shift at an unknown time. By combining the Mann Kendall and Pettitt tests, a several-steps approach was applied. First, the Mann Kendall and Pettitt's tests were applied for the annual rainfall series from 1940 to 2013 to detect the shift and the general trend of the annual rainfall. Once a change-point was detected, a new annual rainfall series was considered starting after the change point. For this new series, we applied the same tests as previously. Furthermore, trends of average annual flow rate (AAFR), maximum daily flow (MADF), and low flow rate (LFR) were evaluated before and after annual rainfall shifts.

### 2.3.2. Standardized Precipitation Index (SPI)

The SPI [9,41] is a widely used index to characterize meteorological drought. It reflects a rainfall surplus or deficit for the year compared to a whole period under investigation. A positive value of SPI indicates rainfall surplus whereas a negative value shows rainfall deficit.

## 3. Results

### 3.1. Temporal Variability of Annual Rainfall

Figure 4 presents the temporal variability of the annual rainfall in the Senegal River basin. The results show that the stations exhibited different shifts. However, two regime shifts statistically significant at the 5% level are exhibited. The first change-point is situated on average at 1969 whereas the second one is at 1994 (Figure 4). While the first shift is very consistent between stations (comprised between 1966 and 1972), there is a significant dispersion of the second rupture between 1984 and 2002. The two identified shifts lead to distinguish three different periods as depicted by the SPI (Figure 5). From 1940 to 1968, the mean SPI is positive and reveals annual rainfall well above the interannual mean characterizing a very wet period. By contrast, the inter-shift period displays very negative values of SPI showing that the Senegal River basin clearly witnessed the well-documented Sahelian great drought. The dispersion of the break date shows that the identification of an end date for the drought period is much less consistent with some stations remaining very dry while others seem to be starting to recover more quickly.
<figure><img src="image_147.png" /><figcaption>**Figure 4.** Breaks identified in the annual rainfall series by the Pettitt test at 5% level. The blue dots represent the year of occurrence of the first shift and the orange dots the year of occurrence of the second shift.</figcaption></figure>

<figure><img src="image_148.png" /><figcaption>**Figure 5.** Variation of SPI over the period 1940–2013.</figcaption></figure>

## 3.2. Annual Rainfall Trend

The trend in annual rainfall is investigated from 1940 to 2013 for both the whole annual rainfall series and the periods after the first shift to 2013. For the whole period (1940–2013), Figure 6a shows that the spatial pattern is dominated by a downward trend except Bakel which exhibits an upward trend but not significant at a 5% significance level. The case of Bakel was mentioned by previous studies [28,37]. However, the period after the first shift to 2013 (Figure 6b) shows a significant positive linear trend (0.05) at the majority of the stations in the Senegal River basin. There are few stations mainly at the upper part of the river basin which exhibit a non-significant positive trend. These results indicate that the Sahelian part of the river basin exhibits a significant upward trend of annual rainfall, whereas the Soudanian one shows an upward trend but is not significant.
<figure><img src="image_149.png" /><figcaption>**Figure 6.** Spatial distribution of the statistics Z of the Mann–Kendall test for the annual rainfall (a) period 1940–2013; (b) period after shift 1 to 2013.</figcaption></figure>

### 3.3. Evolution of Hydrological Variables

An analysis was performed in order to evaluate the variability of the hydrologic variables across the three sub-basins of the area: Gourbassi, Oualia and Dakka Saidou for the period from 1940 to 2013 (Table 2 and Figure 7). Both rainfall and hydrologic variables: AAFR, MADF, LFR seem to have similar trends. For all basins, the period before the first break was the wettest with average rainfall varying from 877 mm (Oualia) up to 1239 mm (Gourbassi). Table 2 presents the relative change computed before and after breaks for all variables and for each sub-basin. The table shows that the relative changes differ based on the variables and the sub-basin. Relative changes before (1940–1969) and after first break (1970–2013) are significantly negative (0.05) for all variables. The highest relative changes are observed for the AAFR reaching –54% in Gourbassi sub-basin. When considering the periods before 1970–1994 and second shifts (1995–2013), the relative changes are mainly significantly positive (0.05) except for the LFR. These results are further supported by the tabulated SPI presented above.

<table><caption>**Table 2.** Mean, standard deviation and changes in hydroclimatic variables.</caption><thead><tr><th rowspan="3">Basin</th><th rowspan="3">Parameters</th><th colspan="2">Long Term Period</th><th colspan="2">Period 1</th><th colspan="2">Period 2</th><th colspan="2">Period 3</th><th colspan="2">Change</th></tr><tr><th colspan="2">1940–2013</th><th colspan="2">1940–1969</th><th colspan="2">1970–1994</th><th colspan="2">1995–2013</th><th rowspan="2">Break 1</th><th rowspan="2">Break 2</th></tr><tr><th>X</th><th>σ</th><th>X</th><th>σ</th><th>X</th><th>σ</th><th>X</th><th>σ</th><th>Δ1</th><th>Δ2</th></tr></thead><tbody><tr><td rowspan="4">Gourbassi</td><td>Rainfall</td><td>1125</td><td>171</td><td>1239</td><td>97</td><td>990</td><td>89</td><td>1111</td><td>86</td><td>-0.15 *</td><td>0.12</td></tr><tr><td>AAFR</td><td>101</td><td>52</td><td>168</td><td>38</td><td>67</td><td>33</td><td>92</td><td>29</td><td>-0.54 *</td><td>0.37 *</td></tr><tr><td>MADF</td><td>922</td><td>429</td><td>1359</td><td>356</td><td>682</td><td>358</td><td>899</td><td>318</td><td>-0.43 *</td><td>0.32 *</td></tr><tr><td>LFR</td><td>26</td><td>11</td><td>31</td><td>8</td><td>20</td><td>9</td><td>31</td><td>13</td><td>-0.18 *</td><td>0.58</td></tr><tr><td rowspan="4">Oualia</td><td>Rainfall</td><td>822</td><td>117</td><td>887</td><td>147</td><td>715</td><td>127</td><td>855</td><td>120</td><td>-0.12 *</td><td>0.20 *</td></tr><tr><td>AAFR</td><td>117</td><td>67</td><td>202</td><td>39</td><td>69</td><td>40</td><td>99</td><td>42</td><td>-0.58 *</td><td>0.44 *</td></tr><tr><td>MADF</td><td>1041</td><td>605</td><td>1578</td><td>408</td><td>666</td><td>429</td><td>1066</td><td>614</td><td>-0.46 *</td><td>0.60 *</td></tr><tr><td>LFR</td><td>39</td><td>18</td><td>62</td><td>12</td><td>31</td><td>10</td><td>29</td><td>11</td><td>-0.51 *</td><td>-0.04</td></tr><tr><td rowspan="4">DakKa Saidou</td><td>Rainfall</td><td>1472</td><td>198</td><td>1629</td><td>164</td><td>1282</td><td>125</td><td>1461</td><td>123</td><td>-0.16 *</td><td>0.14 *</td></tr><tr><td>AAFR</td><td>228</td><td>64</td><td>309</td><td>53</td><td>180</td><td>34</td><td>223</td><td>26</td><td>-0.35 *</td><td>0.24 *</td></tr><tr><td>MADF</td><td>1328</td><td>472</td><td>1796</td><td>540</td><td>1053</td><td>228</td><td>1311</td><td>376</td><td>-0.35 *</td><td>0.24 *</td></tr><tr><td>LFR</td><td>40</td><td>11</td><td>48</td><td>18</td><td>38</td><td>6</td><td>36</td><td>5</td><td>-0.23 *</td><td>-0.05 *</td></tr></tbody></table>

LFR, low flow rate (m³/s); MADF, maximum daily flow (m³/s); AAFR, average annual flow rate (m³/s); Δ1, relative change between period before and after shift 1 (%); Δ2, relative change between period before and after shift 2 (%). * Significant changes at the 5% threshold.
<figure><img src="image_150.png" /><figcaption>Figure 7. Relationship between SPIs and hydrological variables.</figcaption></figure>

**4. Discussion**

The results of this paper show two main shifts on annual rainfall in 1969 and 1994 which corroborates the results of Nicholson et al. [19] who showed that a major change in the rainfall regime occurred in both the Sahel and Guinea Coast around 1968/1969. The annual rainfall exhibited a decreasing trend if we considered the whole series. This decreasing trend of the annual rainfall has the same consequence on the hydroclimatic variables: AAFR, MADF, LFR. This is in accordance with Olivry [42] who found a net relationship between annual rainfall and hydroclimatic variables in terms of trend. In addition, Diop et al. [43] by investigating the annual streamflow in the upper Senegal River basin found a significant decreasing trend from 1961 to 2014.

The first shift (1969) marks the starting point of the drought as indicated by the values of the SPI. Our results suggest an onset of the drought similar to that found by Nicholson et al. [19]. After the second shift (1994), there is an increase of annual rainfall compared to the previous period (1969–1994) which indicated a partial rainfall recovery not significant at a basin level.

Hence, the paper confirms the findings suggesting a return of wet period [18,28,44,45]. This leads to an improvement in water availability. The relative changes between period before and after second
shift range from 0.24 (Dakka Saidou) to 0.60 (Oualia) for MADFs, from 0.24 (Dakka Saidou) to 0.44 (Oualia) for AAFRs and from 0.58 (Gourbassi) to -0.04 (Oualia and Dakka Saidou). With the exception of the low water flows of Oualia and Dakka Saidou, the various hydrological variables analysed show an uptrend. Flows were sustained for a few years at the beginning of the drought by emptying natural reservoirs; it now takes a few years to restore them. However, the relative change of 0.58 for the Gourbassi low water flow does not seem realistic and may be implied by associated uncertainty with the calculation of low water flows. Indeed, the calculation of low water flows requires the separation of flows. This separation of flows was carried out based on the decrease in the annual hydrograph, which corresponds to a regular decrease in flows or drying phase. After rapid flows transfer, the part of the curve with the steep slope corresponds to the recession and the part that shows a gentle slope to drying up [46] that begins in late November or early December in our study area. This method permits to calculate the annual drying coefficients and corresponding low water flows. However, flow separation is hardly possible for a large river [47] and this can affect the calculated low water flows and lead to discrepancies in the results between stations. In addition, the calibration curves of the different stations used in the study back to the 1950s (1950 for Gourbassi, 1952 for Dakka Saidou and 1954 for Oualia). The existence of probable unearthing can persist at the level of the various hydrometric stations with a greater influence on low water flows than on the other hydrological variables. The reasons for this failure to update the calibration curves are various. Indeed, in recent decades, countries which share the Senegal River basin (Guinea-Conakry, Mali, Senegal and Mauritania) have difficulties in providing hydrological monitoring of the river and its tributaries correctly because of a lack of human and financial resources [48]. Over the last few years, African hydrological services have difficulty to ensure water resource monitoring, resulting in significant gaps in streamflow series. The reasons for the discontinuity of data are part of budgetary austerity measures imposed by international financial institutions.

**5. Conclusions**

Analyzing trends of annual rainfall and assessing the impacts of these trends on the hydrological regime are crucial in a context of climate and global change and increasing water use. This paper investigates the recent trend of hydroclimatic variables in the Senegal River basin based on 36 rain gauge stations and 3 hydrometric stations not influenced by hydraulic structures. Results show two main shifts on annual rainfall in 1969 and 1994. The first shift (1969) marks the starting point of the drought as indicated by the values of the SPI. After the second shift (1994), there is an increase of annual rainfall compared to the previous period (1969–1994) which indicated a partial rainfall recovery not significant at a basin level.

Overall, these findings demonstrate that there is a recovery of annual rainfall in the Senegal River basin which is leading to the improvement of surface water availability. However, we cannot affirm with precision if this recovery is significant and persistent unless we have a longer data series. Therefore, it is crucial and necessary to have access to recent hydroclimatic data in order to further investigate the so-called recovery period.

**Author Contributions:** Conceptualization, A.B. and L.D.; methodology, A.B. and L.D.; software, A.B., L.D., G.P., P.M.N.; validation, A.D. (Alain Dezetter), T.V., H.D., and A.D. (Abdoulaye Deme); formal analysis, A.B., L.D., G.P., A.D. (Alain Dezetter), T.V.; data curation, A.B., H.D., I.D.; writing—original draft preparation, A.B. and L.D.; writing—review and editing, A.B., L.D., G.P., A.D. (Alain Dezetter), T.V., I.D., P.M.N.; All authors have read and agreed to the published version of the manuscript.

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

**Acknowledgments:** The authors express their gratitude to the hydrological and meteorological services of the member countries of the Senegal River Basin Development Organization. We would like to thank the IRD and IGE for their technical and financial supports. The authors also thank the three reviewers and the editors.

**Conflicts of Interest:** The authors declare no conflict of interest.
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© 2020 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/).
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Article

# Spatial Drought Characterization for Seyhan River Basin in the Mediterranean Region of Turkey

Yonca Cavus * and Hafzullah Aksoy

Department of Civil Engineering, Istanbul Technical University, Maslak, Istanbul 34469, Turkey

* Correspondence: cavus17@itu.edu.tr

Received: 22 May 2019; Accepted: 24 June 2019; Published: 27 June 2019

**Abstract:** Drought is a natural phenomenon that has great impacts on the economy, society and environment. Therefore, the determination, monitoring and characterization of droughts are of great significance in water resources planning and management. The purpose of this study is to investigate the spatial drought characterizations of Seyhan River basin in the Eastern Mediterranean region of Turkey. The standardized precipitation index (SPI) was calculated from monthly precipitation data at 12-month time scale for 19 meteorological stations scattered over the river basin. Drought with the largest severity in each year is defined as the critical drought of the year. Frequency analysis was applied on the critical drought to determine the best-fit probability distribution function by utilizing the total probability theorem. The sole frequency analysis is insufficient in drought studies unless it is numerically related to other factors such as the severity, duration and intensity. Also, SPI is a technical tool and thus difficult to understand at first glance by end-users and decision-makers. Precipitation deficit defined as the difference between precipitation threshold at SPI = 0 and critical precipitation is therefore more preferable due to its usefulness and for being physically more meaningful to the users. Precipitation deficit is calculated and mapped for 1-, 3-, 6- and 12-month drought durations and 2-, 5-, 10-, 25-, 50- and 100-year return periods at 12-month time scale from the frequency analysis of the critical drought severity. The inverse distance weighted (IDW) interpolation technique is used for the spatial distribution of precipitation deficit over the Seyhan River basin. The spatial and temporal characteristics of drought suggest that the Seyhan River Basin in the Eastern Mediterranean region of Turkey experiences quite mild and severe droughts in terms of precipitation deficit. 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. Another result reached in this study is that it could be common for one point in the basin to suffer dry conditions, whilst surrounding points in the same basin experience normal or even humid conditions. This reinforces the importance of spatial analysis over the basin under investigation instead of the point-scale temporal analysis made in each of the meteorological stations. With the use of spatial mapping of drought, it is expected that the destructive and irreversible effects of hydrological droughts can be realized in a more physical sense.

**Keywords:** critical drought; frequency analysis; Mediterranean region; precipitation deficit; Seyhan River basin; spatial drought analysis; standardized precipitation index (SPI)

## 1. Introduction

Drought is a stochastic natural event which emerges from a remarkable deficiency in precipitation. It has an impact on a large number of sectors since water is the source of life. Drought seriously affects the majority of the population in many ways such as economically, socially and environmentally. The fact that the lack of water affects different sectors makes it more difficult to create one single definition
of drought. A certain definition does not exist for drought because of the complicated stochastic nature of hydrometeorological variables and water demand in different regions around the world [1] and the variability in the climate of the region under investigation. Owing to the increase in water demand, drought hydrology has been receiving much attention. As a result, extensive research studies have been performed on drought and numerous review papers have been published [1–6].

Drought typically has probabilistic characteristics which are severity, duration, intensity, frequency and interarrival time [3,7–9]. In the literature, there have been limitless studies on drought characterization [10–16]. A different approximation of frequency analysis was used to derive precipitation deficit from the drought Severity-Duration-Frequency (SDF) curves for drought characterization in different hydrological basins in Turkey [17–19]. As the most recent study on drought in Turkey, Cavus [20] developed a methodology based on precipitation deficit, precipitation threshold and critical precipitation concepts, all newly introduced and detailed below. Also used is a regression equation established between the SPI and the corresponding precipitation. As the outputs of the methodology, drought SDF and intensity-duration-frequency (IDF) curves were plotted with which it is possible to determine the severity and intensity of a drought with a given duration and return period.

Turkey is situated in the East Mediterranean. Annual average precipitation in Turkey is 630 mm, 67% of which falls during the winter and spring months with the influence of Mediterranean depression [21–23]. Climate models predict that, by the end of the 21st century, Europe will face droughts extending over larger areas in the Mediterranean region with increasing intensity and duration [24,25]. Therefore, monitoring of drought and management plans are vital to determine the impact of drought on the Mediterranean region. Drought action plans should be more efficient such that the use of economic resources is optimized. As examples, Vicente-Serrano and Begueria [26] evaluated the impact of drought using remote sensing in the semi-arid Mediterranean region in Spain. Caloiero et al. [27] analyzed drought to calculate different return-period droughts by using the SPI in the northern hemisphere including the European continent, Ireland, UK and the Mediterranean basins. Vicente-Serrano et al. [28] studied drought patterns in eastern Spain of the Mediterranean region and found the frequency, duration and intensity of drought for each region considered. Also, Spinoni et al. [29] classified drought episodes by frequency, duration and severity.

A particular meteorological station is used when the point-scale temporal analysis of drought is concerned. On the other hand, spatial analysis of drought is as important as its temporal analysis, as it could be common for one point in an area to suffer dry conditions, whilst surrounding points in the same area experience normal or even humid conditions. Thus, information related to not only one particular station but also to neighboring stations is needed in making decision for drought mitigation or preparedness at the basin scale. Spatial analysis is performed using data from all available meteorological stations in the basin. This provides spatial drought characterization that utilizes the severity, intensity, duration and return period. It can be presented in the form of spatial patterns of drought intensity contours for a given drought duration and return period. Spatial variability of drought events in the literature has been approached from different perspectives [21–23,30,31]. He et al. [32] investigated drought hazard and spatial characteristic analysis in China using a GIS-based drought hazard assessment model.

Spatial IDF mapping of the precipitation deficit has been the purpose of this study to provide a comprehensive characterization of droughts for meteorological stations in the Seyhan River basin of the Eastern Mediterranean region, Turkey. In the IDF maps, contours based on the precipitation deficit are plotted for a given drought duration and return period. The maps are given in this study for drought durations of 1, 3, 6 and 12 months and return periods of 2, 5, 10, 25, 50, and 100 years at 12-month time scale. They are expected to provide useful information to use in taking actions against drought such that it becomes less destructive and does not create irreversible effects on the economy, society and ecology. This study is also expected to provide measures for reducing the negative effects of the drought as a step in the drought management plan.
## 2. Seyhan River Basin and Data

The Seyhan River Basin is located in the southern part of Turkey (Figure 1). The climate in the Seyhan River basin is strongly influenced by the topography. The northern part of the basin is characterized by a mountainous, steep, harsh topography while lowlands prevail in the southern part of the basin (Figure 1). The basin extends from the Mediterranean coast to the Central Anatolia and shows three different characteristics in terms of climate. The northern part of the basin exhibits the characteristics of the Central Anatolian climate; thus, it is colder than the southern part of the basin; the highest precipitation is observed at highlands in this part of the basin. In the coastal areas of the Çukurova plain and the surrounding areas, the summer season is hot and dry while the winter is warm and rainy. That part of the basin between the coastal zone in the south and the Tarsus mountains in the north has a semi-arid Mediterranean climate with dry and hot summers and rainy and warm winters.

<figure><img src="image_153.png" /><figcaption>**Figure 1.** Topography of the Seyhan River basin.</figcaption></figure>

Monthly precipitation data were obtained from 19 meteorological stations operated by the State Meteorological Service (MGM with its Turkish acronym) and from the General Directorate of State Hydraulic Works (DSI). The meteorological stations are listed in Table 1 in which the number and name of the stations are given together with the observation period of each station and the total number of missing data filled (in months). Statistical characteristics calculated from the monthly precipitation time series of each meteorological station are also given in Table 1. They are the minimum, maximum and mean values of monthly precipitation data in each meteorological station. It is seen, for example, that station 17981 (Karataş) among all meteorological stations has the highest percentage of no-rainy months (15.31%) and also the lowest altitude (22 m above mean sea level) compared to other stations. The altitude of the meteorological stations varies greatly within the basin owing to the topography. Data quality is an important issue in meteorological applications [33,34]. Any meteorological station with a minimum of 10 years of observation is taken for the analysis. Any gap in the data not exceeding 12 consecutive months was filled; longer gaps, if any, were not filled. In the case that a gap longer than 12 months exists in the time series, data before or after the gap were used provided that either portion is at least 10 years long. From the data before or after such a gap, the longer one was taken. The long-term monthly mean was taken to replace for the missing value of the month in the gap.