Elevation Dependence of the Impact of Global Warming on Rainfall Variations in a Tropical Island

water

Article Elevation Dependence of the Impact of Global Warming on Rainfall Variations in a Tropical Island

Mirindra Finaritra Rabezanahary Tanteliniaina 1, Jia Chen 2, Tanveer M. Adyel 3 and Jun Zhai 1,*

1 MOE Key Laboratory of the Three Gorges Reservoir Region’s Eco-Environment, Chongqing University, Chongqing 400045, China; [email protected] 2 MOE Engineering Research Center for Waste Oil Recovery Technology and Equipment, Chongqing Technology and Business University, Chongqing 400067, China; [email protected] 3 Department of Civil Engineering, Monash University, 23 College Walk, Clayton, VIC 3800, Australia; [email protected] * Correspondence: [email protected]; Tel.: +86-23-6512-0810 or +86-136-3796-6883; Fax: +86-23-6512-0810

Received: 29 November 2020; Accepted: 17 December 2020; Published: 21 December 2020

Abstract: Due to their vulnerability, understanding the impacts of global warming on rainfall is important for a tropical country and islands. This research aimed to assess the impact of global warming on rainfall in Madagascar, using the Mann-Kendall test, continuous wavelet transform, and polynomial regression. The result showed that the annual, seasonal maximum, and minimum temperature increased, while elevation amplified the increase of maximum temperature. Different trends in rainfall were found in the 22 regions of Madagascar but in general, the increasing trend in rainfall was prominent at a higher elevation than lower elevation. The annual rainfall decreased up to 5 mm per year for the regions located below 450 m of altitude while increased up to +5 mm per − year above 500 m. We found that the wet becomes wetter with an important increase in rainfall in summer and the increase in temperature influenced the rainfall. The annual rainfall increased with temperature and elevation. However, if the increase in temperature was more than 0.03 ◦C per year, the annual rainfall increased regardless of elevation. The knowledge of the elevation dependence of the impact of warming on rainfall is important for water resources management and climate change adaptation strategies, especially for island nations and African countries.

Keywords: global warming; temperature; rainfall; elevation; tropical climate; Madagascar

1. Introduction Since the rise of manmade greenhouse gas emissions, global temperature has continuously increased, with a 1.07 ◦C increase since 1880, and 19 of the 20 warmest years have occurred since 2001, except 1998 [1]. Several studies have demonstrated that global warming impacts the temporal and spatial variation of rainfall and leads to frequent extreme weather [2–5]. In general, global warming intensifies the global hydrological cycle and increases average global precipitation, evaporation, and runoff [6,7]. Change in evaporation leads to more frequent and intense storms, and droughts [2,8]; hence, change in land cover and land use [9]. Moreover, climate change and global warming are also responsible for the degradation of ecosystem services [9]. The impacts of global warming are not evenly distributed among all countries around the world as island nations are more exposed and more affected [4]. Furthermore, due to their weaker economies, the impact of global warming is expected to be more severe in African and tropical countries [2,10–13]. Therefore, adaptation is important [14], especially in African countries. However, to design an effective adaptation strategy, plenty of research is needed. Yet, there has been relatively little work published on the hydro climatological impact of global warming for Africa.

Water 2020, 12, 3582; doi:10.3390/w12123582 www.mdpi.com/journal/water Water 2020, 12, 3582 2 of 15

Madagascar is an African island, considered by the World Meteorological Organization to be the third most vulnerable country exposed to global warming [15]. It is located in the Indian Ocean between 12 and 25◦ latitude south, and the climate of the island is in close and direct relation with the atmospheric circulation of the Indian Ocean [16,17]. The island is a good site and reference for the study of global warming impacts because almost all the climate types in the African continent are present in Madagascar [15,18]. Therefore, research led in Madagascar can be scaled up to the African continent. However, the few studies published concerning climate change in Madagascar found evidence of uneven patterns across the country, and many uncertainties remain on how global warming is affecting the rainfall of the island [15,17]. Researchers have found a correlation between rainfall trend and elevation/altitude [19–22]. The risk of heavy rainfall is generally expected in high-elevation regions [4]. However, the effect of altitude may vary spatially and seasonally [19–23], and very few studies have been published to address the elevation dependence of global warming impacts on rainfall in an African tropical island like Madagascar. Continuous Wavelet Transform (CWT) is a recent development in signal processing [24,25], and was applied to the 115 years average temperature and rainfall of Madagascar to study the periodicity in the time series. The CWT can provide time-frequency analysis on a time series and is appropriate for hydrological climate studies [24,26]. The non-parametric Mann-Kendall test (MK-test) [27,28] was used to detect the existence of a monotonic trend in the temperature and rainfall data. The MK-test is a robust tool and is effective regardless of outliers and the skewness of the data [24,29–31]. Sen’s slope estimator was applied to determine the magnitude of the monotonic trend. Finally, polynomial regression, a non-linear regression, was used to describe the relationship between the elevation, the temperature, and the rainfall. Polynomial regression provides the most flexible curve-fitting relationship between the dependent and independent variables [32]. In this study, the spatio-temporal temperature and rainfall changes along with elevation in Madagascar were investigated, using two different climatic data sets, a long-term data from 1901 to 2015 and a short-term data from 1987 to 2017.

2. Materials and Methods

2.1. Description of the Study Area Madagascar is the fourth largest island in the world, located in the southwestern part of the Indian Ocean, just oﬀ the South-Eastern edge of the African continent. The country is located at 11◦550 S and 2 25◦300 S; 43◦100 E and 50◦350 E with 592,000 km of total area. Madagascar has a tropical climate, with two distinct seasons: a hot and rainy season from October to March with January as the wettest month, and a dry season from April to September with July as the driest one. Because of its geographical position and the amount of precipitation received each year, Madagascar enjoys ﬁve types of climate: arid climate (rainfall < 600 mm per year), Semi-arid climate (rainfall between 600 and 1000 mm per year), tropical savanna climate (rainfall between 1000 and 1500 mm per year), tropical monsoon climate (1500 and 2000 mm per year), and tropical rainforest climate (rainfall > 2000 mm per year) (Figure1). In terms of topography, the country is characterized by an elevated central plateau running most of the island’s length, surrounded by coastal lowlands. The average elevation of the plateau’s spine is about 1500 m; the three highest peaks range from 2600 to 2900 m in altitude [33]. Madagascar can be divided into 22 regions and each region owns a meteorological station.

2.2. Data Source For the analysis using CWT, we needed long-term data, so we used historical annual average rainfall and temperature data of 115 years from 1901 to 2017 from the World Bank (https: //climateknowledgeportal.worldbank.org/download-data). The data were assessed with preliminary analysis by plotting time series and inspecting possible errors and uncertainties. The test results indicated that it was a high-quality data and had no missing values. For the rest of the analysis, we used Water 2020, 12, 3582 3 of 15

31 years (short term data from 1987 to 2017) of quality-controlled regional rainfall and temperature data (maximum and minimum temperature), assembled from the Madagascar National Meteorological and Hydrological Service. No missing data were observed with the short-term datasets. Elevation data were gathered from USGS-NASA (https://earthexplorer.usgs.gov/); about 79 digital elevation models Water(SRTM-DEM) 2020, 12, x FOR were PEER needed REVIEW to cover the total area of Madagascar. 3 of 16

FigureFigure 1. ElevationElevation and and rainfall rainfall map map of Madagascar data obtained from Madagascar meteorological andand hydrological hydrological se servicervice and USGS-NASA. 2.3. Methods 2.2. Data Source 2.3.1.For The the MK analysis Test and using Sen’s CWT, Slope we Estimator needed long-term data, so we used historical annual average rainfallThe and MK temperature test [27,28] wasdata applied of 115 to studyyears thefrom rainfall 1901 andto 2017 temperature from the variations World trends.Bank (https://climateknowledgeportal.worldbank.org/dowThe MK-test is a non-parametric test used to detectnload-data). a trend in meteorologicalThe data were and assessed hydrological with preliminarydata [34–40]. analysis The WMO by plotting recommended time series it in and assessing inspecting trends possible in precipitation errors and anduncertainties. temperature The time test resultsseries. indicated It has the advantagethat it was ofa high-quality being eﬀective, data regardless and had ofno the missing distribution values. of For the the data. rest In of this the approach,analysis, wethe used diﬀerences 31 years between (short each term sequential data from value 1987 were to calculated2017) of quality-controlled to detect a trend. Equationregional rainfall (1) describes and temperaturethe test statistic data (S): (maximum and minimum temperature), assembled from the Madagascar National n 1 n Meteorological and Hydrological Service.X− NoX missing data were observed with the short-term S = sgn X X (1) datasets. Elevation data were gathered from USGS-NASAj (https://earthexplor− i er.usgs.gov/); about 79 digital elevation models (SRTM-DEM) werek= 1neededj=k+1 to cover the total area of Madagascar. where, Xi is a time-series from i = 1,2,3, ... ,n 1, Xj is another time-series from j = i + 1, ... ,n, Xj is − 2.3.greater Methods than Xi, and n is the data set record length. Each point xi is used to reference the point of xj, the result recorded as Equation (2): 2.3.1. The MK Test and Sen’s Slope Estimator  The MK test [27,28] was applied to study +the1 > rainfallxj x iand temperature variations trends. The  −  MK-test is a non-parametric test used tosgn detect=  a0 trend= xj in xmeteorologicali and hydrological data [34–(2)  − 40]. The WMO recommended it in assessing trends in precipitation and temperature time series. It  1 < xj xi has the advantage of being effective, regardless− of the distribution− of the data. In this approach, the differences between each sequential value were calculated to detect a trend. Equation (1) describes the test statistic (S):

S= sgn (X −X) (1) where, Xi is a time-series from i = 1,2,3, …,n − 1, Xj is another time-series from j = i + 1,…,n, Xj is greater than Xi, and n is the data set record length. Each point xi is used to reference the point of xj, the result recorded as Equation (2):

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With time-series larger or equal than 10 (n 10), the S statistic follows the normal distribution ≥ with a mean of E(S) = 0 and the variance (Var (S)) as Equation (3):

Pm n(n 1)(2n + 5) = t(t 1)(2t + 5) Var(S) = − − t i − (3) 18 where n is the length of the data set and t is the number of ties with of t-th value. Then, the MK-test statistic test Z can be estimated as Equation (4):

 S 1  − S > 0  √Var(S)   Zc =  0 S = 0 (4)   S+1  S < 0 √Var(S)

A positive value of Zc indicates a positive trend, whereas a negative value represents a negative trend in the time series. At significance level α, if Zc Z , then the trend of the data is considered ≥ α/2 significant; otherwise, it is not. For this study, the significance level α was set as 0.05 and 0.1. To get information about the magnitude of the trend (amount of rainfall and temperature), we used the Sen’s slope estimator proposed by Sen and Theil [41,42], and it was calculated as the median of all slopes (Equation (5)): ! Xi Xj β = Median − , j < i (5) i j ∀ − This analysis was performed using R-studio with the Kendall package and Xlstat 2018 (Addinsoft).

2.3.2. Continuous Wavelet Transform CWT was used to study the periodic structure of the annual average rainfall often hidden in the time series [34,43]. For a given time series xt, CWT is a convolution of xt with a scaled and translated mother wavelet. In this study, the complex “Morlet” wavelet [44] function was used because of its better time-frequency localization when compared to other commonly used wavelets, such as the Mexican Hat and the Daubechies wavelets [26] as Equation (6):

1 iω0n n2/2 Ψ0(η) = π− 4 e− e− (6)

The wavelet transform Wn (S) is the inner product (or convolution) of the wavelet function with the original time series as Equation (7):

N 1 " # X− (n0 n)δt Wn (S) = Xn0 Ψ − (7) ∗ ∗ S n0=0 where n is the localized time-series, n’ is the time variable, s is the wavelet scale, δt is the sampling period, N is the number of points in the time series, and the asterisk (*) indicates the complex conjugate. The result of the CWT is displayed by plotting the wavelet power spectrum |W_n (S)|ˆ2 obtained. In our study, the wavelet transform was performed using Matlab R2016a (MathWorks) software with the code developed elsewhere [25].

2.3.3. Elevation Dependence of Temperature Trends and Rainfall Trends Polynomial regression was used to assess the elevation dependence of temperature trends and rainfall trends. Even though some studies have already proved the linear relationship between these variables [19,20,45], the result of the study of [46,47] found that the relationship existing between rainfall, temperature, and altitude does not always stand linear, especially for a tropical island [48]. Considering that linear equations are not well suited to depicting the relationships of rainfall with Water 2020, 12, 3582 5 of 15 topography and temperature, we used a non-linear regression (Equation (8)) and for the relationship between rainfall temperature and elevation, we used (Equation (9)):

β β β 2 β n ∆Yi = 0 + 1Zi + 2Zi + ...... + nZi (8)

β β β β 2 β 2 β n β n β n n ∆Yi = 00 + 10Xi + 01Zi + 20Xi + 02Zi + ...... + n0Xi + 0nZi + nnXi Zi (9) where the dependent variable ∆Yi is the trend detected in rainfall for the region i, Zi is the altitude of region i, Xi is the temperature change of region i, and βs are the regression coefficients. The order n of our model was determined by assessing R2 and p-value; we stopped increasing the order when it does not improve the fit of the model anymore. We began with a null model with zero-order and increased the model complexity as long as it could be justified with the physical explanation.

3. Results and Discussions

3.1. The Warming in Madagascar

The average annual temperature of Madagascar increased by about 0.0042 ◦C per year since 1901. Through the preliminary analysis, we saw an abrupt change in the average annual temperature after 1990. Hence, we divided the time series into two epochs: 1901 to 1990 and 1991 to 2017. The first epoch had a significant decreasing trend ( 0.0049 C per year), while the second epoch − ◦ displayed a significant increasing trend (0.02 ◦C per year). The decreasing trend in average annual temperature during the first epoch reflects the global cooling during the 1940s [17,49]. The study of Nematchoua et al. and Tadross et al. [15,17] suggested that the annual temperature of Madagascar in the 21st century is 0.2 ◦C warmer compared with the 1950s. Overall, Niang et al. [50] reported that Africa is now 0.5 ◦C warmer compared with the last century. The rise of annual temperature after the 1970s co-occurs simultaneously in Africa and the rest of the world [50,51]. However, similar to our findings, some African countries only experienced significant warming after the 1990s [52]. The study of Collins [53] presented that the African temperature anomalies in the late 1990s were significantly higher compared with the 1970s and 1980s. Niang et al. [50] indicated strong evidence of the influence of anthropogenic activity on warming. The annual regional temperature trend is summarized in Figure2. Overall, both Tmax and Tmin increased. For annual Tmax, 15 out of 22 regions had a warming trend (Figure2b) and for annual Tmin, 19 out of 22 regions experienced a rise (Figure2a). The highest increase in annual Tmax and Tmin was detected in Itasy located at 1200 m of altitude with an increase of 0.05 ◦C per year and 0.038 ◦C per year, respectively. It is worth noting that except for Vakinankaratra (altitude 1500 m), 5 out of 7 regions with an increase in annual Tmax greater than 0.02 ◦C per year are located at more than 800 m and all the regions with decreasing annual Tmax are located below 47 m of elevation (see AppendixA). In line with previous studies, this result demonstrated the enhancement rate of warming with elevation [54–56] inseparable to global warming [55]. Furthermore, we found that annual Tmax is rapidly warming compared to annual Tmin, which has occurred in some countries in the East Africa too [57]. Despite the difference between the magnitude of increase in annual Tmax and Tmin, previous studies and ours agree on the warming of both annual Tmin and Tmax [50,53]. Overall, seasonal analysis revealed an increase in temperature in both winter and summer despite the existence of decrease in some regions. The increase in Tmax in summer was between 0.0009 and 0.05 ◦C per year and in winter it was between 0.0009 and 0.07 ◦C per year. The increase in Tmin in summer and winter was between 0.003 and 0.05 ◦C per year and 0.005 and 0.05 ◦C per year, respectively, suggesting a greater warming in winter compared to summer. It is worth noting that unlike annual Tmax, except for Tmax in summer, the variation of Tmin and Tmax in both seasons did not show a correlation with the elevation (Figure3). Despite the existence of decrease in seasonal temperature at some regions, it is evident that both seasons had warming, and the findings are in agreement with the results of previous studies [15,17]. The increase in temperature in Angola, Namibia, was also greater in Water 2020, 12, x FOR PEER REVIEW 6 of 16 respectively,Water 2020, 12 ,suggesting x FOR PEER REVIEW a greater warming in winter compared to summer. It is worth noting6 of 16that unlike annual Tmax, except for Tmax in summer, the variation of Tmin and Tmax in both seasons respectively, suggesting a greater warming in winter compared to summer. It is worth noting that didunlike not show annual a correlation Tmax, except with for the Tmax elevation in summer, (Figure the 3). variation Despite ofthe Tmin existence and Tmax of decrease in both in seasons seasonal temperaturedid not show at asome correlation regions, with it theis evident elevation that (Figure both 3).seasons Despite had the warming,existence of and decrease the findings in seasonal are in agreementWatertemperature2020, 12 ,with 3582 at somethe results regions, of it previous is evident studies that both [1 5,17].seasons The had increase warming, in andtemperature the findings in areAngola, 6in of 15 Namibia,agreement was with also the greater results in ofwinter previous [58]. studiesAs a whole, [15,17]. our The findings increase are in in temperature line with studies in Angola, on the AfricanNamibia, continent was also revealing greater inthe winter warming [58]. Asof aboth whole, seasons our findings and a aredifference in line within the studies magnitude on the of increasewinterAfrican [ 58for continent]. both As a seasons. whole, revealing our findingsthe warming are in of line both with seasons studies and on a the difference African continentin the magnitude revealing of the warmingincrease offor both both seasons seasons. and a difference in the magnitude of increase for both seasons.

FigureFigureFigure 2. 2. 2. Annual AnnualAnnual ( (aa)) Tmin Tmin andand and ( (bb)) Tmax Tmax trendtrend trend for for for each each each region region region of of ofMadagascar. Madagascar. Madagascar.

Figure 3. Trend magnitudes of (a) annual Tmax, (b) summer Tmax, (c) winter Tmax, (d) Annual Tmin Figure 3. Trend magnitudes of (a) annual Tmax, (b) summer Tmax, (c) winter Tmax, (d) Annual Tmin Figure(e) summer 3. Trend Tmin, magnitudes and (f) winterof (a) annual Tmin accordingTmax, (b) tosummer the elevation. Tmax, (Openc) winter circles, Tmax, dark (d panels,) Annual light Tmin (e) summer Tmin, and (f) winter Tmin according to the elevation. Open circles, dark panels, light panel (e) summer Tmin, and (f) winter Tmin according to the elevation. Open circles, dark panels, light and solid line indicate calculated values, 95% conﬁdence band, 95% prediction band and best curve-ﬁt line, respectively. Water 2020, 12, x FOR PEER REVIEW 7 of 16

Water 2020panel, 12 and, 3582 solid line indicate calculated values, 95% confidence band, 95% prediction band and best7 of 15 curve-fit line, respectively.

3.2. Changes in Annual Rainfall

3.2.1. Increases in Annual Average Rainfall of Madagascar 3.2.1. Increases in Annual Average Rainfall of Madagascar The MK-test applied to the annual average rainfall indicated an increase of 42 mm per decade, The MK-test applied to the annual average rainfall indicated an increase of 42 mm per decade, but this change was statistically insignificant. We did not find an abrupt change in the data through but this change was statistically insignificant. We did not find an abrupt change in the data through the preliminary analysis. The insignificance of the rainfall changing trend was often seen in other the preliminary analysis. The insignificance of the rainfall changing trend was often seen in other studies in Southern Africa [59]. The annual average rainfall in Madagascar and CWT results on annual studies in Southern Africa [59]. The annual average rainfall in Madagascar and CWT results on average rainfall are shown in Figure4. The CWT revealed a 2–8 years interannual periodicity in annual annual average rainfall are shown in Figure 4. The CWT revealed a 2–8 years interannual periodicity average rainfall in 1910–1920, 1927–1938; 1938–1984, and 1980–1992. It is worth noting that power in annual average rainfall in 1910–1920, 1927–1938; 1938–1984, and 1980–1992. It is worth noting that detected at 1 to 2 years of the band can be used to detect wet and dry years [60]. Thus, the power in the power detected at 1 to 2 years of the band can be used to detect wet and dry years [60]. Thus, the 1960s and between 1980 and 1990 indicated a wet period and explained the increasing trend in the power in the 1960s and between 1980 and 1990 indicated a wet period and explained the increasing annual average rainfall. The change in rainfall patterns in the 1980s happened across the whole African trend in the annual average rainfall. The change in rainfall patterns in the 1980s happened across the continent [59]. The periodicity found in this study accords well with the rainfall cycles in equatorial whole African continent [59]. The periodicity found in this study accords well with the rainfall cycles east Africa [61] and our findings are in line with other studies of African and general tropical rainfall in equatorial east Africa [61] and our findings are in line with other studies of African and general variability [62,63]. tropical rainfall variability [62,63].

Figure 4. (a) Annual average rainfall and (b) Wavelet analysis of the annual rainfall of Madagascar. TheFigure thick 4. ( blacka) Annual contour average designates rainfall the and 95% (b confidence) Wavelet analysis level against of the red annu noise.al rainfall The cone of Madagascar. of influence (COI)The thick where black edge contour effects designates might distort the is95% shown confidence as the U-shape level against line. red noise. The cone of influence (COI) where edge effects might distort is shown as the U-shape line. 3.2.2. Changes in regional annual rainfall of Madagascar 3.2.2. Changes in regional annual rainfall of Madagascar The annual rainfall analysis presented an increasing trend for the following regions: SAVA (10.02The mm annual per year), rainfall Sofia analysis (9.27 mm presented per year), an Betsiboka increasing (6.42 trend mm for per the year), following Analamanga regions: (0.71 SAVA mm per(10.02 year), mm Bongolava per year), (10.01 Sofia mm(9.27 per mm year), per Itasyyear), (13.51 Betsiboka mm per (6.42 year), mm Vakinankaratra per year), Analamanga (2.05 mm (0.71 per year), mm Amoron’iper year), ManiaBongolava (9.66 (10.01 mm per mm year), per andyear), Ihorombe Itasy (13.51 (8.97 mm mm per per year), year), Vakinankaratra while a decreasing (2.05 trend mm wasper noticedyear), Amoron’i for the others Mania (Figure (9.66 5mm). The per results year), forand Atsimo Ihorombe Antsinanana (8.97 mm ( pper= 0.09),year), Ihorombewhile a decreasing(p = 0.05), andtrend Itasy was ( pnoticed= 0.06) for were the statistically others (Figure significant. 5). The results All of the for regions Atsimo with Antsinanana an increasing (p = 0.09), trend Ihorombe of annual rainfall(p = 0.05), were and located Itasy (p above = 0.06) 400 were m altitudestatistically and significant. at the center All of of Madagascar. the regions with This an finding increasing indicated trend a relativeof annual correlation rainfall were between located the above annual 400 rainfall m altitude and the and altitude. at the Almostcenter of all Madagascar. regions on the This coast finding side indicated a a relative decreasing correlation rainfall, between except SAVA, the annual while rainfall regions and located the altitude. in the centralAlmost part all regions of the island,on the exceptcoast side Matsiatra indicated Ambony, a decreasing showed rainfall, an increasing except SAVA, trend (Figure while 5regions). The studylocated of inZeng the central et al. [22 part] and of Yuthe etisland, al. [21 except] explained Matsiatra the same Ambony, results showed and their an findingsincreasing agree trend with (Figure ours. 5). The The highest study rise of Zeng of rainfall et al. detected[22] and Yu was et for al. Itasy,[21] explained the region the in thesame central results part and of their the islandfindings with agree 13.51 with mm ours. per year.The highest According rise toof therainfall temperature detected was analysis, for Itasy, Itasy the was region the region in the withcentral the part highest of the increase island with in annual 13.51 mm temperature, per year. suggestingAccording to the the existence temperature of the analysis, warmer-becomes-wetter Itasy was the region phenomena. with the Indeed,highest increase the water-holding in annual capacitytemperature, of the suggesting atmosphere the existence increases of by the about warmer-becomes-wetter 7% change for a 1 ◦ Cphenomena. increase in Indeed, temperature the water- [64]. The highest loss of annual rainfall was noticed at Atsimo Atsinanana located on the East coast,

Water 2020, 12, x FOR PEER REVIEW 8 of 16 Water 2020, 12, 3582 8 of 15 holding capacity of the atmosphere increases by about 7% change for a 1 °C increase in temperature where[64]. The rainfall highest has loss decreased of annual about rainfall 23.8 mmwas pernoticed year. at However, Atsimo Atsinanana Atsimo Atsinanana located on was the not East a region coast, withwhere the rainfall lowest has warming decreased or a about decreasing 23.8 mm annual per year. temperature, However, so Atsimo it implied Atsinanana the inﬂuence was ofnot elevation a region onwith the the change lowest of warming rainfall. Theor a lowest decreasing change annual was noticedtemperature, at Atsimo so it Andrefana,implied the a influence region located of elevation on the Southon the coast change with of onlyrainfall. 0.1 mmThe perlowest year change of decrease. was noticed at Atsimo Andrefana, a region located on the South coast with only 0.1 mm per year of decrease.

Figure 5. Results of the M-K test for annual rainfall in each region of Madagascar. Figure 5. Results of the M-K test for annual rainfall in each region of Madagascar. 3.2.3. Correlation between Elevation and Annual Rainfall Variation 3.2.3. Correlation between Elevation and Annual Rainfall Variation According to the polynomial regression results, the annual rainfall and elevation have a strong co-relationshipAccording (Rto 2the= 0.49,polynomialp < 0.05) regression (Figure6 results,a). We the found annual that rainfall along the and climatological elevation have gradient, a strong 2 theco-relationship annual rainfall (R increased = 0.49, p in< 0.05) the wet (Figure region 6a). (mean We annualfound rainfall:that along 1000–2000 the climatological mm per year), gradient, revealing the theannual phenomenon rainfall increased of the “wet in the becomes wet region wetter”. (mean Yet, annual we alsorainfall: noticed 1000–2000 that the mm wettest per year), region revealing (mean annualthe phenomenon rainfall more of the than “wet 2000 becomes mm per wetter”. year) located Yet, we at a also lower noticed altitude that was the subject wettest to region a decrease (mean in rainfall,annual rainfall suggesting more the than influence 2000 mm of altitudeper year) on located the change at a lower in rainfall. altitude Thus, was we subject classified to a decrease the annual in rainfallrainfall, trend suggesting of Madagascar the influence according of altitude to altitude on the (Figure change7). in The rainfall. two first Thus, regions we classified are located the at annual more thanrainfall 850 trend m and of beneathMadagascar 200 m according of altitude, to altitude associated (Figure with rainfall7). The two changes first regions more than are located5 mm per at more year. ± Thethan third 850 m and and fourth beneath categories 200 m of are altitude, the regions associated with a with rainfall rainfall trend changes between more5 and than 0.5±5 mm mm per per year. year, ± ± regionsThe third between and fourth 200 categories m to 450 m are of the elevation regions had with a decreasinga rainfall trend amount between of rainfall, ±5 and and±0.5 thosemm per located year, betweenregions between 500 m to 200 850 m m to had 450 an m increasing of elevation amount had a ofdecreasing rainfall. The amount last category of rainfall, is theand region those locatedlocated betweenbetween 450500 m and to 500850 mm altitude,had an increasing characterized amount by theof rainfall. smallest The change last category observed is betweenthe region 0.5located and − +between0.5 mm per450 year.and 500 It showed m altitude, that characterized the region in theby the central smallest part ofchange the country observed experienced between -0.5 a rise and while +0.5 themm regions per year. in It the showed coast sidethat experiencedthe region in athe declining central part annual of the rainfall. country A similarexperienced result a wasrise foundwhile the by Yaoregions et al. inand the Zeng coast et side al. [experienced20,22] with a a positive declining correlation annual rainfall. between A rainfall similar andresult elevation. was found This by result Yao iset importantal. and Zeng for et planning al. [20,22] climate-related with a positive activities correlation such asbetween rain-fed rainfall and irrigation and elevation. agriculture This andresult can is beimportant scaled up for to planning other studies climate-related in the African activities continent such [39 as]. rain-fed Furthermore, and irrigation even if annual agriculture rainfall and is notcan directlybe scaled correlated up to other with studies soil erosion in the African and infiltration continent [ 65[39].], this Furthermore, change in annualeven if rainfallannual rainfall according is not to thedirectly elevation correlated will have with a soil diff erenterosion eff ectand on infiltration soil and hydrology [65], this change along the in annual climatological rainfall gradientaccording and to altitude,the elevation as reported will have by a [66 different]. effect on soil and hydrology along the climatological gradient and altitude, as reported by [66].

Water 2020, 12, x FOR PEER REVIEW 9 of 16 Water 2020, 12, 3582 9 of 15 Water 2020, 12, x FOR PEER REVIEW 9 of 16

Figure 6. Trend magnitude of (a) annual rainfall, (b) summer rainfall and (c) winter rainfall according to elevation. Open circles, dark panels, light panel, and solid line indicate calculated values, 95% FigureconfidenceFigure 6. Trend 6. band,Trend magnitude 95% magnitude prediction of (a) annual of band, (a) rainfall, annual and best rainfall,(b )curve-fit summer (b )line, rainfall summer respectively. and rainfall (c) winter and rainfall (c) winter according rainfall to accordingelevation. toOpen elevation. circles, Open dark circles, panels, dark light panels, panel, light and panel, solid andline solidindicate line indicatecalculated calculated values, values,95% confidence 95% confidence band, 95% band, prediction 95% prediction band, and band, best and curve-fit best curve-fit line, respectively. line, respectively.

Figure 7. Observed change of rainfall according to elevation in Madagascar. Figure 7. Observed change of rainfall according to elevation in Madagascar. 3.3. The Phenomenon of Wet becomes Wetter and the Warmer Becomes Wetter 3.3. The PhenomenonFigure 7.of ObservedWet becomes change Wetter of rainfall and the according Warmer Becomesto elevation Wetter in Madagascar. 3.3.1. Changes in Regional Seasonal Rainfall 3.3.1. Changes in Regional Seasonal Rainfall 3.3. TheThe Phenomenon analysis of of Wet the becomes seasonal Wetter rainfall and revealedthe Warmer that Becomes during Wetter summer, except for the regions of IhorombeThe analysis (8.91 mm of per the year), seasonal Menabe rainfall (0.32 mmrevealed per year), that andduring Sofia summer, (8.48 mm except per year) for withthe regions significant of 3.3.1. Changes in Regional Seasonal Rainfall Ihorombeincreasing (8.91 rainfall, mm and per Matsiatra year), Menabe Ambony (0.32 ( 6.97 mm mm per peryear), year) and with Sofia decreasing (8.48 mm rainfall, per year) the otherwith − significantregionsThe analysis had increasing a non-significant of the rainfall, seasonal change and rainfall Matsiatra in rainfall revealed Ambony (Figure that8 a).(during−6.97 In winter, mm summer, per only year) twoexcept with regions for decreasing the had regions a significant rainfall, of Ihorombethetrend, other an (8.91 regions increasing mm had per one a non-significantyear), for Menabe Menabe (0.66 change(0.32 mm mm perin rainfall per year) year), in (Figure the and West 8a). Sofia and In winter,(8.48 a decreasing mm only per two one year) regions for Atsimowith had significantAntsinananaa significant increasing trend, ( 11 mm anrainfall, perincreasing year) and inMatsiatra one the for East Menabe Ambony (Figure (0.668b). (−6.97 mm mm per per year) year) in thewith West decreasing and a decreasing rainfall, − theone other for regionsAtsimo hadAntsinanana a non-significant (−11 mm change per year) in rainfall in the East (Figure (Figure 8a). In8b). winter, only two regions had a significantIn summer, trend, 14an out increasing of 22 of onethe regionsfor Menabe presented (0.66 mman increasing per year) raininfall, the West and andduring a decreasing winter, this onerate for was Atsimo inversed Antsinanana with 13 regions (−11 mm out per of year)22 with in athe decreasing East (Figure rainfall. 8b). In summer, 10 out of 14 regions In summer, 14 out of 22 of the regions presented an increasing rainfall, and during winter, this rate was inversed with 13 regions out of 22 with a decreasing rainfall. In summer, 10 out of 14 regions

Water 2020, 12, x FOR PEER REVIEW 10 of 16

with increasing rainfall are located above 400 m altitude. The highest increase in rainfall was recorded in Itasy with 12.83 mm per year suggesting the influence of temperature and elevation over the rainfall pattern as the region is among the highest increase of summer temperature. The summer rainfall indicated a change according to the elevation—it decreased for the area below 400 m and increased for the area above 400 m (Figure 6b). In winter, the rainfall patterns mainly declined, and the highest decrease was detected in Atsimo Atsinanana with −11.23 mm per year. These findings are in line with a study by Tadross et al. [17], who also detected a reduction of rainfall of Madagascar in winter. However, the influence of warming and elevation on rainfall in winter was not evident. Similar results were reported by Zeng et al. [22], who found a different influence of elevation on seasonal rainfall and different fluctuations in trend with elevation. This finding suggested the existence of wet-becomes-wetter and warmer-becomes-wetter in Madagascar. Because of the human-induced climate change, scientists have been projecting that rainfall patterns will shift. Hence, there will be a seasonally dependent response in rainfall patterns to global warming [67]. As summer gets warmer and is a wet season in Madagascar, it gets more rainfall. A declining rainfall trend in some African countries for both seasons was reported [58]. However, they also discovered different seasonal rainfall variations. In Kenya and Tanzania, for Waterexample,2020, 12 ,rainfall 3582 declined in winter and increased during the summer, similar to the findings of 10this of 15 study.

FigureFigure 8. 8.( a()a) Summer Summer andand ((bb)) winterwinter rainfall trend trend for for ea eachch region region in in Madagascar. Madagascar.

3.3.2.In summer,Correlation 14 outbetween of 22 Rainfall, of the regions Temperature, presented and an Elevation increasing rainfall, and during winter, this rate was inversed with 13 regions out of 22 with a decreasing rainfall. In summer, 10 out of 14 regions with We used the multivariate polynomial regression to understand rainfall variation according to increasing rainfall are located above 400 m altitude. The highest increase in rainfall was recorded in the increase in temperature and elevation. We added other variables (such as relative humidity, wind, Itasy with 12.83 mm per year suggesting the influence of temperature and elevation over the rainfall sea surface temperature, soil moisture) that might influence rainfall to the model [59,68] but only patterntemperature as the and region elevation is among were the significant. highest increaseFurthermore, of summer adding them temperature. to the model The did summer not improve rainfall indicatedthe fit of a the change model. according To avoid to multicollinearity, the elevation—it decreasedwe used the for average the area temperature below 400 mtrend and (average increased of for theΔ areaTmax above and Δ 400Tmin). m (Figure The model6b). indicated that annual warming and elevation influenced the annual rainfall.In winter, The model the rainfall that best patterns fits our mainly data declined,is a model and with the second-order highest decrease temperature was detected and first-order in Atsimo Atsinananaelevation (R with2 = 0.6,11.23 p < 0.05). mm perFigure year. 9 is These a contour findings plot of are the in relationship line with a between study by rainfall Tadross variation, et al. [17 ], − who also detected a reduction of rainfall of Madagascar in winter. However, the influence of warming and elevation on rainfall in winter was not evident. Similar results were reported by Zeng et al. [22], who found a different influence of elevation on seasonal rainfall and different fluctuations in trend with elevation. This finding suggested the existence of wet-becomes-wetter and warmer-becomes-wetter in Madagascar. Because of the human-induced climate change, scientists have been projecting that rainfall patterns will shift. Hence, there will be a seasonally dependent response in rainfall patterns to global warming [67]. As summer gets warmer and is a wet season in Madagascar, it gets more rainfall. A declining rainfall trend in some African countries for both seasons was reported [58]. However, they also discovered different seasonal rainfall variations. In Kenya and Tanzania, for example, rainfall declined in winter and increased during the summer, similar to the findings of this study.

3.3.2. Correlation between Rainfall, Temperature, and Elevation We used the multivariate polynomial regression to understand rainfall variation according to the increase in temperature and elevation. We added other variables (such as relative humidity, wind, sea surface temperature, soil moisture) that might influence rainfall to the model [59,68] but only temperature and elevation were significant. Furthermore, adding them to the model did not improve the fit of the model. To avoid multicollinearity, we used the average temperature trend (average of ∆Tmax and ∆Tmin). The model indicated that annual warming and elevation influenced the annual rainfall. The model that best fits our data is a model with second-order temperature and first-order Water 2020, 12, x FOR PEER REVIEW 11 of 16 average annual temperature variation, and elevation. The maximum increase in rainfall was associated with the highest temperature rise and highest elevation (upper left and right in Figure 9). Beyond an increase in temperature of 0.035 °C per year, the rainfall was uniformly rising at all Water 2020, 12, 3582 11 of 15 elevations. The increase in temperature between 0.035 to 0.04 °C per year corresponded to an increase in rainfall of +5 mm per year to +10 mm per year. Warming of 0. 04 to 0. 045 °C per year corresponded toelevation an increase (R2 =in0.6, rainfallp < 0.05). of +10 Figure to +159 mm is a per contour year. plot However, of the relationshipbelow 0.03 °C between per year rainfall of warming, variation, the increaseaverage annualin rainfall temperature was important variation, at a higher and elevation. elevation The and maximum the rainfall increase decreased in rainfall at a lower was associated elevation with[22]. Decrease the highest in temperaturerainfall about rise more and than highest −5 mm elevation per year (upper was observed left and rightat the in region Figure below9). Beyond 200 m andan increase between in 0 temperatureand −5 mm per of 0.035 year ◦forC perthe year,region the below rainfall 800 wasm for uniformly 0.02 °C per rising year at warming. all elevations. The increaseThe increase in temperature in temperature leads between to an increase 0.035 to in 0.04 rainfall◦C per and year it correspondedsuggested a direct to an influence increase inofrainfall global warmingof +5 mm on per rainfall year to [64].+10 As mm a result per year. of global Warming warming, of 0. 04we tofound 0. 045 that◦C the per annual year corresponded temperature in to Madagascaran increase inincreased rainfall along of +10 the to elevation.+15 mm perConseque year. However,ntly, the higher below increase 0.03 ◦C in per yearly year temperature of warming, atthe higher increase elevation in rainfall resulted was in important a significant at aincrease higher in elevation water vapor and at the high rainfall altitudes decreased and a subsequent at a lower increaseelevation in [22 annual]. Decrease rainfall in for rainfall the region about moreat a higher than altitude5 mm per [20]. year Nevertheless, was observed an atincrease the region in annual below − temperature200 m and between (more than 0 and 0.035 °C) mm increased per year water for the vapor region regardless below 800 of melevation; for 0.02 hence,C per yearthe increase warming. in − ◦ annualThe increase rainfall in at temperature any altitude. leads The to change an increase in rainfall in rainfall discovered and it in suggested this study a directis the result influence of combined of global warmingcomplex topographical on rainfall [64 and]. As warming a result effects, of global leading warming, to a change we found in hydrology. that the annual The change temperature in rainfall in isMadagascar expected to increased result in alongflooding the at elevation. a higher Consequently,altitude and drought the higher at a increase lower altitude in yearly [69]. temperature at higherOur elevation findings resulted provide in relevant a significant information increase to in understand water vapor climate at high change altitudes and and global a subsequent warming forincrease a better in annual understanding rainfall for of the the region earth’s at a climate. higher altitude This research [20]. Nevertheless, provides spatial an increase and intemporal annual variabilitytemperature of (morerainfall than and 0.03 temperature◦C) increased in a watertropical vapor climate regardless where ofhydroclimatic elevation; hence, study the is crucial increase but in stillannual lacking. rainfall It is at worth any altitude. noting Thethat change we did in not rainfall find a discovered significant in influence this study of is elevation the result and of combined seasonal warmingcomplex topographicalon seasonal rainfall. and warming Thus, deep effects, investigation leading to a into change the ininfluence hydrology. of seasonal The change warming in rainfall on seasonalis expected rainfall to result needs in floodingto be done at in a higherfuture altitudeworks. and drought at a lower altitude [69].

Figure 9. Contour plot of rainfall variation, temperature, and elevation. Figure 9. Contour plot of rainfall variation, temperature, and elevation. Our findings provide relevant information to understand climate change and global warming for 4.a betterConclusions understanding of the earth’s climate. This research provides spatial and temporal variability of rainfallThe impact and temperature of global inwarming a tropical on climate rainfall where was dependent hydroclimatic on studyelevation. is crucial Under but warming still lacking. of temperatureIt is worth noting in Madagascar, that we did the not increase find a significant in rainfall influencewas prominent of elevation at a higher and seasonalelevation warming than lower on elevation.seasonal rainfall. However, Thus, with deep an investigationincrease in temperature into the influence more than of seasonal 0.03 °C warming per year, on rainfall seasonal increased rainfall bothneeds in to lower be done and in higher future elevated works. regions. Furthermore, the phenomena of “wet-becomes-wetter” and “warmer-becomes-wetter” is existent in Madagascar with an important increase in rainfall in summer4. Conclusions compared to winter. The knowledge of elevation dependence of the impacts of warming on rainfallThe is impactimportant of globalfor water warming resource on management rainfall was dependentand climate on change elevation. adaptation Under strategies warming at ofa localtemperature level. Our in Madagascar,research provides the increase relevant in rainfallinformation was prominentfor research at on a higher climate elevation change thanand global lower warming,elevation. which However, helps with for a an better increase understanding in temperature of th moree earth’s than climate. 0.03 ◦C Our per findings year, rainfall can be increased used as aboth tool in for lower policymakers and higher or elevated a basis for regions. climate Furthermore, simulation thein other phenomena regions ofwith “wet-becomes-wetter” similar climates as and “warmer-becomes-wetter” is existent in Madagascar with an important increase in rainfall in summer compared to winter. The knowledge of elevation dependence of the impacts of warming on rainfall is important for water resource management and climate change adaptation strategies at Water 2020, 12, 3582 12 of 15

a local level. Our research provides relevant information for research on climate change and global Waterwarming, 2020, 12 which, x FOR helpsPEER REVIEW for a better understanding of the earth’s climate. Our findings can be12 used of 16 as a tool for policymakers or a basis for climate simulation in other regions with similar climates as Madagascar.Madagascar. While for regions withwith aa didifferentfferent climate climate than than our our study study area, area, our our results results can can be be used used to toprove prove that that the the impact impact of global of global warming warming on rainfall on ra isinfall unevenly is unevenly distributed. distri Thebuted. influence The influence of seasonal of seasonalwarming warming on seasonal on rainfallseasonal was rainfall not evident was not in evident this study. in this Thus, study. we suggestThus, we future suggest works future to deepenworks tothe deepen understanding the understanding of the phenomenon of the phenomenon of “wet-becomes-wetter” of “wet-becomes-wetter” and “warmer-becomes-wetter” and “warmer-becomes- in wetter”another in tropical another region tropical through region high-resolution through high-resolution climate modeling. climate modeling. Author Contributions: Conceptualization, M.F.R.T. and J.Z.; Methodology, M.F.R.T, J.Z., and T.M.A.; Validation,Author Contributions: M.F.R.T, J.Z.,Conceptualization, T.M.A., and J.C.; M.F.R.T. Formal and Analysis, J.Z.; Methodology, J.Z. and T.M.A.; M.F.R.T., Resources, J.Z., and T.M.A.;M.F.R.T Validation, and J.Z.; M.F.R.T., J.Z., T.M.A., and J.C.; Formal Analysis, J.Z. and T.M.A.; Resources, M.F.R.T. and J.Z.; Writing—Original Writing—OriginalDraft Preparation, M.F.R.T.,Draft Preparation, T.M.A., and M.F.R.T, J.Z.; Writing—Review T.M.A., and J.Z.; & Editing,Writing—Review M.F.R.T., T.M.A., & Editing, and J.Z.;M.F.R.T, Visualization, T.M.A., andT.M.A., J.Z.; J.Z.,Visualization, and J.C.; Supervision, T.M.A., J.Z., J.Z.,and T.M.A.,J.C.; Supervision, and J.C.; Funding J.Z., T.M.A., Acquisition, and J.C.; J.Z. Funding All authors Acquisition, have read J.Z. andAll authorsagreed tohave the read published and agreed version to ofthe the published manuscript. version of the manuscript.

Funding:Funding: ThisThis work work was was funded funded by by the the National National Natural Natural Science Science Foundation Foundation of China (No. 51878093), 51878093), National National KeyKey R&D Program of China (2018YFB2101001-03), and and Ch Chongqingongqing Technology Technology Innova Innovationtion and Application Demonstration Special Project—Key Research And Development Projects Related to People’s Livelihood Demonstration(cstc2018jscx-mszdX0087, Special Project—Key cstc2018jszx-336 Research zdyfxmX0009). And Development Projects Related to People’s Livelihood (cstc2018jscx-mszdX0087, cstc2018jszx-336 zdyfxmX0009). Conﬂicts of Interest: The authors declare no conﬂict of interest. Conflicts of Interest: The authors declare no conflict of interest. Appendix A Appendix A

FigureFigure A1. A1. LocalizationLocalization of of the the study study area area with with the the name name of of regions regions used used in in this this study. study.

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