Rainfall Variability and its Implications for the Transferability of
Experimental ,Results in the Semi Arid Areas of Tanzania u"
2 1 Mahoo~' B ..Fl*, M.D'.B·.', Young and 0 .. B. Mzirai r, lDepartinent' of AgriculniraJ Engi'neering and Und Planning', Sokoine Univer'sity ofAgriculture, TanZania' ' '. .' ! '" ' 2Centre for Land Use and Water Resources Research, University of Newcastle, Newcastle upon Tyne,.' UK . ' ,"','. ,. " :r .. , , - ' <' , ' I
<.1· Abstract I ~ ,
Mosrof agrj~ultural qctivities in semi-arid areqs of Tanzania ,d~pend on direct rainfall: Conse-, quently, any significant variation in /he temporal and spatial distribution of rainfall usually results in serious shortage of sQil~water available to plants and thus poor crop and livestock production. In this paper the variafJility and reliability of rainfall in four representative areas of the semi.:arid, lands in Tanzania namely Homholo (Dodoma)~' Morogoro (Morogoro); Kisangara (Kilimanjaro) ' and Ngudu (Mwanza) is examined. The start and end of t~e rains and tJ;u: occurrence of long dry spells within the seasons are also analysed. The result showed/hat during the long r{liny seCforJ, the seasonal rainfall for Kisangara and Morogoro were only 525 mm and 445 nun respectively. During the short rains, the seasonal average rainfall was 359 mm and 327 mmfor Kisangara mid, Morogoro respectively. For Ho11Jbolo which ha mononwdal rainfall, the seasonal mean was 592 mm. Apart from seasonal mean rainfall"Qeing .low, the seasonal rainfall variability expressed by the cotif.jicient ofvariaiion (CV) was also observed to' beliig~. Kisangara and Morogoro had CV's
) of 20% and 50% during long' rainy season respectively. During short rainy season the CV's were 2
1 49% and 42 %,respectively. Hombolo. had a CY. of 24%. Analysis of long-temz rainfall character 0 2
istics for Morogo!o and Kisangara 'reyealed that these areas. experience dry spells of vqrious d e t lengths during the seasons: At,Kisangarqfor example,dlY spells of 15 days with 30% probabIlity a d may occur during short rainy season, while zn long rainy'season dry spells of more than 14 days ( r
e at 30% chances do occur 'in the rest of ihenwnths except April. These results form a good basis h s i for explaining the pelfomzance of soil-water conse";'ation n:zeasures including rainw~ter harvesting l b in the semi-arid areas of Ta,nzanid.. . . u P e h t
Key words: Rainfa~l variability ,~ry. sPells,: transferability, rainwater harvesting, a~r~)Il:eteorology y b d e t n a r g Introiluctioij. semi-arid climates, among'the seasons of the e c year may require segregation of data by sea-' n e son. A combination of factors common in one c ainfall in 'tIlt;' semi-arid areas ofTaDzania i l
r is variable with respect to both time and' season of the year. may be virttially non': e R d space. Long term cycles or, cyclic swings in existent during another season. In addition, a n u climate are, often important. Ccnisequently; pait'icular combination of faciors may' exist for y a probability studie~ or computation of long term only'a few days in several years and may'ren-' w e t means are very likely to lead to erroneous con der th~ coinputatkins' based on average values a
G grossly erroneous. Rainfall also varies greatly
clusions, unless consideration is given 'to' the t e position of the 'Period of record in the c)-de or among'different parts in the semi-arid areas of n i b swing. Likewise; the' marked' variatio~.' 'in Tanzania, a:nd indeed even within a catchment.' a S y b
d • Correspondi':'g a~thor -.' I e -' c "" " I ' u i', / .' ' Tanzania J. Agric. Sc. (1999) Vol. 2 No.2: 127 - 140 d o r p e R 128 H. F. Mahoo et aI.
For example, a high elevation zone may be the years that appeared to be the major periodicity only part of a catchment in which rainfall is over East Africa. Tyson and Dyer (1978) in recei ved regularly. In such a catchment, as South Africa obtained similar results, and Mas much,.~S 90, percent of the runoff lllay originate son (1980) in Swaziland, by identifYing trends from 10 percent of its area. in rainfall data which characterised periods of dry and., wet spells over selected periods of Studies'~)Jl rainfall'variabiiity in the semI-arid study. In these studies, the observed or pre aI'eas of Tanzania are limited. Ngana (1990) dicted trends indicated that the decades of the modelled and analysed the periodic features in 1970's were generally wetter dIan nonnal for seasonal rainfall and its implications to water the summer rainfall as a whole; tlle 1980's resources and agricultural planning in central were much drier and the 1990's were expected regions of Tanzania.' He observed thati'n some to be niuch wetter. These predictions should parts of the semi-arid central 'Tanzania, tliere is however, 'be taken cautiously because they a 5-year cyclic phenomenon in dIe 'seasonal 'have tlleir own shortcomings analytically, and rainfall series. He furtller 'obseFved tllat in tlley' can present numerous problems to deci some localities tllere was an additioilal 2.5-yeat sion-makers. cycle superimposed' in tlle 5-year cycle. He concluded tllat the basic pattern of tlle seasonal Venalainen and Mhita (1998) analysed the rainfall could, to some extent be predicted by a variability of raiilfall in' Tanzania using six periodic function Imodel altllOugh the I'llodel' stations (Moshi, Mwanza, Songea, Dodol1'J.a, did not adequately predict the peaks or lows of' Dar es Salaam and Tabora). They reported'that the seasonal rainfall series. Studies by in siIigle peak" areas such', as' Dodonia and Ndorobo (1973), Patton (1971), Ngana (1983) Tabora,'the main onset of rains is 'between )
2 have reported on fi'equency offamines in many mid-November and early Decenlber while tl~e 1
0 parts of ce'ntral semi-arid' Tanzania. Generally, mean end of tlle seasonal rains is' be'tween mid~ 2
d the above studies showed tllat occurrence of Ap'ril and mid-May'. In' these areas ,there is e t a famines was liot only related to yeats of low . about a '10% probability 'of a long dry 'spell.' d (
rainfall but to several otller factors such as low For tlle double 'peaked areas, the' short rains' r e
h acreage accomplished by each farmer, iilCi- start by 25 October and end by mid-January,: s i l dences of pests and cr()p"diseases: Prins and wliile'tlle long, rains start by mid-March ftild' b u Loth (1988) carried out an analysis of rainfall end by earlY' June iIi 75% of tlleyears.' The' P e patterns in northern Tanzania using si~t~,ep., analysis of' tlle occurrence of dry spells re h t
y stations witllin tlle area. They found out tllat vealed dIat tliere is a very high variability of b
d amounts of rainfall were unpredictable and tlle -. rainfall during the shortrains. For Moshi, the e t deviations of long-term mean annual rainfall probability of dry spells of 15 days or more n a r were witllOut cyclic trends for the iqdividual, during long rains ranged from 70% in mid g
e stations. 'How~ver, tlley furtller reported tliat February to zero in mid-April. Howevel', dui'-'/ c n e pooled rainfall data of the different.-statiq~l~' ing the short rains dId probability is abou( c i l showed series o'falternating ~et and dry ye~rs:", sora. '\ ,,-.' ". r e I ' d n u Soniestudie.s cal1'ied out ~y, Rqdhe and yhj) TIle al~lysis. by V~liaiaijlen and, Mhit~' (1998) y a (1976) and Qgallo 11982) t9 exanline tlle Fain~. also, reveal~<;l t0~.riabill~~I, ~itlt.n .~nd betw~t?n w e fall characteristics in some par~sof E~st Africa, seasons. :Pof/example, the ca~culated 20, 50 t a revealed tllat, there are multiple peak,S in_tlle and' 80% values o(teli-aay rainfall iiKllcated. G t
e amlual rainfall and tllere werespatiaJ.variations tllat for,' Dodoma, rainfall is' liar ,re'iiable'.' In n i on dli~, JJhenomenon. Beltrando (1990)' ana- 20'% of the' year.s:,' 'teii-cLiy, ~ai~Jall'alllou~its do' b a
S lysed the space-time variability of rainfall over not exceed 15 mm: In 'lmlf anile years~ dIe y
b East Africa. He reported tllat the spectral amounts exceed 15 mm from 1 Deceiliber fe) 9 d
e analysis of tlle time series associated Witll tlle March. Tabora exllibited ahriost'the sanie un- c u space, c;omponent revealed . peaks around 5 - <> '. ': reliability ofrainfall as Dodonla. In 80 % of tl~e d o r p e R Rainfall Var~ability 129
years the amounts of ten-day rainfall reach 15 The length ~f a dry spell within the growing nUll only from early December to mid-March, season was obtained using the method outlined and in half of the years from early November by Stern e(ai. (1982). A dry day was defided to early April. . as a day wit4 less than 0.85 mm of rain. This is reported',by Nieuwolt (1989) to .be of little The purpose of this paper was to analyse the value as much of it evaporates and aoes, not rainfall· var~bility ~n the study areas and look contribute to the moisture feq~f(';ments of into its implication in the semi-arid areas .of cr~ps.- Tanzania.· In almost all the stations, data had gaps and :... these discrepancies in one way or another must Materials and Methods have affected the description of events like annual rainfall amount, start and end of rain Long-term rainfall data for tour .stations and risk of dry spells. namely Hombolo [50 55' S, 35° 50' E; altitude 1,100 m.a.s.l] (Dodoma Region); Kisangara The INSTAT computer package (Stern et ai., W 43' S, 37°35' E; altitude 870 m.a.s.l] 1990) was used to analyse the data. (Kilimanjaro Region); Morogoro W 50'S, 37° 42'E; altitude 920 m.a.s.l] (Morogoro Region) and Ngudu. [2° 57' S, 33° 09' E] (Mwanza re Results and Discussions giOli). were 'used:The data used was 'collected for periods of 34, 20, 34 ,and 33 years for ~ong term rainfall analysis Kisangara, Ho~bolo, Morogoro and Ngudu, respectively. Analysis carried out included the The descriptive statistical analysi§ of the long )
2 following: ' . . 'term seasonal rainfall for Kisangara, Moro 1 0
2 goro, Hombol0 and Ngudu is shown in Table d e • Descriptive statistics (mean, standard de- 1. Kisangara and Morogoro sites receive bi" t a
d . viation and coefficient· of variation,), . ,modal rains (Masika and Vuli) while the rain (
r fall at Hombolo and Ngudu i~' unimodal. Dur- e h s Masika i • Determination of trends or cycles by use }ng season, the mean seasonal raillfall l b of moving averages: . :' ~as 525 nun and -445 nun ,for -Kisangara and u P
Morogoro, respectively. In the case of Vuli e h t • Determination of start, and end of growip.g season, the mean was 359 mm and 327 nUll for y
b Kisangara and Morogoro, respectively. The
season. d
e values even for Masika are quite'low in meet t n ing the seasonal requirements for a crop like a • Probabilities of minimum and maximum r g maize, which requires about 750 A simi length of dry spell~. inm. e c lar situation is observed for Hombolo (592 n e c
i The stal1 and end' of growing '-season was ana- mm). This amount may be sufficient for a crop l r lysed using dailY'rainfall based ~n the method like sorghum but ids cOllnnon' to find farmers e d
n outlined by Alusa'and Mushi (1974). 'planting maize. . ' u
y The start of growing season was defined as 'the ! a first period with running total of at least 20 As already pointed out in the introduction, in w e t nun of rain in, four consecutive .P1!-Ys. with at' , th~"semi-arid areas, the total rainfall fig.ures a G
least two days being-wet and no dry· spell 'of- . ,may De: misleading. _Variability of the .seasonal t e
n more than ten days in the next thirty' days', The ,rainfaUjs,ther~f6'~e ~~. i~portant parameter. In i b end of growing season was defined as the first Table 1, the/vanabIlity IS represented by the a S occasion after an earliest possible ending date coefficient of variation (CV). In Masika, the y b on which fifteen ,consecutive dry days oc- CVs ,are 20% and 50% for Kisangara and d e c r.llrred. Mordgoro statio¢ respectively. During Vuli u d o r p e R 130 H.F. Maboo et al.
the CYs are '49%' and 42% rt!specti'veIy. For wave or swing every ten years'was observed. Hombol9, ,'the' CV, is 24 %.' These are 'large A similar trend was observed fofboth Vuli and variations ind to a great extent they contribute Masika seasons at Morogoro .(Figure 2(a) and to cropfailtires.· Therefore, the coefficient of 2 (b). " ,I variation 'over the growing', Period implies a high ~ uncertainty in crop yields. Similar r:esults For Kisangara,'both Masika and Vuli had dis wer~' reported by Ngana' (1990) for FarkWa tinct cyclic trends every 10 years:(Figure 3(a) (Dodoma), Singida (Singida), Manyoni (Sin and Figure -3(b). These cyclic waves" were gida), a¢ Maswa (Shinyanga) stations. more distinct during the 1960s and 1970s espe cially for Kisangara. At Hombolo, a similar Rainfall trendS .:, ' trend was observed. Ngana (1990) observed similar results in central Tanzania and con When data for NguQU was ploUC!d on a 5-year cluded that there was a cyclic or periodic fea moving average, (Figure 1), a weak cyclic ture in the data series. I,
Table 1: .' 'Descriptive statistical analysis of'long·term seasonal rainfall (mm) at Kisan- gara, Morogoro, Hombolo and Ngudu.:: ._ ..
,<.
Masika Villi ; Unimodal Kisangara Morogoro Kisangilra Morogoro Hombo10 • Ngudli Mean 444.9 525.1 359.3 . 327.2 592.0 '864.2 Standard Deviation 224.5 106.5 174.7 136.7 139.5 182.1
) Coefficient of variation (%) 50.0 20.0 49.0 42.0 24.0 21.0 2 1 0 2 d e t a d ( r e h 1400 1··----·- s i l . 1200 +---.----.-". b u P
_S 1~ L e "1.,
h e - t '-'800 cL." __ -...
y = .,.1. b C! 600:+ . d . ' . e t .'= -; 400,1 n
a ... ~ r 200' - ...... -.-- g
- ". '."..... , e c .0 c.. -'-", " 'c'" :>:.. _';.-.- .. ,., '1 n e c i l . ~~~ ;o,~b,. ~~'o, ~~'b. ~~ ~'b"v~; ~~. ',:>~ A~ .~~ ~~ ~~o,'" ~~ ~ Years. ,I' .' , \i . ','; y a ,'r ,. ' . w ~# l e '.' i--;:-::'-;- .~:'; -~---::--- --'...... -:: ...... r - - '. t a \ .' .;' i__ o.Ann~aJ.r~~.~all_~~~~~~tm_oviIl~ ave~~~" G t -- ;:... e .' ), rt .~ .. n Figure 1: Annuarrainrall and 5-yea.. moving av~rage for Ngudu ',1 '~ : l •. ;.' i b ..!..'.... ~~ ..l .. , / a '. .'. S y b d e c u d o r p e R Rainfall Variability 13 I 700, 600 500 1400 ~ = 300i ~ ,200 i ! j. 100 . .~.- ~ '1 • ,j 0 <'l 10 .... 00 0 <'l M t: f::! 00 ~ ~ ""~ ~ ~ ~ '"~ N .;.; .... ""C; N"" M ". ;::: .... 00 .~ 00 ~ 00 ~ ~ ~ ~ ~ ~, ~ ~ ~ ~ ""~ ""~ ""~ 'Years", ' ---Seasona rainfall -. - "5-Year moving average i .=---.::=::::....::.:....==.-._-----==- .===----.::----,"--=----'-=-=--=-----,=-- ~ --._---- Figure 2 (a): Vuli seasonal rainfall and 5-year moving average for Morogoro c 800 700 ) 2 600 1 0 2 e 500 d '-'e e t a 400 d - ( ~ r = 300 e "ca h s ~ i l 200 b u P 100" e h t "1'; 0 y b 110 Jo_." ~ ~. !Xl d 0> 0> 0> e t n a Years r g e c n : --.- -'S'-y a e Seaso~ raini~ll;'::'" ~'- ears~iuoving' verag~ c i l r e Figure 2 (b): Masika seasonal rainfall and 5-year moving average for Morogoro d n u y a w e t a G t e n i b a S y b d e c u d o r p e R 132 H. F. Mahoo et al . . _.. _----- 1000 900 800 --8 700 '-'8 600 - .;s- 500 I:: 400 ·ca 300 ~ 200 100 0 N L!) co ..- " i --Seasonal - 5-Year moving average" , ~ - --- - .. '--_._. ------... _.. __ ._- ---.-- -.-.--.. --.--~------...- -_ .. _._-_._----. - Figure 3 (a): Vuli seasonal rainfall and 5-year moving average for Kisangara ) 2 1 0 2 d e 1400 t a d ( 1200 r ,-.. e h 1000 s e i l I b S 800 -. u P e 600 h ~ t y ·ca= b 400 ~ d e t 200 n a r g Of) Of) Of) Of) e ..... M r-- ..... M ..... M ..... M r-- _ 00 r-- r-- c 'D 'D 'D 'D 'D r-- r-- r-- r-- r-- 00 00 00 00' 0- '" '" ", -" '" '" n .. ",I "'~ ...... t --.....-I '" '" e \ i ...... '"...... -'" "'- '" -'" '" '" c '" '" '" '" '" '" '" \ '" '" '" '" '" i ..... l r Years e f I d .- - -_.' n ------.------\: u i --Seasonal - 5-Year moving average I y .. _...... - ...... __ . \ ... . a I w e t I a Figure/3 (b): G Masika seasonal rainfall and 5-year moving average for Kisangara t e \ n i b a S y b d e c u d o r p e R Rainfall Variability 133 Long-term rainfall characteristics example, atKisangara the mond1 of November may receive at least 18 mID of rainfall dming Hombolo Vuli and the lengd1 of the longest dry spell with 30%' probability of occurrence is'15 days. A summary of the long-tenn rainfall charac During Masika, at Kisangara, dry spells of teristics for Hombolo is shown in Table 2(a). more than 14 days at 30.% chance' are Rainfall variability at Hombolo is very high. experienced in. the rest of the mond1s except and varies from 326 mm to 882 mm. The long April ~ In all these situations, there is a high term mean rainfall is '.591 rrim, whereas the possibility of' 'crop damage by water stress 70 % probability rainfall' is 518 1m11- The rains during the season. are always received in the months of Decem ber, January, February and March. Planting is usually successful if it is not fol lowed by a long'dry period. From dIe preced Morogoro and Kisangara ing analysis, the distribution of the start and end rui.tes for the rain can be used successfully Rainfall for Vuli in these stations is both unre- " to select the best planting or harvesting dates, liable and variable from season to season. For, . although other limiting factors like soil water Morogoro, the rains may start in October and holding capacity, variety of crop, length of end in January. The situation at Kisangara is" growing season may have to be taken into ac that, for Vuli, the season starts in November count. Due to the uncertainty of the start of and ends in January. During Masika, the sea rainfall, many farmers in the semi arid areas son may start in early March and stop by the have resorted to dry plantii1g~ This practi~e is end of May in Morogoro (Table 2 (b»). While highly risky because, although emergence may the Masika season starts in March and ends by be good, the crop die due to prolonged dry ) 2 the end of May in Kisangara (Table 2 (c)). spells during this growth stage. Fanners are 1 0 2 therefore forced to do second or even third d e In all the two stations, dry spells of various planting which may lead t9 low and poor t a lengths do occur during the. seasons. For yields. d ( r e h s i l b u P Table 2(a): Summary of long ten~ ,rainfall characteristics for Hombolo' e h t y Month Oct Nov -' Dec Jan Feb Mar Apr May Sea- b d son e t Rainfall ·~35.7 n Min 0.0 0.0 0.0 39.7 7.0 0.8 0.0 326.3 a r (rum) Mean 3.8 33.9 113.5 133.7 121.7 111.4 61.6 5.9 591.9 g e 70%1 0.0 9.6 60.8 83.8 81.8 82.0 20.8 2.8 518.0 c n Max 43.6 88.5 290.5 293.7 253.1 180.0 212,2 24 881.6 e c i Wet l 3rum+ 2 2 6 8 7 7 5 0 37 r e 'days 5rum+ 1 1 5 6 6.4 5 3 0 22 d n 1Omm+ 1 1 3 4 4 2 2.2 0 18 u Longest Min 21 5 5 3 4 8 y 4; a Dry 30%2 31 27 19 11 17 20 15 31 w e spell t Mean 31 19 17 8 12 14 13 29 a (dal:s) Max 31 30 41 17 25 27 34 48 G t e 1 = Probability oj exceeding; 2= Risk oj occurrence n i b a S y b d e c u d o r p e R 134 H. F. Mahoo et aI. TabJe2(b):' .Summary of long termrainfaII characteristics fo'r Morogoro and Kisangara (Vulr) • r :. , Panuneter 'Aug Sep Oct Nov Dec Jan Season - 'Morogoro Raiiuall Min 0.1 . 0.0 . 0.0;. ·1.4 0.8 -10.8 124.3 (mm) 109'.9 321:7 Mean 8.7 9.5 30.5' 65.9 96.8 , ~- , ' ":3.7 4.8 . 10.0 36.7 ·58.9 '.' 48.3 230.9 .' .. l ,~:.: 79% 'J. Max" - 31'.2 22.4 105.0'- 175.7· 262.4 - 287.7. - ' 572.0 ' " " ";:: .- Wet d:ays 3mm+ 2 4 6 6 20.- .. i . '.1 ... .' J ,5.rnm+ O. 1 2 3 5 5 16 :; '" .. .I • IOrnrn+ 0: 0 ·2 '3 3 9. " . ,. .. Longest dry min 13 12 10 6 7 3 ~ ,.; spell. 30% 31 30 25 20.5 14 19.5 .1 . - Mean 2C 24 20 16 12 ';Is"~ ,. ._,' Max 31 30 31 .- ,21 __ .28 , , ': 30 " rj •• -. ~isangara Rainfall- Min'--"'-- 0.0 0.0- 0.0 ,., i8.0 :0.0 0.0 121.3! . , . , ' , Crnrn) Mean 5.0 9.5 35.3 138.3 , , .106.4 65.1 .354.9 _.. , 70% : 6.0 0.0 -; 8.5 "78.5- :' 50:5 : --'22.0' 258.6 ) 2 .. 1 Max 32.0' 55.0 130.0.' 424.0 385.0 211.8 950:0 0 2 d Wet days 3mm+ .1 3 '7 6 3 ,,-_ _ 21· l .. e t a d 5mm+ 2 6 5 3 16 ( r e IOrnrn+ 0 0 2 4 2 h 3 11 s i l b Longest dry Min 10 14 4 S 4 9 u P spell e 30% 31 30 27 15 14 20 h t y Mean 26 26 20 11 13 17 b d e Max 31 36- -. 31 30· 35 -11 t n a r g e c n e c i l r e d n u y a w e t a G t e n i b a S y b d e c u d o r p e R Rainfall Variability 135 Table 2(c): Summary of IoBg term rainfall characteristics for Morogoro and Kisangara (Manka) _ - - -- Feb Mi!J,,- Apr. -May lun lui 'Season Morogoro Raintiill Min 0.,7 4L2 87,1 :12.7 ;0..0._ 0.:0. 348,0." , (il1I11) Meiiii 85.4 i26.4 , 188.4 87,0 - 17.2 15.2' . 504:9 70.%' '~2,6 106.8 . 161.2 67:3 9.5 , 7.0. 498.:2- , " M~x: 257:9 '229.1 2Q6:4 -157.7' 61.4 ' 61.1 757.6 Wet days ,. .3mm" ; 4 8 .. 13. 8 2 1 _ 36 '5nuii+'I', -3 .6 ' '11 . - ~. ,7 -1. 1 29 -, iamm+ 3 4 7 3 0. 0. - 21 Longest dry min 3 3 2' 5 11 11 " :~pell. 30.% i'8 12 8 1-3 30. • .' '·31 Mean 1~ 9 6 10 23 22 . ' ", : : Max - 28 J -• 22 12 . 22 30. 31 Kisangara Raintiill -- Min o..o.,!; 28.0. 30 - 0..0. 0..0. 0..0. f63} (mm) Mean 44.7 . 1655' 162.3 64_5 6.8 12.0. 449.9 - .~ ) " 70.% 23.5 ' ; 76.7 101.0. '35.2 0..0. 0..0. 327.45 2 1 0 161;3 ~ 451.0. '423.0. 47.0. •. 1185.0. 2 . I 203.0. 33.0. ~ r d e t - . Wet days 3mrn+- 'f. 5 8 . '5 L 20. a d ( 5mrn+ 2 4 7 4 0. 17 r e h s IOmm+ 3 5 3 0. 0. 12 i l b u Longeiit-dry Min -- iT 8 4 5 13 14 P spells e h 30.% 24 17 14 15 30. 31 t y b Mean 20. 15 11 13 26 24 d e t n Max 30 31 30 2t 33 39 a r g e c n e c i l r e d n u y a w e t a G t e n i b a S y b d e c u d o r p e R 136 H. F. Mahoo et aI. Start and end of rainy season and the occurrence of dry spells For Hombolo, dry spells occurring between December and April have significant influence The start and cessation of rainfall in the differ on crop growth and yield. There'is a 30% risk ent are~s is given.in Table 3. For Hombolo, (i.e. 3 years ou(oflO) of dry spells of 11, 17, rain may start as early as November to as late 20 and 15 days, for January, February, March as December, while the end of rain is more and April respectively. (Table 2(a)). Maximum ce~iri and occu~ in April (Figure 4) . In lengths of dry spells for these months vary Ngudu the rain may start as early as late Octo between 17 and 34 days. Except for April and ber to, as late as November ,and en!1 as early as December, the rest of the months suffer a dry .' late April to as late as end' of May. For the spell of more than 14 days at 30% chances at other sites,' the probable start of rain varies 'Kisangara and Morogoro. Thus there is high from 15 to 25 days and end of rain varies from possibility Qf crop failure/damage by stress 13 to 18 days. .' during the seasons. Table 3: ~obable date~ for which the rainy season can be expected to start and end at given probabilities Station ,Onset Date at Different Probabilities Cessation Date at Different Prob- abilities " '- . ' 20% 50% 80% s,d' 20% ' 50% 80%' s,d Hombolo 18 Nov 3 Dec 19 Dec 14 15 Apr 19 Apr 27 Apr 9 Ngudu 24 Oct 4 Nov 30 Nov 18 28 Apr 11 May 23 May 13 ) 2 Morogoro (Vul!) 23 Oct 12 Nov 1 Dec 23 6 Jan 18 Jan 29 Jan 13 1 0 Morogoro Masilw) 2 Mar 15 Mar 27 Mar 15 4 May 19 May 2 JUll 17 2 Kisangara (Vult) 23 Oct 11 Nov 1 23 18 2 19 18 d Dec Dec Jan Jan e t Kisan~ara (Masika) 27 Feb 20 Mar 10 A,er 25 2 May 15 Ma~ 29 May 14 a d ( r e h s i l b u P O,g e h t 0,13 y b 0.7 d e t 0.15- n -~ a '.6 r 0_5 g .23 e / c e 0.4 n 0.. ! / e I c i 0.3 I ' l I r e 0.2 d '\ n u O{ y a 0: w e GO 100 120 140 1 GO 100 220 240 200 t " 200 a Day nu rn~r; (1 = 1 SQpt) I G t / i e n Figu/4:- Cumulative probability of start and end of rains in Homb~lo, ~doma i b a S y b d e c u d o r p e R Rainfall Variability 137 Table 4: Effective length (days) of growing season at three levels of probability (rain fall criterion) Station sd Length {Days 2 ------.-----.--.... ------20% 50% 80% Hombolo 35 150 115 90 Morogoro - Masika 20 86 67 ' 47 Morogoro - Vuli 23 86 67 47 Kisangara - Masika 26 81 57 35 Kisangara - Vl{li 20 70 53 31 Ngudu " 38 156 147 123 Effective length of growing season For Kisangara site, during Masika, with the exception of 1999, the seasonal total rainfalls The length of the season mainly depends on the were all above the long teml seasonal mean starting date. The effective length of the rainy during the experimental period (Figure 5b), season at Hombolo, which has a unimodal During this time, no seasonal mean rainfall pattern of rainfall, was found to vary between was in the minimum range. On the' other hand, 90 days at 80% probability to 150 days at 20% during Vuli, with the exception of 1997/98, the probability. The effective length of growing amount of rainfall was within the minimum season during both Masika and Vuli season for range (Figure 5c). The EI Nino rains explain ) 2 Morogoro and Kisangara does not exceed 47 the 1997/98 situation. 1 0 days in 80% probability (four years out of five) 2 d (Table 4). For Ngudu the effective length was At Morogoro site, during Vuli, all seasonal e t a found to vary between 123 days at 80% prob rainfall during the experimental period were d ( ability to 156 days at 20 % probability. It has less than the long term seasonal mean (Figure r e h the longest effective growing season (147 days 5 d), and more of them covered the'maximum s i l at 50% probability) as compared to the other range. However, the 1993/94 seasonal rainfall b u areas. was within the minimum range. P e h t y Seasonal rainfall during the experimental During Masika, only two years, 1993 and 1994 b d period had seasonal rainfall greater than the long-term, e t seasonal mean rainfall and were within the n a r A plot of the loilg-term'mean rainfall and the maximum range· (Figure 5 e). None of the sea g e mean seasonal rainfall for - the four stations sonal rainfall fell within the minimum range c n e indicate that for- Ngudu, the seasonal rainfall during the experimental period. From the c i l for the two experimental years were both above discussion, the Vuli rains at both Kisan r e d above the minimum (Figure 5 a), However, for gara and Morogoro are quite low and tins ex n u 1997/98, the seasonal amount was above the plains the frequent crop failure during this sea y a long-teml' mean .while 1998/99_,season; it:was" son. I The same case explain .. the SituatiOli' in w e below the long term mean. Hombolo (Figure 5 f) .. t a G t e n i b a S y b d e c u d o r p e R 138 H. F. Mahoo et aI. 1400 1 1:ro! SHOO: E ' '-' 8Q) ; ~ an: ~4UJ 20J : tlill 0: 26yeaIS av=ge lWl/9l Ngudu b: Kisangara during'Masika a: ! .. lnng leon lm'B '. lSffi'91 19.W95 .avernge ) 2 c: Kisangara during Vuli d , Morogoro during Vuli 1 .·" 0 2 d e t a d ( r e h s i lID- l b u P e h t y b d e t n a lnngleon r g avernge a\efage. -, e '/ c --" I -/ n e c e: Morogoro during Masika ,- H·ombolo. i I;,·f: l ;~. . i' . r e ,,. \~ : !' . ." .'" d n , • • I.~ .: I. . u y Figure 5: Comparison ofseasonaltotals:with long te;~/averag~ ~t Ngudu; ro~a~gara); a w Morogoro an4,Hombolo' / I e t a G t e n i b a S y b d e c u d o r p e R Rainfall Variability 139 Conclusions Beltrando, G. 1990. Space time ·variability of .. rainfall in April and October - November 1. The analysis of the .rainfall pattern over East Africa during the period 1932 - though limited to a few stations shows 1983: Iruernational Journal of Climo.tol very high variability in both the Masika ogy, 10 (7): 691 - 702. and Vuli seasons. The coefficient of Masson, J.R. 1980. Rainfall variation in Swa variation, which ranged from 20 to ziland, 1917-1977, South African Journal 50 % over the growing period, implies a of Science. 76: 234-235. high uncertainty in crop yields. Ndorobo, B. 1973. Famine problems in Do doma District. Joumal of Geography As 2. In almost all the stations studied, the sociation of Tanzania. 8.: rail,lfall pattern shows that many of the Nieuwolt, S. 1989. Estimating the agricultural early starts of rainfall, are in effect false risks of tropical rainfall. Agric. For. Me starts as far as plant establishment and teorol. 45: 251 - 263. growth is concerned. A good example Ngana, J. O. 1990. Modelling for periodic is Hombolo, where the rains may start features in seasonal rainfall and its impli as early as October and November but cations to water resources and agricul the growing season effectively stalts tural planning. Institute of Resources As after 15 December. sessment Research Paper No. 27, Univer sity of Dar es Salaam. 23pp. 3. The rainfall pattern showed some cyclic Ngana, J. O. 1983. Rainfall, agricultural wave for a few stations (e.g. Kisangara droughts and famine in Dodoma Disuict. during Masika) while in the majority of Institute of Resources Assessment, Re search Report No. 60. University of Dar ) the stations trends were weak or did not 2 1 exist. The absence of trend in some of es Salaam. 27pp 0 2 the data series may be linked to gaps in Ogallo, LJ. 1982. Quasi-periodic patterns in d e t dIe raw data. This raises the issue of East African rainfall records. Kenya a d data quality, an issue that is often times Journal of Science and Technology. (A) ( r 3:43-54. e neglected by planners and project h s A history offamine: Dodomo. i implementers. Patton, M. 1971. l b Region. BRALUP Research Report No. u P 44, University of Dar es Salaam. e 4. The analysis in this paper has shown h t Prins, H.H.T and E.E. Loth 1988. Rainfall that trends may serve as a basis for y b patterns as a background to plant phenol extrapolating future rainfall patterns in d e ogy in Northern Tanzania. Journal of t the area and provide evidence of n a existing differences. When such Biogeography. 15 (3): 451 - 463. r g information is to be applied in a Rodhe, H. and H. Virji, 1976. Trends and e c periodicities of East African rainfall data. n different area, but of similar climatic e c i Monthly Weather Review: 104: 307 -313. l characteristics, physiographic factors r Tyson P.D and T.GJ. Dyer. 1978. The pre e have to be considered. d n dicted above nOffilal rainfall of the sev u y ~nties and the likelihood of droughts in a w eighties in South Africa. South Africa e t References Journal of Science. 74: 372 - 377. a G Venalainen, A. and M. Mhita, 1998. The vari t e Alusa, A.L. and M.T. Mushi. 1974. A study of n ability of rainfall in Tanzania. In De i b the onset, dliration and. cessation of the maree, G., J. Alexandre, M. de Dapper a S rains in East:, Africa, Proc. Int. Trop. (eds). Proc. Int. Conference Tropical y b Meteorology Meeting, Nairobi Kenya pp Climatology, ,Meteorology and Hydrol d e 133 -140. I c ogy, pp 191-199. u d o r p e R 140 H. F. Mahoo et al. Stern, R.D.; M.D. Dennett and I.e. Dale. Stem, R.D; J. Knock and H. Hack. 1990. 1982. Analysing daily rainfall measure INSTAT climatic guide. INSTAT ments to give the agronomically useful Statistical Services Centre, University of results: Direct methods. Experimental Reading. Agriculture. 18: 223 - 236. ) 2 1 0 2 d e t a d ( r e h s i l b u P e h t y b d e t n a r g e c n e c i l r e d n u y a w e t a G t e n i b a S y b d e c u d o r p e R