Journal of Global Resources

Volume 3

July 2016
Page 111-121
ISSN: 2395-3160 (Print), 2455-2445 (Online)

14
ASSESSMENT OF TRADITIONAL ENVIRONMENTAL KNOWLEDGE SYSTEMS APPLIEDTO RAINFALL FORECASTING IN ROMBO DISTRICT, TANZANIA
Evarist Fundisha1*, Wilium Rugumamu2 and Deogratius M. M. Mulungu3
1
Department of Geography, Mkwawa University College of Education, Iringa, Tanzania
Email: evarist_fundisha@yahoo.com
2
Department of Geography, University of Dar es Salaam, Tanzania.
3
Department of Water Resource Engineering, College of Engineering and Technology,
University of Dar es Salaam, Tanzania.
Abstract: Accurate rainfall forecasting enables rural communities to make informed, timely
and effective decisions on their farming activities presumably leading to increased productivity.
As such, a more appropriate, accessible and user friendly rainfall forecasting methods andinformation is needed. This study was conducted to assess efficacy of the traditional
environmental knowledge systems applied to rainfall forecasting in Rombo District, Tanzania
for sustainable climate change adaptation. Triangulation of data collection methods were used
to obtain diverse and detailed information. Out of 611 interviewees, 44.2% could forecast
rainfall using environmental indicators (EIs) of whom 75.5% were unable to forecast beyond a
week. It was somewhat difficult to judge the reliability of some EIs in forecasting rainfall
because the same indicator was rated differently by different respondents, as very reliable,
reliable, not reliable and no longer reliable. Although half of the respondents anticipated adecrease in rainfall, forecasting from Global Climate Models revealed the opposite. The study
recommends a multidisciplinary approach and sharing of knowledge and information for
effective rainfall forecasting.
Key words:

Climate change adaptation, Environmental indicator, Global Climate Model,
Rainfall forecast, Traditional environmental knowledge systems

Introduction
Rainfall is an important element of weather that provides water for living organisms. Rainfed
agriculture on which most of the third world population depends for their livelihoods is
influenced by the timing, quantity and intensity of rainfall. From time immemorial, humans havebeen using environmental indicators (EIs) to predict timing, quantity and intensity of rainfall.
The knowledge developed by people with extended histories of interaction with naturalenvironment is known as Traditional Environmental Knowledge systems (TEK systems)1,2.
TEK is passed from one generation to the next through cultural transmission, and is stored
within people’s memories3. Methods of applying, retaining and communicating TEK, especiallyin times of rapid environmental and political change are not reliable, thus, TEK needs to be
documented4. In this time of global climate change, it is also important to document TEK
systems because they provide a new source of climatic data over a spatial-temporal scale ofseveral generations in regions where there is paucity of documented data5.

111

One important step in reducing vulnerability to climate hazards is to develop an early warning
system for forecasting the timing, quantity and intensity of rainfall6. In modern societies, rainfall
forecasting is usually done through Global Climate Models (GCMs). Although GCMs can beused to provide rainfall information at local scales through downscaling, such information islimited to end users who have access to it through communication media such as radio,
television, newspapers and internet7. Delivery vehicles such as time specific programmes
through electronic media do not take into account the limited time individuals have, and thismay limit their capacity for adaptation8. Furthermore, wealthier households benefit more thanthe poor from the modern rainfall forecasts9. This calls for more appropriate, accessible anduser friendly rainfall forecasting methods and information.
Studies on rainfall forecasting using EIs are numerous2, 3, 7, 10, 11, 12, 13, 14. Indigenous andlocal people’s forecasting methods can offer insights for improving the value of modern
scientific forecasting. It is also emphasised that indigenous forecasting practices are needs
driven. They focus on the locality, timing of rains and are communicated in local languages byexperts well known and entrusted in that particular community4. A number of researchers ofindigenous knowledge have challenged integration of TEK systems into Modern
Environmental Knowledge Systems (MEK systems) to rainfall forecasting by arguing that TEK
systems lack benchmarks12. However, others have maintained that integration of the twosystems of rainfall forecasting will increase overall reliability3, 7. Despite these discrepancies,
studies which seek to relate TEK and MEK systems as applied to rainfall forecasting in Rombo
District are sparse to date. The present study was, therefore, to bridge this gap by assessing
the efficacy of TEK systems applied to rainfall forecasting and relating them to MEK systems
for sustainable climate change adaptation.
Methodology
This study was conducted in Rombo District (Figure 1). The study area has a mountainous
climate with two distinctive rainfall regimes: a short rainy season occurring from October toDecember and a long rainy season from March to May15. The dominant economic activity inthe region is subsistence agriculture, which involves cultivation of rainfed crops including
coffee, bananas, maize and beans, cassava, millet, sorghum, sweet potatoes, sunflower,
yams, and various fruits and vegetables. Coffee and bananas are the main cash crops. Zerograzing livestock is another economic activity that involves keeping of cattle, goats, sheep,
pigs and poultry. Micro-businesses such as small shops and local brew bars, and microindustries such as carpentry and bicycle repairs are also practised to sustain family livelihoods
with few individuals earning income from wage employment in both private and public sectors.
Questionnaires were administered to 611 respondents identified through random
sampling in eight villages, in three agroecological zones (AEZ) (Figure 1) to collect informationon the socio-economic characteristics of respondents, knowledge on using EIs to forecast
rainfall and the perceptions of future rainfall characteristics. Three focus group discussions
were also used to collect narrative information on the subject matter from 21 participants who
were purposively selected from the three AEZs of the study area. In-depth interviews wereconducted with three Agricultural Extension Officers (AEOs) and the District Agricultural andLivestock Development Officer (DALDO) selected purposively from the study area todetermine official support for rainfall forecasting. Field observations were also employed tocollect none verbal information. Recorded and climate model rainfall data were obtained from112

Tanzania Meteorological Agency (TMA) at Ubungo Plaza and the Intergovernmental Panel on
Climate Change (IPCC) Data Distribution centre respectively. Quantitative data were analysedwith a Statistical Package for Social Sciences Software (SPSS, version 16) and Word Excel,
while qualitative data from interviews and focus group discussions were thematically analysedand presented in frequency tables, figures and narrations.
Rainfall prediction for the overall region was done using 22 GCMs downloaded fromthe IPCC Data Distribution Centre website. The downloaded GCMs were characteristicallysensitive to 21st Century Scenarios which considered the atmosphere to have elevatedconcentration of carbon dioxide as compared to preindustrial era. Since the GCMs’
coordinates (i.e. latitudes and longitudes) were not always the same as those of the study
area, decisions were made based on the enclosure of the GCM coordinates in relation to thestudy area. The accuracy and precision of the GCM data were maintained by selecting theupper latitudes and longitudes (centroids) for each model. The generated data were used to
predict future rainfall changes of the study area.
After downloading the historical precipitation data for each model, skill score tests were
performed by summing the minimum relative frequencies (rf) of either observed (recorded)ndata or GCM data [i.e. Skill Score =∑ min(rfObserve , rfGCM ) ]. Due to differences in skill
iscore results, models with greater than 50% and which are in scenario A2 family were chosen
for predicting future rainfall changes (DeltaChange). The GCMs in scenario A2 family assumes
that the world is characterised by independently operating, self-reliant nations with
continuously increasing population and regionally oriented economic development16. Based onthese criteria, three GCMs; ncar_ccsm3_3, ipsl_cm4 and gfdl_cm2.0 were selected todetermine future rainfall changes in which changes in GCM historical rainfall data weresubtracted from the forecasted rainfall and divided by changes in GCM historical rainfall data
(i.e. DeltaChange =DeltaGCMHistorical − DeltaGCMForecasted).
DeltaGCMHistorical

Since each model showed variations in rainfall (i.e. increase or decrease), multi-model means
were determined and used to plot rainfall changes (%) against time (year) to minimize
individual model bias. The 1961-1990 rainfall data were taken as the baseline from which the
2011-2030 and 2046-2065 rainfall changes were referred.

113

Figure 1: Study area

Findings and Discussion
Socio-economic characteristics of respondents
Out of 611 respondents contacted for direct questionnaire, 85.3% were males and 14.7%
females. Of these, 90% were married, with 10.7% having attained no formal education. All of
the respondents engaged in subsistence crop production. Maize and beans are grown in allagroecological zones (AEZs) of the study area, while bananas and coffee are mostly grown in
the highland and intermediate AEZs. About 99.2% mentioned to practise zero grazing
livestock keeping in which 92.2% keep goats, 80% poultry, 61.5% cattle and 29.9% pigs.
Livestock are mainly kept for manure. Sixty percents of respondents mentioned to involve in
off-farm activities such as entrepreneurship, casual labour and formal employment. About
92.7% of the respondents possessed at least one type of communication facility, with amajority (56.6%) possessing radios and mobile phones.
TEK systems applied to rainfall forecasting
Findings revealed that out of 611 respondents contacted for a direct questionnaire, 270(44.2%) used environmental indicators (EIs) to forecast rainfall (44% of all males and 45.7% of
all females). These findings suggest that females are slightly more conversant with EIs in
forecasting rainfall than males because of the farming and family responsibilities females are
obliged to. It is reported that African females in Africa are more involved in cultivation than114

males8, 17, and are often denied access to climate information due to limited participation ingatherings18 and engagement in family chores15.
It was, further, found that the time at which an individual is exposed to the environment
contributed to the acquisition of TEK relevant to rainfall forecasting. This is because no
respondents in the age group 15-24 mentioned use of EIs to forecast rainfall. These findings
concur with the study conducted in Burkina Faso, in which seniority was found to be a mark ofauthority because elders were believed to have accumulated a great deal of experience14. The
study, further, revealed that respondents with post secondary education (76.9%) use more EIs
to forecast rainfall than those without this level of education. This contradicts with the reportthat people who are trained in western education rely more heavily on modern methods offorecasting rainfall because they perceive TEK systems as outdated7.
The findings also revealed that the possession of communication facilities reduces theuse of EIs to forecast rainfall. Seventy five percent of respondents who owned radio and/or
television did not mention the use of EIs to forecast rainfall. Earlier studies reported that
wealthy households, who owned and use communication facilities, benefit more from weather
forecasting using modern methods than the poor7, 9.
The study found that the EIs used by the respondents to forecast rainfall can be
categorised into meteorological, biological, astronomical and others (Appendix 1). The EIs
identified in this study are similar to those mentioned elsewhere by other researchers of TEK
related to rainfall forecasts3, 7, 10, 11, 12, 14. The appendix 1 shows that clouds were the most usedsigns in forecasting rainfall simply because rainfall is antecedent by clouds.
Provision of accurate seasonal climate prediction and early warning of climate events
requires the availability of reliable predictors, with sufficient lead time19. In this study, it wasalso established that the time respondents could predict rainfall in advance is of great concern.
The findings presented in Appendix 2 indicate that 75.5% of respondents were unable toforecast rainfall beyond a week. These respondents benefit less from knowing the expectedcharacteristics of rainfall because a week is too short for anticipatory adaptation and, hence,
reactive adaptation. Reactive adaptation lead into unsustainable use of resources, and it
always leaves the affected community poorer and less able to recover from the climate
shock20. About 10.1% of the respondents are expected to be better prepared for the outcome
by being able to forecast rainfall beyond a month in advance. Being able to predict rainfall amonth ahead or more may facilitate anticipatory adaptation in which today’s resources may be
used to prevent possible crises in the future or to reap the benefits of expected rainfall
characteristics20.
However, the study also found that most of the EIs people used to forecast rainfall
beyond a month using a traditional calendar or vegetation characteristics were generally
claimed to be unreliable (Appendix 3). The appendix shows that insects were rated by ourinterviewees to be very reliable and reliable despite the fact that behaviour of biologicalindicators is expected to be altered by climate change. Changes in climatic variables can leadto changes in physiology of living organisms as a way of adapting to new ecologicalconditions4. Similarly, characteristics of winds are usually influenced by global warming and,
therefore, winds are expected to alter direction, frequency and force rendering them lessreliable in forecasting rainfall.
115

It was, however, difficult to judge on the reliability of some EIs in forecasting rainfall
because the same indicator was rated differently by different respondents, as very reliable,
reliable, not reliable or no longer reliable (Appendix 3). The EIs rated not reliable and no longer
reliable were once reliable, but increasingly inconsistent especially in recent years. Similarly,
the study conducted in Burkina Faso revealed a reduced use of local forecasting knowledge
because there was an inconsistency between the usual indicators and their effectiveness inthe current trends of the increased climate change11. Similar findings were also reported in the
Sahel Africa6. However, studies conducted in the Nigerian Savannah concluded that farmers
are no longer able to predict rain or to know precisely when to plant their crops21. The 89.6%
of respondents noted that to use forecasted information for farming activities is full ofambiguities. This is because in-depth interviews with the District Agricultural and Livestock
Development Officer and Agricultural Extension Officers suggested that most of the farmers inthe study area plant crops based on traditions, and that they do not predetermine planting ofcrop types based on forecasted rainfall characteristics in any case. Therefore, the forecasted
rainfall characteristics may not be necessarily a triggering factor for a certain agricultural
practice such as when and which type of crop to plant.
Findings revealed that respondents have lost confidence over EIs because allmentioned to complement their own knowledge of forecasting rainfall with other sources:
68.3% depend on Tanzania Meteorological Agency (TMA) through radio, television and formal
meetings; 7.7% on friends; and 23.9% depend on both TMA and friends. During FGD held in
the highland AEZ, it was noted that peasants were interested in forecasted information from
Kenya Meteorological Department because the forecasted information were found to be moreaccurate and reliable than those of the TMA. As for this study, alternative sources of rainfall
prediction were considered important to farmers who had lost confidence in their ability to
predict rainfall11.
Trends of future rainfall
Because of the inability of respondents to use EIs to forecast rainfall beyond a year, questions
to determine their perceptions on the trends of future rainfall were formulated. The results
show that perceptions of future rainfall trends differ according to the characteristics of our
respondents. Respondents predicted a decrease of rainfall over time as it had experienced in
the past, and they anticipated this trend to persist mainly due to the continued tree felling,
drying of trees and difficulty in raising tree seedlings (see Appendix 4). These findings imply
that local people are aware that deforestation induces climate variability and changes including
decreasing rainfall similar to modern scientists23.
Moreover, results presented in Appendix 4 show that 30% of respondents anticipatedan increase in rainfall due to the prohibition of tree felling, and tree planting campaigns. Treefelling in the study area was prohibited unless by permit, and, as well, there was a campaign
for planting trees in which every household and institution was supposed to plant 5 and 20
trees respectively. The respondents with this opinion expected that prohibiting tree felling, andthe tree planting campaign to increase vegetation cover would attract more rainfall. Thisobservation is in line with the IPCC report which emphasises tree planting to offer sinks forcarbon dioxide23.
Appendix 4 also indicates that some respondents perceived trends of rainfall to be
unpredictable and didn’t know what to expect. These respondents were noted to have had
116experienced unpredictable rainfall in the past, and expected the same level of unpredictability
in the future. Both respondents who considered future rainfall to be unpredictable and those
who did not know what the trend might be said it was impossible to predict rainfall because it iscontrolled by God. Thus, predicting future rainfall characteristics is full of uncertainties
because it is based on individuals’ perceptions13.
Based on A2 scenario (Appendix 5), prediction of rainfall changes using three GCMs
indicate that there will be variation in both annual and seasonal rainfall. The figure fails to give
clear trend of rainfall with all GCMs. When multi-means (averages) are used, the figure
indicates that while rainfall for annual and October-December are expected to increase theopposite is expected for June-September. The figure, further, shows that the JanuaryFebruary and March-May are expected to decrease and then increase. It is difficult to argue on
the accuracy between the perception of respondents and GCMs because of the divergent
results despite the fact that increase in rainfall in areas with bimodal rainfall have been
projected24. The March-May and October-December rains are very important as they mark two
seasons for growing annual crops in the study area. Abnormal characteristics of these rains
may have catastrophic impacts to the livelihoods of people in the study area.
Conclusions and recommendations
Based on the findings and discussions, it can be concluded that the efficacy of TEK systems inforecast rainfall in the study area is dwindled because: less than half of the respondents could
forecast rainfall using EIs; very few respondents could use EIs to forecast rainfall beyond amonth in advance; and the same EIs was rated differently by different respondents based onreliability scale. It is also difficult to integrate TEK and MEK systems in forecasting rainfall
because of the divergent results from respondents and GCMs on the future characteristics ofrainfall. Still the information obtained seemed meaningful in planning for agricultural and otherdaily activities. For effective rainfall forecasting, the study recommended that respondents who
could forecast rainfall using EIs should share not only the knowledge but also the forecasted
information with others; monitoring of the EIs; and careful multidisciplinary approach.
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Appendix 1 Environmental indicators used to forecast rainfall (N=270)
Environmental
indicator
1. Meteorological
Cloud

Respondents
(%)
41.4
36.4

Thunder
Wind

3.9
1.1

2. Biological
Human body

23.7
13.6

Vegetation

5.9

Bird

2.5

Insect1.7

3. Astronomical
Moon

13.5
8.6

Sun

4.9

4. Others
Mt. Kilimanjaro
Calendar

21.4
16
3.9

Indicators of rainfall onset-Presence of dark clouds in the north-eastern side (ndumuha).
-Presence of cap-like clouds (ngoviri/uviri) at the top of Mount
Kilimanjaro.
-Thunder heard from the north-eastern side (Taita Hill).
-North-westerly winds.
-Strong wind blows dust and other materials into the sky in a spiral-like(kititimakwe).
-Feels overtired with excessive sweating and slumbering (ronge).
-Dreaming to swim in high water.
-Flowering in mango trees (Mangifera indica), coffee (Coffea arabica) andjacaranda (Jacaranda mimosifolia).
-Formation of new leaves in fig tree (Ficus benjamina) and banana
plantain (Musa acuminata).
-Cries of certain birds (modudu).
-Migration of pigeons (mbuku) in a big flock eastwards and trumpeter
hornbill (Bycanistes bucinator) westwards.
-Termites (Reticulitermes virginicus) build a mound with wet soil in theevening.
-Tree bugs (mbalamata) discharges much water than normal.
-Moon set between Kibo and Mawenzi peaks of Mount Kilimanjaro.
-Moon appears bigger than normal when rise at 6 pm (mweri mlemeni).
-Sun appears more glittered than normal while most of sky covered withdark clouds.
-Cap-like clouds (ngoviri/uviri) at the top of Mount Kilimanjaro.
-Traditional agricultural year begins in September after hearing thunder.

Source: Field Data (2012)

Appendix 2 Time in advance for rainfall forecasting
Environmental
Responses on onset of rainfall (%)
indicator
Hour-7days
7–14 days
14-21 days 21-28 days
Clouds
80.7
5.2
5.5
1.2
Mt. Kilimanjaro
70.3
13.3
4.4
1.9
Vegetation
56.5
6.5
4.8
6.5
Human body
85.2
7.4
2.5
2.5
Insects
66.7
19
9.5
4.8
Birds
76.5
8.8
2.9
8.8
Moon
72.9
9.4
1.2
1.2
Sun
58.2
9.1
10.9
0
Calendar
54.8
6.5
1.6
1.6
Wind
78.3
4.3
13
0
Thunder
72.1
14
4.7
2.3
Rainbow100000
Total (N=270)
75.5
7.8
4.9
1.7
Source: Field Data (2012)

119

28+ days
7.3
9.2
25.9
2.5
0
2.9
15.3
21.8
34.5
4.3
7
0
10.1

Appendix 3 Reliability of environmental indicators in forecasting rainfall
Environmental
Responses on reliability (%)
Indicator
Very reliable
Reliable
Not reliable
No longer
Clouds
47.5
43.5
5.3
3.7
Mt. Kilimanjaro
47.4
37.2
9
6.4
Vegetation
58.1
29
8.1
4.8
Human body
45.9
45.1
8.2
0.8
Insects
95.2
4.8
0
0
Birds
70.6
26.5
2.9
0
Moon
51.8
29.4
16.5
2.4
Sun
61.5
21.2
17.3
0
Calendar
63.5
12.7
15.9
7.9
Wind
69.6
30.4
0
0
Thunder
44.2
30.2
20.9
4.7
Rainbow
27.3
36.4
36.4
0
Total (N=270)
47.9
41.3
7
3.8
Source: Field Data (2012)

Appendix 4 Perception of future trends of rainfall
Characteristics ofPerception of rainfall trends (%)
respondents
Increase
Decrease
Unpredictable Don’t know
AEZ
Highland (n=10)
20
50
30
0
Intermediate (n=10)
60
10
20
10
Lowland (n=10)
10
90
0
0
Sex
Male (n=22)
27.3
54.5
18.2
0
Female (n=8)
37.5
37.5
12.5
12.5
Age group
25-34 (n=1)
35-44 (n=5)
45-54 (n=8)
55-64 (n=11)65+ (n=5)
Education
Primary (n=26)
Post Sec. (n=1)
No Formal (n=3)
Total (N=30)

0
60
25
36.4
30

100
20
37.5
45.5
50

0
20
25
18.2
16.7

0
0
12.5
0
3.3

30.8
0
33.3
30

46.2
100
66.6
50

19.2
0
0
16.7

3.8
0
0
3.3

Source: Field Data (2012)120

Appendix 5 Projected rainfall change at Huruma Convent gauging station
GCM Predictions on Annual Rainfall Delta Change at Huruma Convent

Rainfall change (%)

40.0
30.0
20.0
ccsm3.3

10.0

cm4

0.0

cm2.0
-10.0

Average-20.0
Historical (1961-1990)

2011-2030

2046-2065

200.0 GCM Predictions on Jan-Feb Rainfall
150.0 Delta Change at Huruma Convent
100.0
50.0
ccsm3.3
0.0
-50.0
cm4

Rainfall change (%)

GCM Predictions on Mar-May Rainfall
Delta Change at Huruma Convent

cm2.0
Average

80.0
60.0
40.0
20.0
0.0

ccsm3.3
cm2.0
Average
cm4
Years

Years
GCM Predictions on June-Sept Rainfall
Delta Change at Huruma Convent

GCM Predictions on Oct-Dec Rainfall
Delta Change at Huruma Convent

50.0

30.0
20.0
10.0
0.0
-10.0

0.0

Rainfall change (%)

Rainfall change (%)

Rainfall change (%)

Years

cm4

-50.0

ccsm3.3
cm2.0
AverageYears121

ccsm3.3
cm4
cm2.0
AverageYears