1. Introduction
In recent decades, the accentuation of climate variability has sometimes induced catastrophic floods in many countries over the world, and longer or shorter droughts. The consequences of changes in the frequency and intensity of extreme rainfall and atmospheric electrical activity have been violent winds, lightning and floods. These phenomena seriously impede the socio-economic development of nations and especially poor countries. Indeed, many extreme events influence policy-making in several vital sectors of economic and social development, including agriculture, infrastructure, drinking water supply and transport [
1].
In Africa, the last decade has been characterized by frequent floods (notably in the west), which have not even spared the Sahelian countries, such as Senegal, Mali, Burkina Faso, and Niger. Indeed, the African continent was particularly hit in 2007 by floods which affected more than two million lives in the Central and Eastern parts in January, and 2.6 million victims in a large region from west to east in July and August of the same year [
2]. West Africa is therefore one of the most vulnerable regions of the continent, often subject to the adverse effects of climate change.
Like many other countries of the West Africa sub-region, Benin is exposed to devastating natural hazards, resulting in loss of life, loss of economic wealth, and environmental damage. These risks are most often floods, violent precipitation, late rains and drought. From 1984 to 2010, severe floods resulting from heavy rains or river overflow origin affected most regions of the country [
3].
According to the IPCC report [
4], global warming will cause increase of extreme weather events in many parts of the world. This will increase the vulnerability of low income people. Therefore, it is important to know more about how extremes will be fluctuating at several spatial and temporal scales [
5]. Most often, extremes are studied by considering their characteristics indices, mainly frequency indices and intensity indices. However, precipitation is the flux of water that governs the partitioning of water on land into runoff and evapotranspiration. As such, a trend/change in time in precipitation partitions is transformed into a change in evapotranspiration and a change in runoff. This has important implications for calculations of development of human water consumption (as change in evapotranspiration) and water availability (as change in runoff) [
6,
7].
Thus, according to precipitation, the analysis of extreme rainfall is prominent in understanding their previous evolution, current and future trends. This can help in strategies conception to mitigate the effects of climate change. As result, the events have received increased attention in recent years. Many studies have been devoted to trends in extreme weather events in several parts of the world, including Asia [
1,
8,
9], Africa [
10,
11,
12], Europe [
13], America [
14] and Australia [
15].
The results of those studies vary from one region to another, from country to country and even from one local area to another within the same country. In general, several studies showed that while on some stations, there are clearly decreasing or negative trends in extreme rainfall indices, some other stations showed no statistically significant trends or increasing trends. In fact, historical data from 27 rainfall stations in the Iberian Peninsula in Portugal have presented, over the period 1903–2003, an increasing trend, except in the west and in the Gulf of Cadiz where the stations have provided a decreasing trend [
13]. Similarly, in eastern and central Iran (Asia) the seasonal variations in rainfall have significantly decreased whereas in west and north of that country, they have increased over the period 1975–2014 [
16].
At small time scale of 15 min to 240 min, increasing trend and decreasing trend in rainfall are also observable in Ivory Coast: [
10].
In West Africa, considering the trend analysis of climatic indices, more precisely of extreme rainfall, many studies [
10,
11,
17,
18,
19] indicated that there is no uniform trend in all the localities of the region. For example, in Ghana over the period 1960–2011, positive and negative rainfall trends are observed in different localities [
11]. Similarly, in Ivory Coast, even at minute scale, trends depend on localities [
10]. These results clearly demonstrate the need to continue analyzing the extreme rainfall indices trends because of all uncertainties that the climate change could induce.
In Benin, few studies exist on rainfall indices trends. Considering the daily rains of 21 stations over the period 1960–2000, the annual total rainfall, the annual rainy days as well as the maximal rainfall recorded for 30 days exhibited a decreasing trend [
20]. However, there was no trend for the simple daily intensity index, maximum daily rainfall, maximum five-day rainfall, the annual total rainfall of very wet day and extremely wet day indices. This previous work also showed that there is no trend clearly exhibited by the annual number of days with rainfall ≥95th percentile and the annual number of days with rainfall ≥99th percentile as well as the percentage of annual total rainfall from days with rainfall ≥95th percentile and the percentage of annual total rainfall from days with rainfall ≥99th percentile.
At the mesoscale of the upper Ouémé river valley, there is a lack of information on the extreme rainfall indices trend because, even though the study by [
20] took into account some stations of this region, all the stations were not considered. Moreover, this previous study used data ending at the year 2000. Considering increase in floods events in upper Ouémé river valley, which is an area of large agricultural production and shelters the source of the Ouémé river of which flooding often causes floods in southern Benin, it is suitable to extend the analysis to other parameters of the rainfall regime. To achieve this objective, the work carried out in this research aims to identify climate change in the western central part of Benin using rain gauge data, which provide valuable information due to their direct operational mode. The objective of this study is therefore to provide knowledge and more details about extreme rainfall indices trends to contribute to flood and drought management.
4. Discussion
The climatic analysis carried out on the daily rainfall series did not detect a generalized change in the frequency and intensity indices. The analysis showed an almost total absence of statistically significant trends for all the studied variables.
The results show, for almost all the indices of frequency, an absence of trend. For stations that experienced some frequency indices (R10mm, R20mm and CDD) trends, the extents are less significant. These findings confirm those of Hountondji [
13] for most stations in Benin over the period 1960–2000. For intensity indices, many more trends are observed than the case of frequency indices. Nevertheless, the extent of these trends remains also weak except PRCPTOT. This situation could be explained on one hand by the great variability of the precipitations and the random behavior of convective processes which influence the PRCPTOT. Trend absence noted can also be explained by the test of Kendall which supposes a single trend in the data while several changes of the direction of trend can occur. The trend of CDD and CWD that is not clear, indicates the resumption of precipitations in the study area. This result corroborates the findings of Nicholson [
26] which reported the reduction of the negative anomalies of precipitation compared to the reference period 1961–1990, with a certain resumption of the precipitations during the decade 1990. In spite of the negative evolution of PRCPTOT and CWD respectively for 80% and 60% of the stations, the number of consecutive dry days does not present positive trend. Thus, the dry sequences seem to have undergone weak changes. That means that the onset and cessation of the rainy season did not experience any modification over the period 1951–2014. PRCPTOT and CWD indices evolution is the consequence of dry sequences attenuation after the 1980s. This result confirms the “rainfall recovery” during the 1990s noted by Ozer [
27] for the Sahelian area and Lawin [
28] for the study area.
The negative SDII evolution (80% stations) indicates that the changes in intensity of precipitation do not depend necessarily on CWD evolution. However, CWD and PRCPTOT trends are similar on 50% of stations.
Nevertheless, R10mm, R20mm, R25mm, RX1day, RX5day, R95P and R99P indices presented the same trend with PRCPTOT over the period 1951–2014 in almost all the stations. These results show that the annual total precipitation depends on extreme precipitation observed for the period 1951–2014. Thus, the floods recorded in the upper Ouémé river valley during these last decades result from the strong precipitations that occur in zone.
5. Conclusions
The results obtained in this work showed an almost total absence of trend in the frequency indices. Only R10m showed a decreasing trend at Bassila, Bembereke and Kouande; R20m a decreasing trend at Bembereke and an increasing trend at Birni; and CDD showed an increasing trend in Bassila.
As for the intensity indices, they showed more trends than the frequency indices. Decreasing trends are more observed than increasing trends for RX1day, RX5day, PRCPTOT, SDII, R95P, and R10mm, at 30%, 10%, 20%, 20%, 20%, and 30% of stations, respectively. For increasing trend, only CDD faced it, at 10% of stations. About R20mm and R99P decreasing and increasing trends were detected respectively for 10% of the stations.
This study revealed that the upper Ouémé river valley is dominated by non-significant trends.
Bassila, Bembereke, Kouande and Okpara stations exhibited a decrease (negative Z values) in the PRCPTOT and CWD indices, while Beterou station showed an increase for these two parameters (positive Z values). At Birni, a growth of the SDII index is identified. This increase of SDII index is due to the increase in PRCPTOT index and a reduction in the number of wet days. On the other hand, a reduction in the SDII index is recorded at Djougou, Ina, Parakou and Tchaourou stations where the PRCPTOT decreases and the number of wet days increases. Furthermore, Bassila, Bembereke, Birni and Ina experienced a decrease of R95P and R99P while Beterou, Djougou and Parakou showed a slight decrease for these two parameters.
More globally, the analysis of the evolution of the rainfall indices over the period 1951–2014 underlined the non-significant decreasing of the PRCPTOT index, indicating a rainfall recovery in the study region. This trend is partially due to an insignificant reduction of the number of consecutive dry days caused mainly by the wet period 1990–2014. Furthermore, at local scale, the annual rainfall trend seems to be correlated both to the number of heavy, very heavy and extremely heavy rainfall days as well as the simple rainfall intensity indices. Thus, if water consumption and availability have indeed changed in the study area during this period, it must be due to other drivers such as human activities.