Abstract

This study analyzed the trends and spatio-temporal variability in rainfall and temperature, and the length of the rainy season (LRS) in the Lake Manyara catchment, Tanzania, covering a period between 1988 and 2018 using stations and satellite climate product. The Mann-Kendall statistical test, Sen's slope estimator, and inverse distance weighting interpolation techniques were used to detect the trends, magnitude of trends and spatial distribution of rainfall and temperature. A modified Stern's method and water balance concept were used for rainfall onset, cessation and LRS analysis, while a standardized precipitation index (SPI) was used to investigate the wetness or dryness of the area. The results showed high variability and decreasing trend (4 mm/y) in annual rainfall, and non-significant increasing trend for minimum and maximum temperature. Rainfall increased from the western to the northern part of the catchment whereas a reversal pattern was noticed for temperature. The SPI shows a signal of normal condition (about 65%) for all stations – with few years showing evidence of wetter and drier conditions. The LRS showed a decreasing trend indicating a potential negative influence on rain-dependent activities. There is a need, therefore, for adaptation measures such as improving water productivity and irrigation at the farm and catchment level.

INTRODUCTION

Climate variability and changes have been projected to increase by various climate modeling systems. These changes have been associated with an increase in anthropogenic greenhouse gases (GHGs) emissions leading to global warming. Other anthropogenic factors such as industrial activities, economic and population growth are also known to contribute to climatic variabilities (Pachauri et al. 2014). Anthropogenic activities such as industrial and agricultural activities have contributed significantly to the increase in atmospheric concentrations of greenhouse gases since the 20th century (Solomon et al. 2009). Undeniable evidence suggests that the impact of greenhouse gases on the Earth's atmosphere is significant and inevitable (Hartmann et al. 2017). The GHGs emissions impacts are associated with changes in various climatic parameters such as temperature and rainfall that may lead to catastrophic environmental and socioeconomic events including water scarcity, health problems, energy deficiencies, poor livelihoods, food insecurity, human insecurity, poor forestry practices, poor agricultural yields and low economic growth (Holman 2005; Chang'a et al. 2017).

Precipitation and temperature trends have a direct influence on the management of water resources. Various studies indicate that the changing patterns of precipitation have a direct impact on the catchments (Jones et al. 2015). Various studies report a considerable attention on rainfall and temperature variability related to the water resources. Trend detection in climate time series is one of the interesting research areas in climatology in recent years (Zarenistanak et al. 2014). Spatial- and temporal-scale rainfall trend analysis has been of concern since the past century because of the increased attention given to global climate change by the scientific community (Haigh 2010). Trend analysis of precipitation and temperature series research has also gained substantial attention lately due to improved capabilities to explore the climatic variability and extreme climatic events (Nenwiini & Kabanda 2013).

Sub-Saharan African countries are more vulnerable to the anticipated changes and variability in climatic trends, specifically in the intensifications of extreme events such as floods, drought and heat waves (IPCC 2018). These changes and variability of climate have shown unequal distribution from place to place. Some parts have experienced significant reduction in precipitation resulting in drought, while others receive increased rain that may result in flooding (Kijazi & Reason 2009). These anomalies are expected to continue or vary in the near future. Several studies have indicated that the increasing tendency of precipitation in the wet areas and decreasing tendency in the dry regions will lead to the persistence of floods and droughts (Deus & Gloaguen 2013; Trammell et al. 2015).

In Tanzania, for example, heterogeneous climate conditions are experienced due to the complicated topographical patterns, numerous inland water bodies, variation in vegetation types and land–ocean contrasts (Kijazi & Reason 2009). This complexity has led to experiences of the spatial climatic variation – a relatively small change in distances provides substantial variation (Karim et al. 2016). Furthermore, various reports from the Intergovernmental Panel on Climate Change (IPCC) have insisted on local and regional scale research on climate variability and change to provide necessary information to assess the impacts of climate variability and change on human and natural resources for sustainable development (Mamuye & Kebebewu 2018).

In Lake Manyara catchment, agriculture and tourism are the main economic activities that employ and provide income for the population. The catchment supports irrigated agricultural activities, livestock keeping and wildlife conservation. Intensification of human activities and climate variability and change has introduced stresses on water use in Lake Manyara catchment due to the increased freshwater demands and temporal variability of surface water flow. Water-related conflicts have been recorded in some communities in the catchment between pastoralist and farmers. Several studies have investigated spatial and temporal analysis of rainfall and temperature extreme indices in Tanzania (Zorita & Tilya 2002; Kijazi & Reason 2009; Lema & Majule 2009; Chang'a et al. 2017). However, these studies assessed large-scale climate drivers without examining climate change and variability on the catchment on a local scale. Therefore, this study aims at analyzing the temporal trend and spatial distribution of rainfall and temperature at the catchment level using different contexts.

METHODS

Study area

Lake Manyara is located within two regions of Tanzania – Arusha and Manyara – and five districts – Arusha, Babati, Monduli, Karatu and Mbulu. The Lake Manyara sub-basin (Figure 1(b)) comprises three catchments, namely, Lake Manyara (Figure 1(c)), Lake Burunge and Lake Babati. This study was conducted in the Lake Manyara catchment, which is at the upper part of Lake Manyara sub-basin with an area coverage of about 7,920 km2 (Attarzadeh et al. 2015). It's located between latitude 3°00′ S to 5°30′ S and longitude 35°30′ E and 37°00′ E. Lake Manyara has no outflow, no outlet to the sea or other lakes and is believed to be sensitive to climate variabilities (Olaka et al. 2010). Climatologically, the region is characterized by a bimodal rainy season with short rains between October and December and long rains between March and May. The southern part of the catchment is in the transition area with both unimodal and bimodal characteristics. The mean annual rainfall in the catchment is approximately 790 mm with an annual mean temperature of 19.4 °C (Yanda & Madulu 2005; Bachofer et al. 2014). The most dominant types of land use and land cover in the Manyara catchment are bushland, woodland, grassland, cultivated land and bare soil (Maerker et al. 2015). According to Ngana et al. (2003), during the dry season, herds of livestock migrate to the lake from other areas, thereby increasing pressure on the water resources at the catchment. Despite the pasture in the area, the most frequently cultivated crop types are bananas, maize, rice and vegetables (authors' observation) and most of the crops grown in this area have high demand for water, and belong to the group of the thirstiest crops (Lipper & Raney 2007). Both perennial and non-perennial rivers bring water to the lake while the main tributaries of Lake Manyara catchment are Mto wa Mbu, Mto wa Simba and Kirurumu River.

Figure 1

Map of Lake Manyara catchment showing: (a) its location within Tanzania, (b) the study area, and (c) study area elevation and weather station locations.

Figure 1

Map of Lake Manyara catchment showing: (a) its location within Tanzania, (b) the study area, and (c) study area elevation and weather station locations.

Climate data

Daily rainfall and temperature data for 32 rainfall weather stations (Table 1) were used in this study, however observed data were available for only three stations: Mbulu District Office, Monduli and Babati. The available rainfall data covering the period 1988–2018 were collected from the Tanzania Meteorological Agency (TMA). Due to lack of observed stations data for the rest of the weather stations outlined in Table 1, satellite-based rainfall and temperature products for the same period were collected and utilized. The satellite-based rainfall and temperature data were obtained from the NASA Langley Research Center (LaRC) POWER project (https://www.power.larc.nasa.gov/data-access-viewer). The NASA POWER ability to successfully reproduce the climate pattern of stations data has been previously demonstrated (Larbi et al. 2018). All data were then quality controlled to check outliers and negative rainfall values.

Table 1

List of meteorological stations whose data (19882018) were used in the present study

No.Station nameStation #Longitude (deg.)Latitude (deg.)Elevation (m)
Mbulu District Office 9335001 35.55 −3.87 1,737 
Kainam 9335009 35.52 −3.93 1,881 
Idulu Ginnery 9335011 35.75 −3.95 1,005 
Mbulumbulu 9335024 35.78 −3.27 1,828 
Mbulumbulu 111 9335026 35.82 −3.27 1,549 
Mbulumbulu iv 9335027 35.83 −3.28 1,508 
Mbulu devp farm 9335029 35.52 −3.88 1,828 
MtowaMbu Game Department 9335030 35.83 −3.30 1,066 
Mtowambu Agriculture office 9335032 35.85 −3.37 975 
10 Lake Manyara Maji 9335034 35.85 −3.4 999 
11 Karatu Agricultural 9335046 35.70 −3.35 999 
12 Tarosero Estate 9336007 36.43 −3.30 1,402 
13 Rasharasha Estate 9336008 36.47 −3.32 1,524 
14 Monduli 9336014 36.45 −3.32 1,585 
15 Eluanata Dam 9336019 36.28 −3.40 1,371 
16 Ardai 1 9336022 36.40 −3.42 1,371 
17 Ardai 11 9336023 36.35 −3.45 1,371 
18 Ardai 111 9336024 36.28 −3.42 1,371 
19 Ardai iv 9336025 36.39 −3.38 1,371 
20 Ardai v 9336026 36.40 −3.35 1,371 
21 Ardai Ranch 9336043 36.33 −3.5 2,658 
22 Mayoka-moya 9336047 36.00 −3.5 1,071 
23 Mangola 9336050 36.32 −3.60 999 
24 Lente Estate 9336063 36.13 −3.47 999 
25 Singu Estates 9431002 35.67 −4.20 1,737 
26 Margaret Coffee Estates 9435009 35.63 −4.12 1,365 
27 Mbogo Estates 9435012 35.68 −4.10 1,127 
28 Dukumomaay 9435023 35.5 −4.13 2,133 
29 Magugu Primary School 9435029 35.77 −4.02 999 
30 Babati 9435030 35.75 −4.22 999 
31 Darakuta Ranch 9435033 35.58 −4.00 1,128 
32 Magugu TPRI 9435035 35.77 −4.00 999 
No.Station nameStation #Longitude (deg.)Latitude (deg.)Elevation (m)
Mbulu District Office 9335001 35.55 −3.87 1,737 
Kainam 9335009 35.52 −3.93 1,881 
Idulu Ginnery 9335011 35.75 −3.95 1,005 
Mbulumbulu 9335024 35.78 −3.27 1,828 
Mbulumbulu 111 9335026 35.82 −3.27 1,549 
Mbulumbulu iv 9335027 35.83 −3.28 1,508 
Mbulu devp farm 9335029 35.52 −3.88 1,828 
MtowaMbu Game Department 9335030 35.83 −3.30 1,066 
Mtowambu Agriculture office 9335032 35.85 −3.37 975 
10 Lake Manyara Maji 9335034 35.85 −3.4 999 
11 Karatu Agricultural 9335046 35.70 −3.35 999 
12 Tarosero Estate 9336007 36.43 −3.30 1,402 
13 Rasharasha Estate 9336008 36.47 −3.32 1,524 
14 Monduli 9336014 36.45 −3.32 1,585 
15 Eluanata Dam 9336019 36.28 −3.40 1,371 
16 Ardai 1 9336022 36.40 −3.42 1,371 
17 Ardai 11 9336023 36.35 −3.45 1,371 
18 Ardai 111 9336024 36.28 −3.42 1,371 
19 Ardai iv 9336025 36.39 −3.38 1,371 
20 Ardai v 9336026 36.40 −3.35 1,371 
21 Ardai Ranch 9336043 36.33 −3.5 2,658 
22 Mayoka-moya 9336047 36.00 −3.5 1,071 
23 Mangola 9336050 36.32 −3.60 999 
24 Lente Estate 9336063 36.13 −3.47 999 
25 Singu Estates 9431002 35.67 −4.20 1,737 
26 Margaret Coffee Estates 9435009 35.63 −4.12 1,365 
27 Mbogo Estates 9435012 35.68 −4.10 1,127 
28 Dukumomaay 9435023 35.5 −4.13 2,133 
29 Magugu Primary School 9435029 35.77 −4.02 999 
30 Babati 9435030 35.75 −4.22 999 
31 Darakuta Ranch 9435033 35.58 −4.00 1,128 
32 Magugu TPRI 9435035 35.77 −4.00 999 

Trends and magnitude of trend detection

An investigation of the annual and monthly series and trend analysis was performed for each station and for the entire catchment. The annual rainfall and temperature trends were computed using the Mann-Kendall (MK) statistical test at the confidence level of 95% (Yavuz & Erdoğan 2011). The MK statistic tests for whether to accept the alternative hypothesis (Ha) which states the presence of a monotonic trend or to accept the null hypothesis (Ho) which states that no monotonic trend occurred were used. This approach has been suggested by the World Meteorological Organization (WMO) as the best approach to assess trends in environmental data time series (Huret & Legras 2014). The method is simple, robust against outliers and can handle missing values (Gao et al. 2017). The MK tests (Equations (1)–(5)) calculate the slope of the line formed by plotting the variable of interest against time, but only considers the sign and not the magnitude of this slope. The MK statistic S is computed as follows: 
formula
(1)
where xj and xk are sequential data values for the time series data of length n. The sgn series is defined as: 
formula
(2)
whenever there is an identical and independent dataset distribution, the mean of S is zero whereas the variance of S is given by Equation (3). 
formula
(3)
where ti is the extent of any given tie. Σti denotes the summation over all ties and is only used if the data series contain tied values. The standard normal variate Z is calculated as indicated in Equation (4): 
formula
(4)

The trend is decreasing if Z is negative and increasing if Z is positive. H0, the null hypothesis of no trend, is rejected if the absolute value of Z is greater than Z1- α/2, where Z1- α/2 is obtained from the standard normal cumulative distribution tables.

The Sen's slope estimator (Equations (5) and (6)) was used to determine the magnitude of the trends after obtaining the direction of the trend with the Mann–Kendall test. The method uses a linear model to calculate the change of slope, and the variance of the residuals should be constant in time (Sen 1968). 
formula
(5)
 
formula
(6)
where Qi is the slope between data points Xj and Xk, Qmed is median slope estimator which reflects the direction of the trend in the data.

Spatial distribution analysis of rainfall and temperature

The inverse distance weighting (IDW) interpolation technique was used to spatially interpolate the rainfall and temperature patterns between the observation stations using the Quantum Geographic Information System (QGIS). The IDW averages the weights of observation values after which the interpolated points' neighbors were identified (Shepard 1968). The averaged values of annual precipitation and temperature calculated over 30 years for the 32 weather stations were used as the input data in the QGIS software (Abolverdi et al. 2016). The IDW method is given by Equation (7) as follows: 
formula
(7)
where p is the number of points, d is the distance between points, and w is the weighting function.

Rainfall anomaly and LRS analysis

An investigation of dry, normal and wet years in the Lake Manyara catchment was performed using the Standardized Precipitation Index (SPI) approach for the time intervals of 12 months (McKee et al. 1993). The classification scheme suggested by the study of McKee et al. (1993) (Table 3) was used to determine wet or dry intensity over the study area.

The onset and cessation dates were computed for the Babati, Monduli, Karatu and Mbulu District office stations for each year based on a modified method defined by Stern et al. (1981). In the present study, the rainfall onset date is defined as the date with the total of at least 20 mm of rainfall within 2 consecutive days, in which the starting day must be wet (at least 0.85 mm rainfall recorded), followed by a no dry period of seven (7) or more consecutive days occurring in the following 30 days. The cessation date was computed based on the Instat© concept of the water balance (i.e. the first date the soil profile is empty after a given date) which is calculated for each year. The length of the rainy season (LRS) is then obtained from the difference between the rainfall onset and cessation date.

RESULTS AND DISCUSSION

Monthly mean rainfall and temperature

The results for the mean monthly rainfall, maximum and minimum temperature for Babati, Monduli and Mbulu District office meteorological stations within the Lake Manyara catchment for the period 1988–2018 are presented in Figure 2. The results for rainfall (Figure 2(a)–2(c)) show two rainy season periods between March and May (MAM season), and between October and December (OND season). Between June and September (JJAS), the dry condition exists over the catchment. The maximum temperature (Figure 2(d)–2(f)) indicates the highest temperature during the month of October and the lowest for the months of June and July. These conditions can be caused by various factors, including topography and other local factors. The results for the minimum temperature (Figure 2(g)–2(i)) show the highest temperature in December and the lowest temperature for the three stations in July. The present study found that the status of catchment average rainfall from 1988 to 2018 was 780 mm, and the maximum and minimum temperatures were 27.8 °C and 15.0 °C, respectively.

Figure 2

Mean monthly cycle of rainfall (a–c), maximum temperature (d–f) and minimum temperature (g–i) for Babati (a, d, g), Monduli (b, e, h), and Mbulu District office (c, f, i) from 1988 to 2018.

Figure 2

Mean monthly cycle of rainfall (a–c), maximum temperature (d–f) and minimum temperature (g–i) for Babati (a, d, g), Monduli (b, e, h), and Mbulu District office (c, f, i) from 1988 to 2018.

Spatial-temporal variability of rainfall and temperature

The results for the annual series plots of rainfall for the Lake Manyara catchment (Figure 3), and for the eastern, western and the southern part of the catchment (see Figure S1 in the attached Supplementary Material) show a year to year variability with a mean annual value of 780 mm between 1988 and 2018. The results of annual mean of maximum temperature (Figure 4) and annual mean of minimum temperature (Figure 5) indicate high variability within 30 years. The average of the annual minimum temperature showed an increasing trend at a rate of 0.025 °C/y compared to the maximum temperature which showed a slight yearly decrease in trend in all stations for the entire catchment. Similar to the present study, annual rainfall, maximum and minimum temperature in several studies done elsewhere showed variations (Alemu & Bawoke 2019).

Figure 3

Annual rainfall distribution for (a) Babati (b) Monduli (c) Mbulu District office (d) Arithmetic mean of entire catchment for the period 1988–2018.

Figure 3

Annual rainfall distribution for (a) Babati (b) Monduli (c) Mbulu District office (d) Arithmetic mean of entire catchment for the period 1988–2018.

Figure 4

Mean annual maximum temperature time series for (a) Babati, (b) Monduli, (c) Mbulu District office, and (d) the entire catchment for the period 1988–2018.

Figure 4

Mean annual maximum temperature time series for (a) Babati, (b) Monduli, (c) Mbulu District office, and (d) the entire catchment for the period 1988–2018.

Figure 5

Mean annual minimum temperature for (a) Babati, (b) Monduli, (c) Mbulu District office, and (d) the entire catchment from 1988 to 2018.

Figure 5

Mean annual minimum temperature for (a) Babati, (b) Monduli, (c) Mbulu District office, and (d) the entire catchment from 1988 to 2018.

The spatial distribution of the annual rainfall reveals that the highest amount (>700 mm) is received at the north part (Ngorongoro and Karatu) of the catchment (Figure 6), while the lowest rainfall amount of about 600 mm is noticed in the eastern part of the catchment. This observed difference in spatial rainfall might be due to the high elevation in the north where orographic effect seems to be more dominant compared to other regions. In the case of temperature (Figure 7), the spatial distribution reveals that the annual average of both maximum and minimum temperature is highest in the eastern part (around Monduli) and lowest in the northern regions (Ngorongoro and Karatu) of the catchment. This may be attributed to the high elevation of Ngorongoro and Karatu at the northern part of the catchment. This variability in climate has implications in various field including hydrology and agriculture due to over-dependence on agriculture and livestock keeping in the catchment.

Figure 6

Spatial distribution of mean annual rainfall temperature at the Manyara catchment for the period 1988–2018.

Figure 6

Spatial distribution of mean annual rainfall temperature at the Manyara catchment for the period 1988–2018.

Figure 7

Spatial distribution of mean annual (a) maximum temperature (b) minimum temperature at the Manyara catchment for the period 1988–2018.

Figure 7

Spatial distribution of mean annual (a) maximum temperature (b) minimum temperature at the Manyara catchment for the period 1988–2018.

Trends of rainfall and temperature

The results for the MK trend analysis (Table 2a) of rainfall indicate a non-significant decreasing trend for the three climate stations and the entire catchment. A decreasing trend in the annual rainfall at the rate of 4.3mm/y and 5.0mm/y was noticed for the Babati and Monduli stations respectively, while a slightly decreasing trend was noticed for the Mbulu station. The entire catchment also showed a decreasing trend in annual rainfall at a rate of 5mm/y. The MK test also computed for the maximum temperature reveals a non-significant increasing trend in two out of the three stations (Table 2b). Unlike maximum temperature, the mean annual minimum temperature showed a significant positive trend for the three stations (Table 2c). This means that during the night, the temperature increased significantly due to the release of longwave radiation. The minimum temperature showed an increasing trend at a rate of 0.024 °C/y compared to the maximum temperature which showed a slight yearly decreasing trend in all stations and for the entire catchment. Other studies indicate no statistically significant trends in annual rainfall (Mengistu et al. 2014). Likewise, warming trends of mean annual minimum temperatures have been reported to be more pronounced than maximum temperatures (Tesso et al. 2012; Chang'a et al. 2017).

Table 2

Mann-Kendall and Sen's slope result for annual (a) rainfall (b) maximum temperature (c) minimum temperature

StationZSen's slopeSVar (S)Kendall's tauP-valueα
Babati −0.85 −4.32 −51 3,461.67 −0.11 0.4 0.05  
Monduli −0.85 −5.08 −51 3,461.67 −0.11 0.4 0.05 (a) 
Mbulu District office −1.36 −11.97 −81 3,461.66 −0.17 0.17 0.05  
Catchment 1.557 97 3,801.67 0.2 0.12 0.05  
Babati 1.02 0.01 61 3,439.67 0.13 0.31 0.05  
Monduli −1.82 −0.05 −108 3,441.33 −0.24 0.07 0.05 (b) 
Mbulu District office 0.07 0.03 3,443 0.01 0.9 0.05  
Catchment −0.46 −0.006 −29 3,779.67 −0.06 0.65 0.05  
Babati 4.82 0.03 281 3,380.33 0.63 0.05  
Monduli 4.07 0.03 238 3,394.67 0.53 0.05 (c) 
Mbulu District office 4.39 0.03 259 3,461.67 0.56 0.05  
Catchment 4.07 0.024 248 3,682 0.53 0.05  
StationZSen's slopeSVar (S)Kendall's tauP-valueα
Babati −0.85 −4.32 −51 3,461.67 −0.11 0.4 0.05  
Monduli −0.85 −5.08 −51 3,461.67 −0.11 0.4 0.05 (a) 
Mbulu District office −1.36 −11.97 −81 3,461.66 −0.17 0.17 0.05  
Catchment 1.557 97 3,801.67 0.2 0.12 0.05  
Babati 1.02 0.01 61 3,439.67 0.13 0.31 0.05  
Monduli −1.82 −0.05 −108 3,441.33 −0.24 0.07 0.05 (b) 
Mbulu District office 0.07 0.03 3,443 0.01 0.9 0.05  
Catchment −0.46 −0.006 −29 3,779.67 −0.06 0.65 0.05  
Babati 4.82 0.03 281 3,380.33 0.63 0.05  
Monduli 4.07 0.03 238 3,394.67 0.53 0.05 (c) 
Mbulu District office 4.39 0.03 259 3,461.67 0.56 0.05  
Catchment 4.07 0.024 248 3,682 0.53 0.05  

Standardized precipitation index results

The analysis was focused on understanding the sensitivity of SPI to rainfall deviation from the annual mean and discovered the wet, normal and dry condition in the area. The SPI analysis results (Table 3 and Figure 8) based on McKee et al. (1993) classification shows the signal of normal conditions in all stations with few years showing evidence of wetter and drier conditions. From the SPI results, more positive values were obtained, indicating a wet year in the catchment mostly in the years 2002 and 2006, whereas the more negative values obtained in the years 1993 and 2003. Also, the SPI results revealed that in the catchment, drought conditions were more sustained compared to wet conditions. Furthermore, the SPI trends showed an increase in the intensity of drought in many stations within the region. This has also been demonstrated by a number of other studies: increase in drought severity associated with higher water demand as a result of evapotranspiration. SPI has the advantage of being multi-scalar, which is crucial for drought analysis and monitoring (Beguería et al. 2014; Yu et al. 2014).

Table 3

Classification of the SPI and the corresponding number of years for each station in Lake Manyara catchment from 1988 to 2018

SPI valuesConditiona(a) Babati(b) Monduli(c) Mbulu District office
2.00 and above Extremely wet 
1.50 to 1.99 Severely wet 
1.00 to 1.49 Moderately wet 
0.99 to −0.99 Normal 20 20 19 
−1.00 to −1.49 Moderate drought 
−1.50 to −1.99 Severe drought 
−2.0 and less Extreme drought 
SPI valuesConditiona(a) Babati(b) Monduli(c) Mbulu District office
2.00 and above Extremely wet 
1.50 to 1.99 Severely wet 
1.00 to 1.49 Moderately wet 
0.99 to −0.99 Normal 20 20 19 
−1.00 to −1.49 Moderate drought 
−1.50 to −1.99 Severe drought 
−2.0 and less Extreme drought 
Figure 8

SPI results for (a) Babati (b) Monduli and (c) Mbulu District office.

Figure 8

SPI results for (a) Babati (b) Monduli and (c) Mbulu District office.

Rainfall onset, cessation and length of the rainy season

Rainfall in Lake Manyara catchment exhibits variability for both time and space. The start and end of rainfall seasons for the period from 1988 to 2018 are given in Table 4. The results show inconsistency and high variations in the start of the rainy season for the different locations. On average, the rainy season starts in November and ends in April or May, although the catchment location seems to have both unimodal and bimodal modes of rainfall. The results also show that the rainy season mostly starts early in the areas with high altitude around Karatu and Mbulu Agriculture stations and later in the western part around Monduli. A similar pattern is followed during the cessation period.

Table 4

Onset and cessation patterns of rainfall for 30 years in the Lake Manyara catchment

Onset
Cessation
YearKaratuBabatiMbulu District officeMonduliKaratuBabatiMbulu District officeMonduli
1989 25-Nov 02-Jan 07-Dec 26-Dec 11-May 07-May 14-May 23-May 
1990 12-Nov 12-Nov 29-Nov 08-Dec 17-May 06-May 30-Apr 09-May 
1991 15-Nov 11-Dec 09-Dec 19-Dec 11-May 01-May 29-Apr 15-Apr 
1992 18-Nov 30-Jan 24-Nov 09-Apr 15-Apr 15-Apr 18-May 08-May 
1993 01-Jan 20-Nov 19-Nov 10-Nov 20-May 15-Apr 15-Apr 17-Apr 
1994 02-Nov 16-Nov 06-Jan 18-Dec 18-Apr 15-Apr 03-May 15-Apr 
1995 30-Oct 07-Nov 30-Nov 23-Nov 15-Apr 24-May 03-Jun 26-May 
1996 27-Nov 26-Dec 02-Feb 17-Feb 28-May 28-Apr 13-May 29-Apr 
1997 16-Nov 17-Nov 05-Apr 17-Nov 27-Apr 27-Apr 25-Apr 05-Jun 
1998 04-Dec 17-Nov 02-Jan 17-Nov 05-Jun 15-Apr 19-May 26-May 
1999 29-Nov 01-Dec 04-Dec 17-Nov 15-Apr 15-Apr 17-Apr 05-Jun 
2000 07-Nov 08-Feb 23-Nov 17-Nov 20-May 21-Apr 17-Apr 25-Apr 
2001 18-Nov 21-Nov 21-Nov 21-Nov 15-Apr 19-Apr 15-Apr 03-May 
2002 28-Oct 06-Nov 21-Nov 22-Oct 15-Apr 15-Apr 25-Apr 01-May 
2003 29-Oct 06-Nov 06-Nov 23-Oct 15-Apr 21-Apr 15-Apr 15-Apr 
2004 23-Nov 09-Dec 05-Jan 03-Feb 15-Apr 15-Apr 01-May 02-May 
2005 18-Dec 21-Nov 26-Nov 25-Dec 30-Apr 21-Apr 22-Apr 02-May 
2006 23-Nov 28-Nov 02-Dec 16-Oct 03-May 15-Apr 22-May 19-May 
2007 12-Oct 06-Nov 01-Nov 21-Oct 06-May 18-Apr 15-Apr 15-Apr 
2008 01-Jan 19-Nov 16-Jan 22-Mar 16-Apr 07-May 03-May 08-May 
2009 12-Nov 29-Nov 08-Nov 06-Dec 09-May 29-Apr 26-Apr 24-Apr 
2010 08-Nov 07-Nov 22-Nov 10-Dec 17-Apr 15-Apr 03-May 12-May 
2011 24-Nov 07-Dec 14-Nov 14-Feb 22-Apr 15-Apr 24-Apr 21-Apr 
2012 02-Nov 09-Nov 14-Nov 24-Nov 15-Apr 15-Apr 18-Apr 18-May 
2013 06-Dec 13-Dec 11-Dec 12-Nov 15-Apr 15-Apr 26-Apr 27-Apr 
2014 28-Nov 21-Jan 05-Feb 03-Nov 13-May 15-Apr 29-Apr 01-May 
2015 03-Nov 18-Nov 15-Nov 03-Nov 15-Apr 20-May 15-Apr 15-Apr 
2016 15-Nov 09-Jan 08-Jan 17-Nov 25-May 15-Apr 11-Apr 09-May 
2017 04-Nov 12-Nov 18-Nov 19-Nov 21-May 15-Apr 15-Apr 16-Apr 
2018 18-Oct 05-Jan 22-Dec 01-Jan 15-Apr 09-May 15-Apr 15-Apr 
Onset
Cessation
YearKaratuBabatiMbulu District officeMonduliKaratuBabatiMbulu District officeMonduli
1989 25-Nov 02-Jan 07-Dec 26-Dec 11-May 07-May 14-May 23-May 
1990 12-Nov 12-Nov 29-Nov 08-Dec 17-May 06-May 30-Apr 09-May 
1991 15-Nov 11-Dec 09-Dec 19-Dec 11-May 01-May 29-Apr 15-Apr 
1992 18-Nov 30-Jan 24-Nov 09-Apr 15-Apr 15-Apr 18-May 08-May 
1993 01-Jan 20-Nov 19-Nov 10-Nov 20-May 15-Apr 15-Apr 17-Apr 
1994 02-Nov 16-Nov 06-Jan 18-Dec 18-Apr 15-Apr 03-May 15-Apr 
1995 30-Oct 07-Nov 30-Nov 23-Nov 15-Apr 24-May 03-Jun 26-May 
1996 27-Nov 26-Dec 02-Feb 17-Feb 28-May 28-Apr 13-May 29-Apr 
1997 16-Nov 17-Nov 05-Apr 17-Nov 27-Apr 27-Apr 25-Apr 05-Jun 
1998 04-Dec 17-Nov 02-Jan 17-Nov 05-Jun 15-Apr 19-May 26-May 
1999 29-Nov 01-Dec 04-Dec 17-Nov 15-Apr 15-Apr 17-Apr 05-Jun 
2000 07-Nov 08-Feb 23-Nov 17-Nov 20-May 21-Apr 17-Apr 25-Apr 
2001 18-Nov 21-Nov 21-Nov 21-Nov 15-Apr 19-Apr 15-Apr 03-May 
2002 28-Oct 06-Nov 21-Nov 22-Oct 15-Apr 15-Apr 25-Apr 01-May 
2003 29-Oct 06-Nov 06-Nov 23-Oct 15-Apr 21-Apr 15-Apr 15-Apr 
2004 23-Nov 09-Dec 05-Jan 03-Feb 15-Apr 15-Apr 01-May 02-May 
2005 18-Dec 21-Nov 26-Nov 25-Dec 30-Apr 21-Apr 22-Apr 02-May 
2006 23-Nov 28-Nov 02-Dec 16-Oct 03-May 15-Apr 22-May 19-May 
2007 12-Oct 06-Nov 01-Nov 21-Oct 06-May 18-Apr 15-Apr 15-Apr 
2008 01-Jan 19-Nov 16-Jan 22-Mar 16-Apr 07-May 03-May 08-May 
2009 12-Nov 29-Nov 08-Nov 06-Dec 09-May 29-Apr 26-Apr 24-Apr 
2010 08-Nov 07-Nov 22-Nov 10-Dec 17-Apr 15-Apr 03-May 12-May 
2011 24-Nov 07-Dec 14-Nov 14-Feb 22-Apr 15-Apr 24-Apr 21-Apr 
2012 02-Nov 09-Nov 14-Nov 24-Nov 15-Apr 15-Apr 18-Apr 18-May 
2013 06-Dec 13-Dec 11-Dec 12-Nov 15-Apr 15-Apr 26-Apr 27-Apr 
2014 28-Nov 21-Jan 05-Feb 03-Nov 13-May 15-Apr 29-Apr 01-May 
2015 03-Nov 18-Nov 15-Nov 03-Nov 15-Apr 20-May 15-Apr 15-Apr 
2016 15-Nov 09-Jan 08-Jan 17-Nov 25-May 15-Apr 11-Apr 09-May 
2017 04-Nov 12-Nov 18-Nov 19-Nov 21-May 15-Apr 15-Apr 16-Apr 
2018 18-Oct 05-Jan 22-Dec 01-Jan 15-Apr 09-May 15-Apr 15-Apr 

The results of the length of seasonal rainfall (LRS) in days are presented in Figure 9 and show high variations and a decreasing trend except for the Mbulu District office station. These variations may be attributed to variability in the onset and cessation of the season. The Mbulu region shows the lowest LRS compared to other regions whereas Karatu indicates the highest LRS in the catchment. This variability in the duration of the rainy season suggests the possible influence of climate change in these areas which may have implications for crop growth, especially for crops which require long seasons of rain to mature. Other researchers have also found that in Tanzania, variations in the onset and cessation as well as length of the rainy season are usually insignificant (Kimambo & Ndeto 2018).

Figure 9

Length of seasonal rainfall in days for (a) Karatu (b) Babati (c) Monduli and (d) Mbulu District office.

Figure 9

Length of seasonal rainfall in days for (a) Karatu (b) Babati (c) Monduli and (d) Mbulu District office.

CONCLUSIONS

This study provides a clear picture of the spatial and temporal variability of rainfall and temperature, trends and length of rainy season over the past 30 years for the Lake Manyara catchment in Tanzania. From this study, the climate status of Lake Manyara catchment shows high variability in both spatial and temporal rainfall and temperature distribution. Decreasing trends in annual rainfall was found in most stations while the maximum and minimum temperature showed an increasing trend in the area. Locally, drought conditions showed a more enhanced signal compared to wet conditions. The periods of onset and end of the rainy season were inconsistent in most of the meteorological stations leading to high variability in the length of the rainy season.

The present study gives valuable information to authorities responsible for planning of water resources within the catchment and in the country at large. Therefore, climate change adaptation policies in the studied catchment may include measures such as changing the crop type and improving water productivity and irrigation practices at the farm and basin level.

ACKNOWLEDGEMENTS

We acknowledge financial support from the Water Infrastructure and sustainable Energy Futures (WISE-Features) and the Tanzania Meteorological Authority (TMA) their support throughout this study. Professor Alfred N. N. Muzuka, who passed away during the course of this work – Alfred, you will always be remembered for your diligence and adherence to standards. Thank you!

SUPPLEMENTARY MATERIAL

The Supplementary Material for this paper is available online at https://dx.doi.org/10.2166/wcc.2020.197.

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Supplementary data