Variability and Trends of Precipitation in Quelimane, Central Mozambique, and Their Relation to El Niño Southern Oscillation

Understanding precipitation variability and trends is very important for sus-tainable water management. In this paper, we used 65 years (1951-2016) long-term precipitation data to evaluate the precipitation variability and trends in Quelimane, and their relation to El Niño Southern Oscillation (ENSO). The analysis includes annual, inter-annual inter-decadal variations, Mann-Kendall trend test, and drought frequency. The study also evaluated the relationship between Oceanic Niño Index (ONI) and precipitation patterns during ENSO positive, normal and negative phases. The results show two distinct seasons of precipitation in Quelimane, the wet season extending between December and April and the dry season extending from May to November. ENSO was found to influence the inter-annual variations of precipitation during the wet season, with warm ENSO (El Niño) and cold (La Niña) events tending to reduce and increase the precipitation amounts, respectively. Decreasing trends in in-ter-annual variations of precipitation and increase of drought frequency and severity are highlighted in this study. Both decreasing trend of inter-annual variations and increasing of drought frequency and severity have intensified after the 1970s climate shift. These intensifications seem to be associated with the strengthening of ENSO after the 1970s climate shift. The results of the present study may be useful for the design of the climate change adaptation plans in central Mozambique.


Introduction
Water is a key resource for the economic development and food production, considering the fact that both agriculture and industry sectors are heavily dependent on water availability. During the last century, the surface temperature has increased by almost 1˚C on average, although this value might have been higher in some regions and lower in others. In parallel to temperature increasing, there has been significant reduction in water levels of rivers and lakes, and in precipitation quantity and periodicity due to natural and anthropogenic factors including El Niño Southern Oscillation (ENSO) and changes in the atmospheric circulation (Alley et al., 2007).
Overall, long-term observations of precipitation have shown pronounced trends in the last hundred years, where some places became wetter while others such as Sahel, Mediterranean, and Southern Africa are drying gradually. In general, long-term observations highlight strong inter-annual variations with long periods of droughts interspersed by a year of heavy rain, in response to ENSO (Alley et al., 2007). However, studies conducted in different geographical locations have shown that the effects of ENSO in precipitation can vary from place to place; therefore, the analysis of ENSO-precipitation relation on local scale is important.
The consequences of extreme weather such as floods and droughts have devastating effects in Southern Africa due to poor infrastructure and sanitation observed in most regions (Fauchereu et al., 2003). Although there is still limited research about the mechanisms, previous studies found that ENSO tends to decrease the precipitation in Southern Africa mostly during Austral summer season (December to March) when ENSO has reached maturity (Mason & Jury, 1997;Ratnam et al., 2014). The mid-1970s have been designated as the last change point of global climate regime due to abrupt changes in environmental conditions in the Pacific Basin (Meehl et al., 2009). Previous studies, including the researches by Richard et al., (2000), Mason (2001) and Marengo (2008) have pointed out that precipitation pattern has changed after the 1970's decade, also known as the 1970's climate shift. Studies from Gaughan & Waylen (2012), Gaughan et al. (2016) and Nicholson et al. (2018) found out that after 1970s the drought frequency has increased in some sub-regions of Southern Africa, including the Lower Zambezi basin.
Quelimane is located in central Mozambique and most of its population has precipitation-fed rice farming as their main source of income, and therefore, the droughts have dramatic social economic impacts. Although there are some studies addressed to the variability of precipitation of Southern Africa and its relationship to ENSO, there are imited published studies based on local data for central Mozambique, for instance. It should be mentioned, however, that such studies are more valuable since they add to the studies conducted at a regional scale, which are based on station aggregation and coarse space network average.
In view of the above, this study aimed at evaluating the temporal variability and

Study Area
Quelimane (Figure 1) is the capital of Zambézia province, which is located in  Kottek et al. (2006), the climate of Quelimane is equatorial savannah with dry winter.

Data Analysis
In general, this study focuses on three main analysis, namely variability, trend detection and relationship between precipitation in Quelimane and ENSO. The analysis of variability was performed using monthly climatology data, and standardized precipitation anomaly, which allowed inferring the annual cycle, drought frequency and severity before and after the 1970s climate shift. The standardized precipitation anomaly was calculated as follows (Angnew & Chappel, 1999).
Precipitation anomaly, P t is the total annual precipitation in year t; P m is the mean precipitation over a specific period. For comparison of frequency and severity between years (1951-1975) and (1980-2016), it was used the dataset from 1951-2016 period to determine the mean precipitation for both periods. σ is the standard deviation of precipitation for 1951-2016 period. The droughts were categorized into extreme drought (P a < −1.65), severe (−1.28 > P a > −1.65), moderate drought (−0.84 > P a > −1.28) and no drought (P a > −0.84), folowing the criteria used by Angnew & Chappel, (1999). To normalize the difference in sample size between period 1 (prior climate shift, 25 years) and period 2 (after climate shift, 37 years), the drought frequency was estimated as the number of droughts per decade, given by the following equation: 10 * Freq λ n = (2) where: Freq. is the drought frequency, n is the number of drought in years for the period, and λ is the sample size.
Trend detection and analysis were determined using Mann-Kendall nonparametric test and Sen's estimator of slope. The goal of Mann-Kendall (MK) nonparametric test (Mann, 1945;Kendall, 1975) is to infer if there is a monotonic upward or downward trend in a time series. The Mann-Kendall parameter is widely used to evaluate the trends of hydrological and meteorological time series. In this study, the Mann-Kendall test was applied to detect the trend of annual, seasonal and monthly precipitation in Quelimane. The MK test is a two-tailed test.
It tests to reject the null hypothesis (h 0 ), which is "the absence of trend" and accept the alternative hypothesis (h 1 ), which is "existence of a monotonic trend in the time series". The MK statistic S in a time of series of n elements is given in where n is the number of data point, x j and x k are the annual values in years j ( ) When the number of observation is more than 10, the statistic S is assumed to be approximately normally distributed, with mean E(S) = 0 (Kendall, 1975). In this case, the variance Var(S) can be obtained through Equation (5) where t i is the number of ties of length i.
The dimensionless statistic test Z (Equation (6)) follows a normal distribution, where positive (negative) values indicate upwards (downwards) trend, and is given by: In the case of two-sided test the h 0 is rejected and h 1 is accepted if [Z] is higher that Z α/2 .
In this study the hypothesis was tested at 95% confidence level, where α = 0.05. The slope of precipitation trend was computed using Sen's estimator (Sen, 1968). According to this method the slope T i of any two values of time series x can be estimated through the following equation: where x i and x j are data values at time j and i (j > i) correspondingly. The median of these N values of T i is represented as Sen's estimator of slope and is determined as Q = T(N + 1)/2 if N is odd and Q = [T (N/2)+ T( (N+2)/2 )/2] if N is even. Positive value of Q indicates an upwards trend and negative value of Q indicates a downwards trend.
To evaluate the relationship between ENSO and precipitation, 3-month moving average time series of precipitation was computed from mean monthly data, in order to match the 3-month moving average of ONI. For each trimester, correlation analysis between ONI and precipitation and comparisons of precipitation climatology for Positive, Normal, and Negative ONI were performed. The ONI is defined as Positive if ONI ≥ 0.5, Normal if −0.5 < ONI < 0.5, and Negative if ONI ≤ −0.5 ( Figure 2). Additionally, percentage of the anomalies of accumulative annual precipitation was calculated to point out how the precipitation in a specific ENSO year deviates from the historical means (Equation (8)).

Results
Variation in annual precipitation shows similar patterns in the three periods; 1951-1971, 1951-2016 and 1980-2016     (May-November) and for the three selected periods (1951-1975, 1951-2016 and 1980-2016). Comparing the droughts of the periods 1951-1975 and 1980-2016 Journal of Geoscience and Environment Protection total annual precipitation, which had similar pattern to wet season, the total annual precipitation varied between about 900 mm in years 1951, 1871, 1984, 1987, 1992, 2002 and 2008 and 2100 mm in years 1962, 1979 and 1981. There were three noticeable time intervals in the time series, namely, 1961series, namely, -1977series, namely, , 1977series, namely, -1990series, namely, and 2008series, namely, -2016series, namely, . During 1965series, namely, -1977, the total annual precipitation presents smaller peaks and smaller amplitudes between wetter and drier years. The time interval 1977-1990 is characterized by higher amplitude between wetter and drier years. From 1977 to 1978, for example, there was an increase of about 133% in the total annual precipitation. In the 2008-2016, there were fewer peaks such that in most of the years the precipitation amounts were lower than historical mean. For dry season, it is possible to highlight five driest years of the time series (1957,1987,2005,2008 and 2016) and four sub-periods, 1957sub-periods, -1976sub-periods, , 1977sub-periods, -1984sub-periods, , 1995sub-periods, -2007sub-periods, and 2008sub-periods, -2016sub-periods, . The 1957sub-periods, -1976  Comparing the values of trend's slope for the periods before  and    ONI ( Figure 9) are plotted for the three selected periods, (1951-1975, 1951-2016 and 1980-2016)

Discussion
Climatology of annual variation of precipitation in Quelimane (Figure 3) showed ing the austral summer months as reported Aimola & Moura (2016). Overall, El Niño (La Niña) years tend to be associated to drier (wetter) conditions in response to a high (low) pressure center which dominates the southern Africa during warm (cold) phases of ENSO, repressing (enhancing) convection and rainfall (Nicholson & Kim, 1997;Reason & Jagadheesha, 2005). This pattern is also observed in the present study for Quelimane, where most of the drier years (e.g. 1957-1958, 1965-1966, 1982-1983, 1991-1992, 2015-2016) are associated to warm phases of ENSO while wetter years (e.g. 1955-1956, 1988-1989, 199-2000) are associated to cold phases of ENSO. However, the relationship between the ENSO and precipitation does not seem to be linear. An example of non-linearity is the difference in precipitation amounts during 1997-1998strong El Niño and 1991-1992and 2002-2003moderate El Niño. The year 1997-1998was expected to be drier than 1991-1992and 2002-2003 due to the strong El Niño but it was relatively wetter for all the three seasons (total annual precipitation, wet and dry seasons), suggesting that the impact of El Niño in this year was not as strong as would be expected.
These findings corroborate with the study from Reason & Jagadheesha (2005) who compared the NDJ and JFM precipitation anomalies in Southern Africa using NCEP reanalysis data. According to Reason & Jagadheesha (2005) the source for the difference in ENSO impact was the strength of the Angola Low, which together with other local and regional forcings, including Atlantic and Indian Ocean also play a role on inter-annual variability of precipitation in Southern Africa (Nicholson & Kim, 1997;Reason & Jagadheesha, 2005). Moreover, the general pattern of drier conditions during El Niño and wetter conditions during La Niña is not evident in the composites of some dry season trimesters (ASO and SON, Figure 9), which may be an indication of overcoming of the impact of ENSO on inter-annual variation by the inter-decadal variation.
To assess the possible overcoming of ENSO impact by inter-decadal variation we plotted the inter-annual variation of SON precipitation anomalies ( Figure   10). In inter-annual time scale one can note that the SON precipitation is randomly distributed in relation do ONI. On the other hand, it is possible to distinguish three sub-periods in decadal scale : 1951-1976, 1977-1989, and 1990-2015 (1977,1979,1982,1984), two of which were El Niño years (1977,1982). By contrast, between 1990 and 2015 after climate shift, more than 80% of years presented SON precipitation below the historical mean. The 1990-2015 sub-period also held five of the six driest SON precipitation years, four of which were normal years. This suggests that the impact of ENSO on inter-annual precipitation pattern is more discernible in the wet season, and is masked by the inter-decadal variations in some months of the dry season. Journal of Geoscience and Environment Protection In general, the comparisons of precipitation patterns during the periods before and after the 1970s climate shift  showed a tendency of drier conditions as evidenced by reduction in monthly mean precipitation in all months except in January (Figure 3), increasing in frequency and severity of droughts, and negative Mann-Kendall and Sens's Slope (Figure 4, Figure 6, Figure 7). The drier conditions in later decades were in line with intensification of the negative correlation between ONI and precipitation (Figure 8), specially in wet season months, suggesting strengthening of ENSO impact. Previous studies by Richard et al. (2000), Rouault & Richard (2005), and Monerie et al. (2019), also pointed out strengthening of the ENSO impact on Southern African precipitation in recent decades. With regards to the cause of the modification of the impact of ENSO on precipitation in recent years, the change in the climate state, associated to the increasing in Sea Surface temperature over the Equatorial Pacific Ocean under global warming (Wang et al., 2019), might be the main cause.

Conclusion
The precipitation in Quelimane presents two distinct seasons, the wet season extending between December and April and the dry season extending between May and November. The inter-annual time series revealed that the precipitation in Quelimane presents a significant year-to-year variation and pronounced inter-decadal oscillations. Mann-Kendall trend analysis of monthly, seasonal and annual time series revealed that overall, the precipitation in Quelimane is decreasing and the decreasing rate intensified after the 1970s climate shift. However only three of forty-five-time series presented statistically significant trends. The evaluation of the variation of drought frequency showed increase in both frequency and severity of droughts after the 1970s climate shift such that all of the severe droughts for the wet season occurred after the climate shift.
El Niño influence was found to be more discernible on wet season precipitation with warm ENSO events (El Niño) tending to reduce the precipitation and cold ENSO events (La Niña) tending to increase the precipitation amounts in Quelimane. For the dry season, the influence of ENSO is overcome by inter-decadal variation. Moreover, the intensification of the decreasing trend along with the increase of drought frequency and severity seems to be associated with the strengthening of ENSO impact after the 1970s climate shift.
The results of the present research suggest that the reduction in agricultural production due to precipitation scarcity may increase in the future. This study used long-term dataset of only one meteorological station due to a lack of continuous long-term data from other stations of Central Mozambique. Further research should be carried out on validating long-term data of satellite-estimated precipitation to evaluate the variability and trends of precipitation in the entire central Mozambique region.