852 © IWA Publishing 2019 Journal of Water and Climate Change | 10.4 | 2019
Trends and changes detection in rainfall, temperature and wind speed in Burundi Agnidé Emmanuel Lawin, Célestin Manirakiza and Batablinlè Lamboni
ABSTRACT
This paper assessed the potential impacts of trends detected in rainfall, temperature and wind speed Agnidé Emmanuel Lawin (corresponding author) Laboratory of Applied Hydrology, National Water on hydro and wind power resources in Burundi. Two climatic stations located at two contrasting Institute, University of Abomey-Calavi, regions, namely Rwegura catchment and northern Imbo plain, were considered. Rainfall, P.O. Box 2041, Calavi, temperature and wind speed observed data were considered for the period 1950–2014 and future Benin E-mail: [email protected] projection data from seven Regional Climate Models (RCMs) for the period 2021–2050 were used. Célestin Manirakiza The interannual variability analysis was made using standardized variables. Trends and rupture were Batablinlè Lamboni – – Institute of Mathematics and Physical Sciences, respectively detected through Mann Kendall and Pettitt non-parametric tests. Mann Whitney and University of Abomey-Calavi, Kolmogorov–Smirnov tests were considered as subseries comparison tests. The results showed a 01 BP 613, Porto-Novo, Benin downward trend of rainfall while temperature and wind speed revealed upward trends for the period 1950–2014. All models projected increases in temperature and wind speed compared to the baseline period 1981–2010. Five models forecasted an increase in rainfall at northern Imbo plain station while four models projected a decrease in rainfall at Rwegura station. November was forecasted by the ensemble mean model to slightly increase in rainfall for both stations. Therefore, the country of Burundi may benefit more if it plans to invest in wind power. Key words | Burundi, changes, climatic variables, trends
INTRODUCTION
Energy is the foundation of human society development. north and east of New Zealand (Salinger & Griffiths ), Industrial civilization was built around the exploitation of South Asia and parts of the central Pacific(Griffiths et al. coal at the end of the 18th century and on fossil fuel in the ), Poland (Bielec ) and in an ecological reserve in middle of the 20th century. In developing countries, the popu- the Federal District of Brazil (dos Santos ). On the other lation generally uses wood energy, which contributes to the hand, other studies showed increasing trends in rainfall in Bul- destruction of the environment (Ramade ). One of the garia (Boers et al. ), France (Cantet ), China (Zhai major challenges faced by the governments of those countries et al. ), Germany (Tomassini & Jacob ), South Caro- is to produce less expensive energy which at the same time lina (Powell & Keim ), south-western Montenegro (Burí meets the standards of environmental protection. Thus, renew- et al. ) and Brazil (Sugahara et al. ). Likewise, studies able energies in general, and in particular hydro and wind conducted in Portugal (de Lima et al. ), Brazil (Sugahara power, have become a solution. However, these types of et al. ) and the Netherlands revealed no significant energy are linked to the trends of climatic variables (Yao trends in rainfall. Concerning temperature, the Fourth Assess- et al. ), which is problematic. Recently, several studies ment Report (AR4) of the Intergovernmental Panel on Climate revealed decreasing trends in rainfall in Iran (Talaee ; Change (IPCC) () reported that since the early 20th cen- Talaee et al. ), Kerala India (Rajeevan et al. ), the tury the global mean surface temperature increased by 0.8 C.
doi: 10.2166/wcc.2018.155
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On a regional scale, many scientific papers show some while the period 2002–2012 was characterized by a chronic different gradients. Zhao et al. () reported that mean sur- shortage of energy in general, and electrical energy in par- face air temperature in Eastern China increased by 1.52 C ticular. Thus, since 2006, the electricity grid deficit is over the last 100 years. At the same scales, Martinez et al. manifested by load shedding, and this electricity deficit is (), Karmeshu () and Ceppi et al. () revealed estimated at about 25 MW in the dry season. Few studies increasing trends in temperature for Florida, several north- have been conducted in this country concerning climatic eastern states and Sweden, respectively. However, Karme- parameters trends and effects on hydro and wind power gen- shu () showed no significant trend in temperature in eration. Joel et al. () developed a method that can be two of nine states studied in the Northeastern United States. used to assess the hydrology in periods of low flow but the Considering wind speed, the study made in Iran at the paper did not assess the impact of climatic variables Esfehan station (Fatemeh et al. ) revealed the decreasing trends either on hydropower or wind power. Therefore, trend of the mean annual wind speeds during the period this paper aims to revisit rainfall, temperature and wind 1961–2005. On the other hand, the study conducted in speed variability by considering two stations from two con- east Africa, in Tanzania (Mahongo et al. ), indicated trasting regions of Burundi; one station at low altitude that trends in the monthly mean and maximum wind (774 m) at Imbo north plain and the other station at high speeds have generally strengthened over the past three altitude (1,800–2,621 m) at Rwegura catchment. Thus it decades, with the corresponding rates of increase at about fills the gap by assessing historical trends in climate variabil- – – 0.04–0.07 and 0.03–0.08 m·s 1·y 1, respectively. ity as well as future changes for the period 2021–2050 to the Indeed, these results concerning climate parameter considered climate parameters at Rwegura station and Imbo trends led the research to continue analyzing their effects north plain. Specifically, the study focuses on detecting on hydro and wind power generation since there is no homo- trends in the rainfall and interannual temperature variability geneous trend. For that matter, Shamin et al. () reported at Rwegura, and repeats the same analysis at the Imbo north that since 1970 the annual energy production of some Euro- plain, adding wind speeds. pean hydropower stations has decreased. Hamududu & Killingtveit () revealed that these reductions are generally attributed to the decrease in average flow due to climatic DATA AND METHODS variability. According to Yao et al. (), in northern Europe, Finland, the potential for wind energy could increase Study area by 2–10% under conditions of climate change, while in the north-west of the United States (US), summer wind speeds Figure 1 shows the study area location in Burundi, an east may decrease by 5–10%, suggesting a 40% reduction in the African country between longitudes 28.8 and 30.9 east potential of US summer wind power generation. and latitudes 2.3 and 4.45 south (Bidou et al. ). The study made in southern Africa, in Angola at Bounded to the north by Rwanda, to the east and south by Kwanza River Basin (Hamududu & Killingtveit ), Tanzania and to the west by the Democratic Republic showed that increasing trends in rainfall and consequently of Congo, Burundi covers an area of 27,834 km2 (Ministry the increase of water resources would lead to the increase of Water Resources, Environment & Land Management of of hydropower production potential in the basin. Burundi ). The annual rainfall in the high altitude With regard to the impact of climate trends on hydro- regions is almost double that of low altitude regions. Aver- power and wind power, the central Africa region age maximum annual temperatures range from 21.8 to (Hamududu & Killingtveit ) has not yet been extensively 29.5 C. The average wind speeds vary between 4 and investigated. 6 m·s 1. The studied station at Rwegura catchment is In Burundi, the crucial challenge is that in 2004–2005 entirely located in Kibira national park of Burundi, at two provinces in the north of the country (Kirundo and 29.5 longitude east and 3 latitude south, where climate is Muyinga) were severely hit by famine following the drought, tropical. The northern Imbo plain site is in the west of
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Figure 1 | Study area location.
Burundi, to the north coast of Lake Tanganyika at 29.32 on the quality of the data collected from various stations longitude east and 3.32 latitude south, and contains a at various times as presented in Table 1. However, only part of the Rukoko savanna nature reserve. two principal stations, Rwegura and Bujumbura Airport (located at Imbo north plain), where climatic data are Data used hourly collected, were selected for the period 1950– 2014. The annual rainfall, temperature and wind speed Two sets of data were used in this study. The first set data collected over this period of more than 60 years groups observed data collected from the synoptic stations were used, as recommended by the World Meteorology of Burundi belonging to the Geographical Institute of Bur- Organization, for a climate study. Missing observed data undi (IGEBU), completed using Food and Agriculture have been filled using cross validation methods. Table 2 Organization climate data. The IGEBU also has in its gives a statistical summary of the used data on an archives all historical meteorological data collected annual scale. throughout the country and grouped according to each The data on wind speeds presented in Table 2 were col- climatological network. Each study area contains meteor- lected at a height of 12 m and, during the whole period of ological stations (Figure 1) which provide daily study, the dominant direction of the wind was south. information on rainfall, temperature and wind speed. The second set of used data groups daily rainfall, temp- The concise information on climate stations considered erature and wind speed from seven regional climate models for the period of study, 1950–2014, are presented in (RCMs). These data are available in the context of the COor- Table 1. The records vary in their lengths, start and end dinated Regional Climate Downscaling Experiment dates.Theselectionofthetimeseriesdatawasbased (CORDEX) over Africa at 0.44 resolution for the period
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Table 1 | Geographic characteristics of the meteorological stations used
Station name ID (IGEBU) Lon.a Lat.b Period Dc (%) Altitude (m)
Gatara 10036 29.65 2.98 1931–1998 75 1806 Munanira 10106 29.57 2.92 1954–2002 75 2120 Muyange 10126 29.62 2.92 1970–1987 26.56 1930 Ndora 10133 29.40 2.92 1936–1991 64.1 2120 Rwegura 10164 29.52 2.92 1950–2014 100 2302 Teza 10167 29.57 3.18 1963–1996 51.56 2166 Bujumbura (Airport) 10011 29.32 3.32 1927–2014 100 783 Bujumbura (Kamenge) 10014 29.40 3.35 1977–2000 36 890 Gifurwe 10037 29.40 3.18 1979–1983 6.25 872 Gihanga 10038 29.30 3.20 1952–1986 53 819 Imbo (Sems) 10052 29.35 3.18 1971–2014 67 820 Kivoga 10080 29.42 3.28 1935–1990 62.5 877 Maramvya 10087 29.32 3.27 1977–1992 23.43 785 Mpanda 10094 29.40 3.17 1977–1986 25 890 Murukaramu 10115 29.32 3.30 1970–1984 22 785 Musenyi (Bubanza) 10118 29.40 3.22 ––882 Rukoko 10151 29.22 3.25 1973–1995 34.4 790
aLon: longitude east. bLat: latitude south. cD: time period in percentage included in the period of study.
Table 2 | Description of data collected at Rwegura and at northern Imbo plain upon the period 1950–2014
Variable Station Minimum Maximum Mean Standard deviation
Rainfall (mm) Rwegura 1,012 2,073 1,658.15 237.55 Northern Imbo plain 212 1,213 802.18 217.24 TMa Rwegura 13 17.70 16.28 1.13 Northern Imbo plain 20 26.42 24.39 1.60 Tminb Rwegura 10.26 12.40 11.60 0.61 Northern Imbo plain 16.79 20.02 18.90 0.85 Tmaxc Rwegura 19 20.97 20.18 0.64 Northern Imbo plain 27.03 29.96 29.01 0.69 WSd Rwegura –– – – Northern Imbo plain 2.7 5.8 3.7 0.84
aTM ¼ Mean temperature ( C). bTmin ¼ Minimum temperature ( C). cTmax ¼ Maximum temperature ( C). dWS ¼ Wind speed (m.s–1).
1950–2100 (Giorgi et al. ) and can be accessed online In this study, only experiments performed following the (www.cordex.org) through user registration. Table 3 shows most extreme IPCC Representative Concentration Pathways the used climatic models, with the third column giving scenario (RCP8.5) for the period 2021–2050 in the their acronyms adopted in this paper. CORDEX database have been considered. The RCP8.5 is
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Table 3 | Used climatic models determine whether the distribution functions of two popu- lations are identical. The test statistic D is defined by: Institute ID Global climate model name Model short name
CNRM-CERFACS CNRM-CM5 CNRM ¼ j j D supx T1(x) T2(x) (1) ICHEC EC-EARTH ICHEC IPSL IPSL-CM5A-MR IPSL MIROC MIROC5 MIROC where T1(x) is the empirical distribution function of the time MPI-M MPI-ESM-LR MPI-M subseries (x1, … ,xn) and T2(x) the empirical distribution of NCC NorESM1-M NCC the time subseries (y1, … ,yn). NOAA-GFDL GFDL-ESM2M NOAA The null hypothesis H0, which assumes there is no difference between the two distributions, is rejected at the based on the A2r scenario which combines assumptions significance level α if D is greater than the value of the Smir-
about high population and relatively slow income growth nov’s table tn,m,1–α with n, m and (1 – α) parameters. with modest rates of technological change and energy inten- sity improvements, leading in the long-term to high energy Interannual variability analysis demand and greenhouse gas emissions in the absence of cli- mate change policies (Riahi et al. ). This set of data is For each variable, the discrimination of surplus years and the most up-to-date ensemble of high-resolution RCM** pro- deficit years was determined using the standardized vari- jections. RCM historical data have been used for bias able index (I)createdbyMcKee et al. () and can be correction. found in many articles (Lawin et al. ; Soro et al. ):
METHODS ¼ Xi Xm I(i) σ (2) Trend detection where Xi, Xm and σ are the value for the year i,theaver- For trend detection, the Mann–Kendall (MK) non-para- ageandthestandarddeviationofthetimeseries, metric test (Mann ; Kendall ) was used. Break respectively. points were detected through Pettitt (). These tests are Thus, in this work, a year will be considered normal if used worldwide in hydro meteorological variables trend its index is included between 0.5 and þ0.5. It will be in detection (Rajeevan et al. ; Karmeshu ; Telemu surplus if its index is greater than þ0.5 and deficit is ; Talaee ; Talaee et al. ). In cases where a below 0.5. This interval is criticized since it is relatively break point existed, Mann–Whitney (MW) and Kolmo- weak. However, it clearly makes it possible to discriminate gorov–Smirnov (KS) non-parametric tests were used to test the deficit years from surplus years. The standardized the two time subseries (before and after the break point). variable index also makes it possible to analyze the Those tests are used to measure the mean equality of the interannual variability of the key variable at considered two time subseries and thus prove whether the difference spatial scales. between the two means was significant or not. Details of the Mann–Whitney test can be found in many Future climate analysis papers (Mann & Whitney ; Vincent & Gilles ). It tests whether one of two subseries is stochastically larger The comparison of the changes on future climatic variables than the other. were carried out with respect to the baseline period (1981– The second mean equality test (KS (Rice ; Vincent 2010) and referred to as the current or historical period. & Gilles )) is a non-parametric adequacy test to Delta change factors were computed and added to daily
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time series of observation records in order to disaggregate MJJASO are characterized by low rainfall at Rwegura and the local climate projection into a daily time step. Calcu- Imbo, respectively. These months of low rainfall correspond lations of delta change factors were based on the to the dry season in Burundi where highlands are ranked in climatology of a selected projection interval (2021–2050) the climatic zone, recording three dry months while the low- and a baseline period (1981–2010) using Equations (3) and lands count six dry months. Analysis of the figure shows a (4). Change factors for temperature and wind speed were minimum of rainfall in February which is explained by the determined by: small dry season. In September 1986, a power plant was built in Rwegura Δω ¼ ω ω i,j,k i,j,k ref,k (3) catchment on Kitenge River. Recently, the water level in the Rwegura reservoir has where Δω is the change factors ( C or m/s), ω is the monthly decreased and this has resulted in a reduction in generated mean ( C or m/s), i is the RCM, j is the projection period, ref hydropower and hence, in the dry season hydropower is the reference period and k is the month. rationing is difficult, especially in the capital Bujumbura Rainfall change factors were computed as follows: located in northern Imbo plain. There is thus a fundamental need to find a climate variable having an opposite trend to P P the rainfall. Δ ¼ i,j,k ref,k × Pi,j,k 100 (4) Pref,k Figure 3 shows the rainfall pattern at Rwegura and at Imbo north plain stations. The standardized rainfall indices where ΔP is the rainfall change factor (%), P is the mean divide the rainfall pattern into three time subseries including monthly rainfall. the period 1950–1973 (24 years) where there is no deficit year, the period 1974–1992 (19 years) which is a mixture of deficit and surplus years, and the period 1993–2014 RESULTS AND DISCUSSIONS (22 years) where there is no surplus year. From 1950 to 2014, there are 19 surplus years at Rwegura against 24 Rainfall trend surplus years at Imbo north plain, and 14 deficit years at Rwe- gura against 17 deficit years at Imbo north plain. Thus, the Figure 2 presents the monthly rainfall at Rwegura catchment period 1950–1973 has 78.95% of surplus years at Rwegura and northern Imbo plain. The consecutive months JJAS and against 66.67% of surplus years at northern Imbo plain. In a similar analysis, the period 1993–2014 has 77.78% of deficit years at Rwegura against 70.59% at Imbo. All other missing fractions which represent less than 40% for either surplus or deficit years are found in the second time subseries (the period 1974–1992). Overall, this analysis points out the decrease of surplus years and the increase of deficit years which may have effects on water availability. Table 4 shows the mean rainfall in the three time periods for Rwegura and Imbo north plain. The analysis shows that from the period 1950–1973 to 1974–1992, the mean rainfall decrease of 11.03% at Rwegura against 16.57% at Imbo north plain. From the period 1974–1992 to 1993–2014, the mean rainfall decrease of 11.21% at Rwe- gura against 19.65% at Imbo north plain. Overall, from the first period (period 1950–1973) to the last (1993–2014) the
Figure 2 | Monthly rainfall patterns at Rwegura and at northern Imbo plain. mean rainfall decrease of 21.01% at Rwegura against
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Figure 3 | Standardized rainfall index at Rwegura and at Imbo north plain stations (1950–2014).
’ Table 4 | Mean rainfall in the three time subseries where the Kendall s tau is 0.474 for Rwegura and 0.351 for Imbo north plain with a p-value of <0.0001 for the two Mean rainfall (mm) regions as given in Table 5 at the α ¼ 0.05 level. The Pettitt
Period Rwegura Imbo north plain test showed break points in 1978 and in 1990 at Imbo
1950–1973 1849 955 north plain and at Rwegura, respectively, as given in μ μ 1974–1992 1645 797 Figure 4, where 1 and 2 are the means of the time subseries 1993–2014 1461 640 before and after the break point, respectively. Consequently, Table 6 gives the results of the MW and KS tests applied upon the subseries where they all reported 32.97% at Imbo. These findings show that the mean rainfall that the decrease in rainfall for both the studied regions was in time periods declines at a higher rate at Imbo north plain significant at α ¼ 0.05 level. In Table 5, τ gives 318 mm or than at Rwegura. 17.90% and 270.212 mm or 28.39% as the mean rainfall On an annual scale, the MK test showed a decreasing decrease at Rwegura and at Imbo, respectively. The rate of trend in annual rainfall pattern for the two studied areas the rainfall decrease is higher (almost double) at Imbo
Table 5 | Subseries mean comparison before and after the break point
a Variable Station Kendall’s tau p-value μ1 μ1 τ
Rainfall (mm) Rwegura 0.474 <0.0001 1776 1458 318 Imbo north plain 0.351 <0.0001 951.834 681.622 270.212 TM Rwegura 0.590 <0.0001 15.254 17.008 1.754 Imbo north plain 0.606 <0.0001 23.281 25.606 2.325 Tmin Rwegura 0.424 <0.0001 11.150 11.943 0.793 Imbo north plain 0.451 <0.0001 18.214 19.410 1.196 Tmax Rwegura 0.540 <0.0001 19.743 20.604 0.861 Imbo north plain 0.473 <0.0001 28.561 29.447 0.886 WS Rwegura ––––– Imbo north plain 0.627 <0.0001 2.950 4.974 1.724
a τ ¼ |μ1–μ2| is the rate of decrease (when Kendall’s tau is negative) or the rate of increase (when Kendall’s tau is positive) of the climatic variable.
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north plain than at Rwegura. The downward trend in rainfall observed in this analysis is consistent with the findings in many studies. For instance, the mean annual rainfall in Tan- zania (Telemu ) has declined by about 4% between the periods 1981–1989 and 2000–2010. In central Kenya, Funk () reported that the total rainfall of the long rainy season has decreased by more than 100 mm since the mid-1970s and that this decline in rainfall is most likely due to warming in the Indian Ocean.
Temperature trend
Figure 5 presents the monthly temperature pattern at Rwe- gura (Figure 5(a)) and Imbo (Figure 5(b)). At Rwegura, Tmin shows minimum values in JJA while Tmax reaches its maximum value in September. TM does not show remarkable variations. For Northern Imbo plain station (Airport), TM and Tmax show maximum values in Septem- ber while Tmin reveals its minimum values in JJA, which is the dry season in Burundi. The analysis pointed out that the average temperature at Rwegura is almost three-quarters of Imbo north plain’s average temperature. The explanation for the difference in temperature gradient resides in the altitude separating the two zones, which varies between 1,026 and 1,847 m. Figure 4 | Break point year location at northern Imbo plain (a) and at Rwegura (b). Figure 6 shows the increase of excess TM at Rwegura and
Table 6 | Results from MW and KS tests at α ¼ 0.05 level
Test statistic p-value
Variable Station MW(D1)a KS(D2)b MW(ev)c MW KS
Rainfall (mm) Rwegura 895.5 0.780 492 <0.0001 <0.0001 Imbo north plain 922.5 0.689 522 <0.0001 <0.0001 TM Rwegura 34 0.926 513 <0.0001 <0.0001 Imbo north plain 42 0.850 527 <0.0001 <0.0001 Tmin Rwegura 146 0.679 518 <0.0001 <0.0001 Imbo north plain 100.5 0.714 518 <0.0001 <0.0001 Tmax Rwegura 109.5 0.656 528 <0.0001 <0.0001 Imbo north plain 107 0.602 528 <0.0001 <0.0001 WS Rwegura –––– – Imbo north plain 8.5 0.952 483 <0.0001 <0.0001
aMW(D1) ¼ difference of location between the samples computed by MW test. bKS(D2) ¼ maximum distance computed by KS test between the repartition function of the distribution before the break point and the distribution after the break point. cMW (ev) ¼ MW expected value.
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Imbo north plain in the period 1950–2014. The standardized temperature index revealed three subseries in TM time series. The first period is 1950–1966, where there is no year with TM greater than the mean. The second period, 1967–1976, is a mixture of two years with normal TMs, two years with TMs higher than the mean, and six years with TMs lower than the mean. The last period, 1977– 2014, has lower TM than the mean. Analysis of Table 7 shows that from the first period to the second, the TM increased by 0.65 and 0.64 C at Rwegura and at Imbo north plain, respectively. From the second period to the last, the TM increased by 1.35 and 2.07 C at Rwegura and at Imbo north plain, respectively. Overall, from the first period (1950–1966) to the last (1977–2014), the TM increased by 2 and 2.71 C at Rwegura and at Imbo north plain, respectively. The results of the MK test revealed that the increasing trend in temperature was significant at α ¼ 0.05 level for both study areas where the Kendall’s tau is equal to 0.590, 0.424 and 0.540 for TM, Tmin and Tmax at Rwegura, respectively, and 0.606, 0.451 and 0.473 for TM, Tmin and Tmax at Imbo north plain, respectively (their respective p- values are given in Table 5). The Pettitt test revealed the break points in 1976 and 1983 for TM at Rwegura and at Imbo north plain, respectively, as given in Figure 7(a) and 7(b). For Tmax and Tmin, break points were detected in 1981 and 1977 respectively for the two stations, as presented Figure 5 | Monthly temperature patterns at Rwegura (a) and at Imbo north plain (b). in Figure 7(c)–7(f).
Figure 6 | Standardized TM index at Rwegura and Imbo north plain (1950–2014).
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Table 7 | Average TM in the three time subseries The analysis shows that at Imbo north plain, the differ-
ence between μ2 of Tmax and μ2 of Tmin is smaller than Average TM the difference between μ1 of Tmax and μ1 of Tmin. This Period Rwegura Imbo north reveals the decrease of the distance between Tmax and 1950–1966 15.01 22.71 Tmin after the break point year. Table 6 gives the results 1967–1976 15.66 23.35 of the MW and KS tests applied upon the temperatures 1977–2014 17.01 25.42 time subseries. Overall, they all confirmed the upward trend in temperature patterns for the two study stations at
Figure 7 | Break point in TM, Tmax and Tmin at Rwegura and at Imbo north plain.
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a significance level of α ¼ 0.05. Furthermore, Table 5 gives Wind speed trend the rates of increase of the subseries temperatures (TM, Tmin and Tmax) mean after the break point. For Figure 8 shows the monthly wind speed pattern at Imbo instance, the values of τ for temperatures show that the north plain (Bujumbura Airport station) where the months TM mean before the break point (1976) increased by JJAS (June-July-August-September) reveal the highest wind 1.754 C after the break point at Rwegura, while at Imbo speed values of the year. This period matches with the dry north plain, the TM mean before the break point (1983) season in Burundi. The analysis of this output figure over increased by 2.325 C after the break point. The upward 65 years shows that mean wind speeds are optimized trend in temperature observed in this analysis is in during summer time and this may reveal the period when accordance with the findings in many studies which wind power turbines would expect to receive maximum show increases in temperature over the last century due wind speeds. to climate warming. Figure 9 shows the standardized WS pattern from 1950 to 2014 and reveals three parts of the time subseries. The first part, including 1950–1977, is characterized by lower wind speeds than the annual mean. In the second part, including 1978–1997, the standard indices reveal 11 normal years in WS and nine years with lower wind speed than the annual mean. In contrast with the first part, the third part, including the period 1998–2014, shows only higher wind speeds. The analysis of the three time periods in mean WS (Table 8) shows that from 1950–1977 to 1978–1997 the mean WS has strengthened by 0.34 m·s 1, whereas from 1978–1997 to 1998–2014 the rate of increase in mean WS is 2.07 m·s 1. It is clear that the mean WS in the last period is almost double that of the first period. The MK test recorded a significant increasing trend in WS at Imbo north plain station. The Kendall’s tau was
Figure 8 | Monthly WS at Imbo north plain. 0.627 for p-value <0.0001 at a significance level of α ¼
Figure 9 | Standardized WS index at Imbo north plain from 1950 to 2014.
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Table 8 | Mean WS in the three time subseries the increase in the wind speed observed, Nzigidahera () reported the devastation of vegetation in Rusizi Average WS national park around the studied station. In fact, the Period Rwegura Imbo north plain need for fire wood, agricultural fields, wooden materials 1950–1977 – 2.89 for house construction and exploitation of vegetation for 1978–1997 – 3.23 sales, especially hyphaene petersiana which was creating 1998–2014 – 5.3 a stable microclimate in the area, grew with the increase of the surrounding population (Gihanga, Gatumba, Rukaramu and Buringa), estimated at 9000 families in 0.05, as given in Table 5. The Pettitt test showed the break 1950. In addition, since 1993, the start of the socio politi- point in 1991 as given by Figure 10. cal crisis in Burundi, many families fled from other Furthermore, Table 6 shows the results from the MW localities to Gatumba and Gihanga, which caused a and KS tests applied upon the subseries before and after demographic explosion. For example, in 2005, Gihanga the break point. All of them confirmed the increasing had 50,792 inhabitants and in 2010 the population had trend in WS at Imbo north plain at α ¼ 0.05. increased by 17.8%. Therefore, the jump in observed The upward trend detected in wind speed in this study is wind speed may have been accelerated by the environ- consistent with the observations by Mahongo et al. () mental destruction. who recorded an increasing trend in monthly mean velocity Table 9 shows the average increase over a period of 10 at Zanzibar during 1985–2004. years in rainfall and temperature for both stations, and the This increasing trend in wind speed may be attributed to average increase in wind speed at Imbo north plain. The changes in global climate. According to AR4 of IPCC (Inter- average increase in the mean rainfall over the period of 10 governmental Panel on Climate Change ), mid-latitude, years is negative, showing the declining trend of the rainfall. westerly winds have strengthened in both hemispheres since Thus, lower rainfall and higher temperature due to climate the 1960s as a result of climate change. variability may impact water flow, extend the dry season, The analysis of Figures 9 and 10 pointed out two main increase evaporation from water and soil surfaces and tran- parts, before and after 1980. The first part does not show spiration from plants leading to drought. In the Maldives, asignificant change in wind speed, while the second part Mörner et al. () linked the intensification of north-east shows a jump. In addition to the reasons given above for winds in over 30 years from the 1970s to increased evaporation.
Table 9 | Average increase upon a period of 10 years
Average increase upon a period of Variable Region 10 years
Rainfall (mm) Rwegura 87.21 Imbo north plain 58.70 TM Rwegura 0.45 Imbo north plain 0.63 Tmin Rwegura 0.18 Imbo north plain 0.25 Tmax Rwegura 0.27 Imbo north plain 0.20 WS Rwegura – Imbo north plain 0.51 Figure 10 | Break point in WS at Imbo north plain.
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Future climate
The future climate in monthly patterns using seven RCMs (CNRM, ICHEC, IPSL, MIROC, MPI-M, NCC and NOAA) are presented in Figures 11–13. Figure 11(a) and 11(b) respectively relate to the monthly projection in rainfall at northern Imbo plain and Rwegura stations in the period 2021–2050 according to the baseline period 1981–2010. Figures 12(a) and 12(b) forecast the monthly mean tempera- ture at northern Imbo plain station and Rwegura station, respectively, with respect to the above periods. Figure 13 shows the projected monthly wind speed pattern at Imbo north plain station over the period 2021–2050 according to the reference period (1981–2010).
Monthly scale changes
On a monthly scale, the findings showed that between months with more rainfall (November, March and April), only November is projected to increase in rainfall by 18 and 11% at Imbo north plain and Rwegura stations, respectively
Figure 12 | Observed (1981–2010) and projected monthly mean temperature over the period 2021–2050: Imbo north plain (top) and Rwegura (bottom).
(Figure 11). In contrast, the month of October will experience a decrease in rainfall of 24 and 17% at Imbo north plain and Rwegura stations, respectively. This change in rainfall pattern may translate to months with heavy rains or increase in number of rain events and extend the long dry season by 2050 compared to the baseline period. The temperature (Figure 12) is forecasted to increase in both stations. From May to October, the mean temperature will increase between 3 and 3.9 C at Imbo station, and between 2.3 and 2.9 C at Rwegura station by 2050. May will experience the highest increase at Imbo north plain, while at Rwegura the month with the highest increase will be October. A general increase is projected for wind speed (Figure 13) for all months at Imbo north plain. The increase is expected to be between 1 and 1.6 m/s from April to Octo- ber, with August being the month with the highest change. The changes pointed out in the wind speed will obviously have a positive impact on wind power potential in the Figure 11 | Observed (1981–2010) and projected monthly rainfall over the period 2021– 2050: (a) Imbo and (b) Rwegura. long dry season.
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(1981–2010) for the two sites. At Imbo station (plain region) model projections are closer than at Rwegura (mountainous region). Future rainfall will be characterized by high interannual variability, including a decreasing trend from 2029 to 2041 and an increase from 2042 to 2050 at Rwegura. Table 10 shows the mean change for the period 2021–2050. It is found that the ICHEC model pro- jected the highest rate of increase in rainfall (þ17.5% for Imbo and þ15.4% for Rwegura) while the NOAA model forecasted the highest rate of decrease for both stations ( 12.9% for Imbo and 14.1% for Rwegura). The analysis using multi-model mean (Ensemble mean model) revealed Figure 13 | Observed (1981–2010) and projected monthly wind speed at Imbo north plain over the period 2021–2050. that at northern Imbo plain station, the rainfall will increase by 1.73% while at Rwegura station the rainfall is projected to Interannual scale changes decrease slightly by 0.33% over the period 2021–2050 com- pared to the baseline period (1981–2010). However, the MK Figure 14 presents the interannual projection in rainfall for test revealed no significant new trend despite those rates of the period 2021–2050 compared to the baseline period change.
Figure 14 | Projected average annual rainfall a) Imbo and b) Rwegura for the period 2021–2050 with the reference period 1981–2010.
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Table 10 | Changes in average annual rainfall (RCMs outputs) increasing trend of the mean temperature. The IPSL model forecasted the highest values while ICHEC projected 1981–2010 2021–2050 the lowest for both stations. As summarized in Table 11 for Average rainfall Average rainfall the period 2021–2050, all the models projected a higher (mm) (mm) Change in % increase at the plain site than the mountainous site, except RCM Imbo Rwegura Imbo Rwegura Imbo Rwegura for ICHEC. The IPSL model forecasted an increase of CNRM 667 1542 655 1557 1.8 1.0 3.88 C at Rwegura and 4.33 C at Imbo, while the ICHEC ICHEC 667 1542 784 1780 17.5 15.4 model projected an increase of 0.68 C at Rwegura and IPSL 667 1542 675 1516 1.2 1.7 0.47 C at Imbo. The analysis using multi-model mean MIROC 667 1542 690 1574 3.4 2.1 revealed that the average annual temperature is projected MPI-M 667 1542 675 1534 1.2 0.5 to increase by 2.29 C at northern Imbo plain station and NCC 667 1542 682 1521 2.2 1.4 by 2.08 C at Rwegura station for the period 2021–2050. NOAA 667 1542 581 1325 12.9 14.1 The projected fluctuations of wind speed for the period 2021–2050 are presented in Figure 16, which shows a good Figure 15 presents the projected average annual temp- agreement of models, except MIROC which projected lower erature. Despite the high deviation between the models values. This figure also clearly shows the increasing trend of contrarily to the precipitation, all the models projected an the wind speed. Table 12 summarizes the average annual
Figure 15 | Observed (1981–2010) and projected average annual temperature over the period 2021–2050: a) Imbo and b) Rwegura.
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Table 11 | Changes in average annual temperature (RCMs outputs) The increases in temperature and wind speed found in this study are aligned with several studies in the region as 1981–2010 2021–2050 well as in other regions of the world as a result of the general Average TM ( C) Average TM ( C) Change in C warming reported by the IPCC (Intergovernmental Panel on
RCM Imbo Rwegura Imbo Rwegura Imbo Rwegura Climate Change ).
CNRM 25.45 17.05 27.29 18.30 1.85 1.25 In Burundi, electricity production is largely dependent ICHEC 25.45 17.05 25.92 17.73 0.47 0.68 on hydropower production. Therefore the non-clear trend fi IPSL 25.45 17.05 29.78 20.93 4.33 3.88 in rainfall is not bene cial for hydropower production plan- MIROC 25.45 17.05 28.07 19.49 2.62 2.44 ning. However, other sources positively impacted by fi MPI-M 25.45 17.05 27.51 19.01 2.06 1.96 climatic variation, such as wind speed, will be bene cial NCC 25.45 17.05 27.98 19.41 2.57 2.36 for the country. NOAA 25.45 17.05 27.62 19.05 2.17 2.00
CONCLUSIONS wind speed as projected by the RCMs and highlights that the highest value of increase (1.61 m/s) is forecasted by the The study of interannual variability using the standardized MPI-M model, while the lowest value (0.91 m/s) is projected variable index discriminated three time periods at Rwegura by the MIROC model. and at Imbo north plain stations for each climatic par- These findings are in accordance with many scientific ameter. In the rainfall pattern, the period 1950–1973 is reports on Burundi which generally forecast the decrease widely in excess with no deficit year and the period 1974– in rainfall in some northern parts of Burundi and the 1992 is a mixture of normal, deficit and excess rainfall, increase in rainfall in the southern parts from east to west while the period 1993–2014 has no excess year. For the (Liersch & Rivas ). They also projected the increase in mean temperature pattern analyzed, the period 1950–1966 temperature across the country. has no excess mean temperatures and the period 1967– The non-homogenous trend in projected rainfall seems 1976 is a mixture of normal, deficit and surplus mean temp- not to be a particular characteristic of the studied sites. eratures while the period 1977–2014 is in high surplus with Indeed, it was reported for several areas over the world no deficit mean temperature. In the wind speed time series, and for different climate contexts (N’Tcha M’Po et al. the period 1950–1977 is deficit and the period 1978–1997 is a, b). a mixture of deficit and normal wind speeds, while the
Figure 16 | Observed (1981–2010) and projected average annual wind speed at Imbo for the period 2021–2050.
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Table 12 | Changes in average annual wind speed (RCMs outputs) during the long dry season from May to October. The aver- age annual temperature is forecasted to increase by 2.29 C 1981–2010 2021–2050 at Imbo north station, while at Rwegura station it will be Average WS (m/s) Average WS (m/s) Change in m/s 2.08 C. The average wind speed is projected to increase by
RCM Imbo Rwegura Imbo Rwegura Imbo Rwegura 1.44 m/s at Imbo north plain station. The highest values in
CNRM 4.10 – 5.57 – 1.47 – wind speeds are forecasted during the long dry season. ICHEC 4.10 – 5.54 – 1.44 – This study showed that the observed trend within the refer- – IPSL 4.10 – 5.62 – 1.52 – ence period will not be altered in the period 2021 2050. fi MIROC 4.10 – 5.01 – 0.91 – Therefore, the country would bene t from wind power if it MPI-M 4.10 – 5.71 – 1.61 – invests more in wind power production. NCC 4.10 – 5.67 – 1.57 – One of the causes of the trends detected in wind speed NOAA 4.10 – 5.66 – 1.56 – historical data has been attributed to the environmental degradation due to global climate change and demographic explosion. period 1998–2014 has excess wind speeds. Furthermore, a series of statistical tests applied, namely Mann–Kendall, Pet- ACKNOWLEDGEMENTS titt, Mann–Whitney and Kolmogorov–Smirnov tests, have detected trends in the climatic variables. Concerning the rainfall, downward trends have been detected both at Rwe- The authors gratefully acknowledge the German Academic fi gura and at Imbo north plain. The break points in rainfall Exchange Service (DAAD) for the nancial support to this have been detected in 1990 and 1978 at Rwegura and at research work. Imbo north plain, respectively. In the temperature pattern, upward trends have been detected at both study stations. REFERENCES The break points in mean temperature have been localized in 1976 and 1983 at Rwegura and at Imbo north plain, Bidou, J. E., Ndayirukiye, S., Ndayishimiye, J. P. & Sirven, P. respectively. However, for both stations, maximum and Géographie du Burundi (Geography of Burundi). Hatier, minimum temperature break points have been detected in Paris, France. Bielec, Z. Long term variability of thunderstorms and 1981 and 1977, respectively. For the wind speed pattern, thunderstorms precipitation occurrence in Cracow, Poland, an upward trend has been detected and the break point in the period 1896–1995. Atmos. Res. 56, 161–170. has been located in 1991. The causes of the jump in wind Boers, Z., Donner, R. V., Bookhagen, B. & Kurths, J. Complex network analysis helps to identify impacts of the El Niño speed observed since the 1980s have been analyzed. Southern Oscillation on moisture divergence in South The future climate has been addressed on a monthly and America. Clim. Dyn. 45, 619–632. interannual scale. The key climatic variables were projected Burí, C. D., Lukoví, C. J., Bajat, B., Kilibarda, M. & Živkoví, C. N. to the period 2021–2050 with respect to the baseline period Recent trends in daily rainfall extremes over Montenegro (1951–2010). Nat. Hazards Earth Syst. Sci. 15, 2069–2077. (1981–2010) using seven RCMs. The findings showed that of Cantet, P. Impacts du changement Climatique sur les Pluies the months with more rainfall (November, March and Extrêmes par l’utilisation d’un Générateur Stochastique de April), November will experience the highest increase in Pluies (Impacts of Climate Change on Extreme Rains by rainfall. The mean increase for the period 2021–2050 is pro- Using a Stochastic Rain Generator). PhD Thesis, Université Montpellier II, Montpellier, France. jected to reach 18% at Imbo north station and 11% at Ceppi, P., Scherrer, S. C., Fischer, A. M. & Appenzeller, C. Rwegura. Furthermore, the month of October will experi- Revisiting Swiss temperature trends 1959–2008. Int. J. ence a decrease which could reach 24% at Imbo and 17% Climatol. 32, 203–213. de Lima, M. I. P., Santo, F. E., Ramos, M. A. & Trigo, R. T. at Rwegura. On an interannual scale, it is found that there Trends and correlations in annual extreme precipitation fi is no signi cant trend in the projected rainfall for the two indices for mainland Portugal, 1941–2007. Theor. Appl. sites. Warming will be more significant for both stations Climatol. 119,55–75.
Downloaded from https://iwaponline.com/jwcc/article-pdf/10/4/852/640645/jwc0100852.pdf by guest on 27 April 2020 869 A. E. Lawin et al. | Trends and changes detection in rainfall, temperature and wind speed in Burundi Journal of Water and Climate Change | 10.4 | 2019
dos Santos, C. A. C. Recent changes in temperature and Martinez, J. C., Maleski, J. J. & Miller, F. M. Trends in precipitation extremes in an ecological reserve in Federal precipitation and temperature in Florida. USA J. Hydrol. District. Brazil. Rev. Bras. Meteorol. 29,13–20. 452–453, 259–281. Fatemeh, R., Ali, M. N., Mojdeh, P. & Michael, C. K. Wind McKee, T. B., Doesken, N. J. & Kleist, J. The Relationship of speed variability over Iran and its impacts on wind power Drought Frequency and Duration Times Scales. In: 8th potential: a case study for Esfehan Province. Meteorol. Appl. Conference on Applied Climatology. American 18, 198–210. Meteorological Society, Anaheim. Funk, C. A Climate Trend Analysis of Kenya – August 2010. Ministry of Water Resources, Environment and Land Management USGS Numbered Series. U.S. Geological Survey Fact Sheet of Burundi National Communication Strategy on 2010-3074, Sioux Falls, USA. Adaptation to Climate Change and Precautionary Warning of Giorgi, F., Jones, C. & Asrar, G. Addressing climate Extreme Climate Events. Bujumbura, Burundi. information needs at the regional level: the CORDEX Mörner, N., Tooley, M. & Possnert, G. New perspectives for framework. World Meteorol. Org. Bull. 58, 175–183. the future of the Maldives. Glob. Planet. Change 40, 177–182. Griffiths, G. M., Salinger, M. J. & Leleu, I. Trends in extreme N’Tcha M’Po, Y., Lawin, A. E., Oyerinde, G. T., Yao, K. B. & daily rainfall across the South Pacific and relationship to the Afouda, A. A. a Comparison of daily precipitation bias South Pacific convergence zone. Int. J. Climatol. 23, correction methods based on four regional climate model 847–869. outputs in Ouémé Basin. Hydrology 4,58–71. Hamududu, B. & Killingtveit, A. Assing climate change N’Tcha M’Po, Y., Lawin, A. E., Yao, K. B., Oyerinde, G. T., impacts on global hydropower. Energies 5, 305–322. Attogouinon, A. & Afouda, A. A. b Decreasing past and Hamududu, B. & Killingtveit, A. Hydropower production in mid-century rainfall indices over the Ouémé River Basin, future climate scenarios: the case for Kwanza River, Angola. Benin (West Africa). Climate 5 (74), 1–28. Energies 9 (363), 1–13. Nzigidahera, B. Etude d’évaluation des impacts des actions Intergovernmental Panel on Climate Change 2007 The Scientific antropiques et du dgré de disparition de la biodiversité Basis. Contribution of Working Group I to the Fourth (Evaluation study of the impacts of anthropogenic actions Assessment Report of the Intergovernmental Panel on and the degree of disappearance of biodiversity). Rapport de Climate Change. Cambridge University Press, UK and Recherche (Research Report) MB. Divisions des Sciences New York. Ecologiques, UNESCO-MAB. Joel, N., Jackson, N. & Simon, M. Regional flow duration Pettitt, A. N. A non-parametric approach to the change-point curve estimation and its application in assessing low flow problem. Appl. Statist. 28, 126–135. characteristics for ungauged catchment. a case study of Powell, E. J. & Keim, B. D. Trends in daily temperature and Rwegura Catchment, Burundi. Nile Basin Water Sci. Eng. J. precipitation extremes for the Southeastern United States: 4 (1), 14–23. 1948–2012. J. Clim. 28, 1592–1612. Karmeshu, N. Trend Detection in Annual Temperature & Rajeevan, M., Bhate, J. & Jaswal, A. K. Analysis of variability Precipitation Using the Mann Kendall Test – A Case Study to and trends of extreme rainfall events over India using 104 Assess Climate Change on Select States in the Northeastern years of gridded daily rainfall data. Geophys. Res. Lett. United States. Master’s Thesis, University of Pennsylvania, 35 (18708), 1–6. Philadelphia, PA, USA. Ramade, F. Eléments d’Ecologie: Ecologie Appliquée Kendall, M. G. Rank Correlation Methods, 2nd edn. Hafner (Ecology Elements: Applied Ecology), 6th edn. Dunod, Paris, Press, NYC. France. Lawin, A. E., Afouda, A., Gosset, M. & Lebel, T. Variabilité Riahi, K., Grübler, A. & Nakicenovic, N. Scenarios of comparé du régime pluviométrique aux échelles régionale et long-term socio-economic and environmental development locales sur la Haute Vallée de l’Ouémé au Bénin (Comparative under climate stabilization. Technol. Forecast. Soc. Chang. variability of rainfall regime at regional and local scales on the 74, 887–935. Upper Ouémé Valley in Benin). IAHS Publ. 340,61–68. Rice, J. A. Mathematical Statistics and Data Analysis, 3rd Liersch, S. & Rivas, R. Climate Change Report for Burundi. edn. Thomson/Brooks/Cole, Belmont, CA. GIZ, Eschborn. Salinger,M.J.&Griffiths, G. M. Trends in New Zealand daily Mahongo, S. B., Francis, J. & Osima, S. E. Wind patterns of temperature and rainfall extremes.Int. J. Climatol. 21, 1437–1452. coastal Tanzania: their variability and trends. West. Indian Shamin, M., Daphnée, A., Sophie, B., Laura, L. & Charpentier, E. Ocean J. Mar. Sci. 10 (2), 107–120. Les Centrales Hydrauliques (Hydraulic Power Stations). Mann, H. B. Nonparametric tests against trend. Econometrica O.P., Paris, France. 13, 245–259. Soro, G. E., Anouman, D., Goula Bi, T. A. & Srohorou, B. Mann, H. B. & Whitney, D. R. On a test whether one of 2 Characterization of sequences of meteorological dryness at random variables is stochastically larger than the other. Ann. various time scales in Sudanese climate: case of the extreme Math. Stat. 18,50–60. northwest of the Ivory Coast. Larhyss J. 18, 107–124.
Downloaded from https://iwaponline.com/jwcc/article-pdf/10/4/852/640645/jwc0100852.pdf by guest on 27 April 2020 870 A. E. Lawin et al. | Trends and changes detection in rainfall, temperature and wind speed in Burundi Journal of Water and Climate Change | 10.4 | 2019
Sugahara, S., Porfiírio da Rocha, R. & Silveira, R. Non- Vincent, R. & Gilles, S. Statistique Mathématique en Action stationary frequency analysis of extreme daily rainfall in Sao (Mathematical Statistics in Action), 2nd edn. Vuibert, Paris, Paulo, Brazil. Int. J. Climatol. 29, 1339–1349. France. Talaee, P. H. Iranian rainfall series analysis by means of Yao, Y., Gordon, H. H. & Qianguo, L. Climate change nonparametric tests. Theor. Appl. Climatol. 116, 597–607. impacts on Ontario wind power resource. Environ. Syst. Res. Talaee, P. H., Kouchakzadeh, M. & Shifteh, S. B. S. 1 (2), 1–11. Homogeneity analysis of precipitation series in Iran. Theor. Zhai, P. M., Zhang, X. B., Wang, H. & Pan, X. H. Trends in Appl. Climatol. 118, 297–305. total precipitation and frequency of daily precipitation Telemu, K. Trend analysis of monthly rainfall data in central extremes over China. J. Clim. 18, 1096–1108. Zone. J. Math. Stat. 9 (1), 1–11. Zhao, P., Jones, P., Cao, L., Yan, Z., Zha, S., Zhu, Y., Yu, Y. & Tomassini, L. & Jacob, D. Spatial analysis of trends in extreme Tang, G. Trend of surface air temperature in Eastern precipitation events in high resolution climate model results China and associated large-scale climate variability over the and observations for Germany. J. Geophys. Res. 114,1–20. last 100 years. Am. Meteorol. Soc. 27, 4693–4703.
First received 22 September 2017; accepted in revised form 17 January 2018. Available online 7 March 2018
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