AF91

References Goufa Z, Minakawa N. Githeko AK. Guiyun Y. (2004) Association between Climate Variability and Malaria epidemics. Proceeding of the National Academy of Sciences 24;101(8):2375-80

89 AF91

Figure 2

Number of Cholera cases in Uganda 1997-2002

50000 El Nino

s 40000 30000

20000 10000 Number of case 0 1996 1997 1998 1999 2000 2001 2002 2003 Time in years

Figure 3

Numbers of cholera cases from 1997 to 2002 Western Kenya

10000 El Nino 8000 6000 4000 2000

Number of cases 0 1996 1997 1998 1999 2000 2001 2002 2003 Year 88 AF91

Similar calculation will be carried out on the climate data and correlations with malaria data determined. Recently the methodology for the analysis was published by Goufa et al 2004. Contacts have been made with Dr. Goufa and he has accepted to assist our team with the analysis. Figure 1

Standardized anomalies in malaria cases in highlands of Kenya, Tanzania and Uganda

20 e

15

10

5

0 Jan.96 Jan.97 Jan.98 Jan.99 Jan.00 Jan01. Jan.02 Standardized anomali -5 Time

Litein Muleba Kigezi IPD

Cholera Cholera data was collected from the Ministries of health for Entebbe (Uganda), Kisumu (Kenya) and Mwanza (Tanzania), districts adjacent to the Lake Victoria. These districts have a history of cholera epidemics. For Kisumu data was available from 1997, Uganda 1996 and Tanzania 1980. Trends in cholera cases in the three countries are similar and are associated with the El Niño phenomena (see figure 2 and 3). It is interesting to note that no cholera cases have been reported in Kisumu district since 2002. However, it is believed that the bacteria is still in the lake waters but in low densities incapable in causing human infections. Certain climate conditions can reverse this situation causing an increase in bacteria density and consequently cholera infections.

Implications of climate variability on malaria and cholera The current data strongly suggests that increases in temperature and rainfall as seen during the El Niño event will increase malaria cases in the highlands and cholera cases around the lake Victoria. This hypothesis will be examined using mathematical models developed in this project using down scaled future climate simulation data as inputs.

87 AF91

OUTPUT 7: TRENDS IN MALARIA AND CHOLERA Malaria Total monthly in-patient data from three hospitals located in the highlands of Kenya (Litein), Tanzania (Muleba) and Uganda (Kigezi) were received in January 2004. Cholera data from Kisumu, (Kenya) Mwanza (Tanzania) and Entebbe (Uganda) was also received.

The malaria data covered the period starting from 1996 and ending 2003. Data from previous years in all the three countries has not been summarized or was incomplete. Because the catchment population data for three hospitals was not available it is not possible to calculate the proportion of the population attending the hospitals. However, calculating standardized anomalies in in-patient cases can solve the problem. Standardized anomalies, which have no units, were thus calculated using long-term monthly means for the available data. These means were then subtracted from the means of individual years and then divided by the standard deviation. The data is shown in figure 1 below.

The main objective in the data analysis was to determine if there is a relationship between climate variability and cases of malaria. However the climate data was not available at the time of malaria data analysis. A second objective was to identify malaria outbreaks and epidemics as opposed to seasonal fluctuations. This is an area that has been controversial as in most cases health authorities do not use statistical methods for the classification of cases into outbreaks and epidemics. A search of literature indicated that there are three statistical definitions of what a malaria epidemic is. However these definitions do not take into consideration the capacity of health facilities to deal with malaria cases. Thus while an outbreak my not be classified as an epidemic it can easily be a medical disaster in resource poor health facilities. Moreover drug resistance can exacerbate an outbreak into a medical disaster similar to a full blown epidemic.

A common statistical definition of a malaria epidemic is cases that are more than two standard deviations for the month in question. These definitions may only be useful for retrospective analysis of epidemic data.

Results of the analysis of malaria cases from the three sites in Kenya, Uganda and Tanzania indicated that the trend in cases anomalies were very similar despite the great distances between the sites. For example a correlation of 0.71 between standardized anomalies in monthly malaria cases from two highland sites in Kenya (Litein) and Tanzania (Muleba) was found. Similar observations were made between data collected in highlands of Uganda (Kigezi) and Tanzania (Muleba).

This similarity suggests that a common factor is driving the trends in malaria cases anomalies. Climate is the most likely factor. Evidence of climate as the major factor was seen during the 1997/98 El Niño, which had very similar effects on malaria in the three sites.

86 AF91

Map showing average distances from water points in Kisumu

34°35' 34°40'

$ $$$$ $ $$$ $$ $ $ $ $ N $ 0°5' $ $ 0°5' $ $ $ $ $ $$ $$ $ $$#$#$ LEGEND $ $ $ $# $ $$$# #$ $ $ $ $$$ # $ #$ Water Points $ $ $$$ $ $$# $ #$$#$$$ $ $ #$ $ Surveyed Households $ $$ $ $$$ $ $ $$ $ $#$ # $ $ Lake $ $ $$ Distance to Water Points (m) $ $ 70 $$ $ $ $$ 70 - 140 $ $ $ $$ $ $ $ #$ $ $ 140 - 210 # $ $ $ 210 - 270 LAKE VICTORIA 270- 340 LAKE VICTORIA $ 340 - 410 410 - 480

0°10' 0°10' 480 - 550 $ 550 - 620 $$ $

$ 2024Kilometers

34°35' 34°40'

85 AF91

84 AF91

83 AF91

82 AF91

81 AF91

80 AF91

OUTPUT 6: THE GIS MAPS OF THE STUDY SITES

79 AF91

Ropelewski, C. F., Halpert, M.S. (1987). "Global and regional scale precipitation patterns associated with El Nino/ Southern Oscillation." Monthly Weather Review. 115: 1606 – 1626

Scheren, P. A. G. M., Zanting, H.A., Lemmens, A.M.C. (2000). "Estimation of water pollution sources in the Lakes Victoria, East Africa: Application and elaboration of the rapid assessment methodolgy." Journal of Environmental Management 58: 235 - 248.

Shepherd, K., Walsh, M., Mugo, F., Ong, C., Hansen, T.S., Swallow, B., Awiti, A., Hai, M., Nyantika, D., Ombao, D., Grunder, M., Mbote, F., Mungai, D. (2000). Improved land management in the Lake Victoria basin: Linking land and lake, research and extension, catchment and lake basin. Nairobi, Kenya, International Centre for Research in Agroforestry and Kenya Ministry of Agriculture and Rural Development, Soil and water Conservation Branch, National Soil and Water Conservation progamme.

Spigel, R. H., Colter, G.W. (1996). Comparison of hydrology and physical limnology of the East African Great Lakes: Tanganyika, Malawi, Victoria, Kivu and Turkana ( with reference to some North American Great lakes). Toronto, Gordon and Breach.

Talling, J. F. (1966). "The annual cycle of stratification and phytoplankton growth in lake victoria." Internationale Revue der gesamten Hydrobiologie und Hydrographie 51(4): 545 - 621.

Thompson, J. C., S. (2002). "Drawers of water asssessing water in Africa." Bulletin of the World Health organisation 80: 61 - 62.

Tiercelin, J. J., Mondeguer, A. (1991). The geology of the Tanganyika trough. London, Oxford University Press.

World Bank, (1996). Kenya, Tanzania and Uganda: Lake Victoria Environmental Management Project. GEF Documentation Report No.15541-ARF.

78 AF91

References Awiti, A., Walsh, M. (2002). Improved land and lake, research and extension catchment and lake basin. Final Technical Report. Nairobi, Kenya, International Centre for research in Agroforestry. Ministry of Agriculture and Rural Development,: 30 - 33.

Bootsma, H. A., Hecky, R.E. (1993). "Conservation of the AfricanGreat Lakes: a limnolgical perpective." Conservation Biology (Special Issue) 7(3): 644- 656.

Bugenyi, F. W. B., Magumba, K.M. (1996). The present physicochemical ecology of Lake Victoria, Uganda. Toronto, Gordon and Breach.

Bwathondi, P. O. J., Ogutu-Ohwayo, R., Ogari, J. (2001). Lake Victoria Fisheries Management Plan LVFRP/TECH/01/16, Technical document No. 16.

Capart, A. (1949). "Sondarge et carte barhymetrique du lac Tanganika. Resultats scientifiques de l'exploration hydrobiologique du lac Tanganyika (1946- 1947)." Institut Royal des Sciences Naturelles de Belgique 2: 1- 16.

Johnson, T. C., Ng'ang'a, P. (1990). "Reflections on a rift lake." Lacustrine Basin Exploration, Case studies and Modern Analogues, AAPG Memoir 50: 113 - 135.

Johnson, T. C., Odada, E.O., Kelts, K. (2000). "The Holocene history of Lake Victoria." Ambio 29(1): 2 - 14.

Karanja, D. M. S. (2002). Health and diseases: a case study of Lake Victoria basin. Nairobi, Pan-African START Secretariat.

Lehman, J. T. (1997). "How climate change is shaping Lake Victoria." The International Decade for the East Africa Lakes. (IDEAL) 1 - 2.

Lowe-McConnell, R. H. (1994). "The changing ecosytem of Lake Victoria, East Africa." Fresh Forum 4: 76 - 89.

Magunda, M. K., Majaliwa, M. (2000). "A review of the effects of population pressure on wetlands management practices in the Lake Victoria basin." African Journal of Tropical Hydrobiology and Fisheries 19: 78 - 91.

Nicholson, S. E. (1996). A review of climate dynamics and climate variability in Eastern Africa. Australia, Gordon and Breach Publishers.

Ochumba, P. B. O. (1996). Measurement of water currents, temperature, dissolved oxygen and winds on the Kenyan Lake Victoria. Toronto, Gordon and Breach Publishers.

Ogallo, L. J. (1989). "The spatial and temporal patterns of the East African seasonal rainfall derived from principal components analysis." International Journal of Climatology. 9: 145 - 167.

77 AF91

Percentage of November Flow Compared to Mean Annual Flow for 1961-1991

700

600

500

400

300 Relative Mean Flow (%) 200

100

0 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 Year

Cholera data, Kenya 1FG01-Yala 1FG02-Yala 1JG01-Sondu 1JD03-Yurith 1HA14-Awach 1GD03-Nyando

Percentage of December Flow Compared to Mean Annual Flow for 1961-1991

600

500

400

300

Relative Mean Flow (%) 200

100

0 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 Year

Cholera data, Kenya 1FG01-Yala 1FG02-Yala 1JG01-Sondu 1JD03-Yurith 1HA14-Awach 1GD03-Nyando

76 AF91

SOND

Percentage of September Flow Compared to Mean Annual Flow for 1961-1991

600

500

400

300

Relative Mean Flow (%) 200

100

0 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 Year

Cholera data, Kenya 1FG01-Yala 1FG02-Yala 1JG01-Sondu 1JD03-Yurith 1HA14-Awach 1GD03-Nyando

Percentage of October Flow Compared to Mean Annual Flow for 1961-1991

600

500

400

300

Relative Mean Flow (%) 200

100

0 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 Year

Cholera data, Kenya 1FG01-Yala 1FG02-Yala 1JG01-Sondu 1JD03-Yurith 1HA14-Awach 1GD03-Nyando

75 AF91

Percentage of May Flow Compared to Mean Annual Flow for 1961-1991

600

500

400

300

Relative Mean Flow (%) 200

100

0 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 Year

Cholera data, Kenya 1FG01-Yala 1FG02-Yala 1JG01-Sondu 1JD03-Yurith 1HA14-Awach 1GD03-Nyando

Percentage of June Flow Compared to Mean Annual Flow for 1961-1991

600

500

400

300

Relative Mean Flow (%) 200

100

0 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 Year

Cholera data, Kenya 1FG01-Yala 1FG02-Yala 1JG01-Sondu 1JD03-Yurith 1HA14-Awach 1GD03-Nyando

74 AF91

Percentage of April Flow Compared to Mean Annual Flow for 1961-1991

600

500

400

300

Relative Mean Flow (%) 200

100

0 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 Year

Cholera data, Kenya 1FG01-Yala 1FG02-Yala 1JG01-Sondu 1JD03-Yurith 1HA14-Awach 1GD03-Nyando

73 AF91

epidemics) are associated with the long rains season, or short rains season when there is above normal rainfall but not as intense as that experienced during El Niño. High positive anomalies in maximum temperature are required to drive both cholera and malaria epidemics.

MAMJ Plots

Percentage of March Flow Compared to Mean Annual Flow for 1961-1991

700

600

500

400

300 Relative Mean Flow (%) 200

100

0 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 Year

Cholera data, Kenya 1FG01-Yala 1FG02-Yala 1JG01-Sondu 1JD03-Yurith 1HA14-Awach 1GD03-Nyando

72 AF91

Flood Frequency From the analysis of flood frequency for Sondu River (see below), we note that the return period for maximum discharge for 1961-1981 is between 2 to 5 years. This ties in well with the inter-annual variations of precipitation (and El Niño) that has been established for the region.

1JG01_SONDU_RIVER Number of years : 45 Fitting procedure : GEV-PWM

u = 143.420 a = 96.898 k = -.4019 Return period Magn. S.E. 2. 181.68 24.19 5. 342.88 48.79 10. 497.97 102.78 25. 774.27 267.73 50. 1059.15 500.27 100. * 1433.91 874.69 200. * 1928.00 1457.19

Relationship of Hydrology and Cholera Disease Outbreaks Comparison of the time series plots of the percentage of monthly flow relative to mean annual flow, versus the WHO record for cholera outbreaks shows some interesting correlations. It should be noted that the WHO cholera record covers outbreaks in the entire region, not only in the Lake Victoria basin. However, going by the hypothesis that that El Niño related outbreaks are regional and will match the most important months in the year that have the climatological characteristics to precipitate a cholera outbreak, we should be able to detect this effect despite the underlying noise coming from other regions.

We present below the plots for the long rains season (MAMJ) and those for the short rains season (SOND). The extra month added to the long and short rains season, respectively, takes into account the one month lag in peak discharge as compared to peak rainfall during these wet seasons. These plots indicate that cholera epidemics (high disease prevalence in all parts of eastern Africa) appear to be closely associated with the El Niño, which is mainly associated with the short rains season in eastern Africa. Cholera peaks coincide with high discharge peaks during the months of October, November and December. During other months of the year the data are offset, indicating that there is no correlation between the two, and hence cholera outbreaks during the matching months in these cases can be attributed to non-climatic causes. During the El Niño years, the hydrological discharge during the short rains seasons exceeds that in the long rains season (discharge characteristics are reversed). This is consistent with the association of El Niño and the short rains season (cf. Nicholson, 1996). Cholera outbreaks (not

71 AF91

70 AF91

Cross-spectral Analysis Cross-spectral analysis was also carried out between rainfall and discharge. Representative rainfall stations were selected for each of the main basins. The results obtained for the cross spectral analyses are as shown below. Note that both datasets (i.e. rainfall and discharge) were simultaneous and had the same time period from 1961 – 1990.

ID Number River Rainfall station

1FG01 Yala Kakamega met. Station 1FG02 Yala Kakamega met. Station 1JG01 Sondu Kericho met. Station 1JD03 Yurith Kisumu met station 1GD03 Nyando Kericho met. Station 1HA14 Awach Kisumu met station

The cross spectrum results for 1FG01 discharge and Kakamega rainfall showed significant peak at f = 0.1667 that corresponds to a period of 6 months (see plots below). The other significant peak occurs at 12 months which is assumed to be repetition of the harmonic cycle. The cross spectrum results for 1FG02 discharge and Kakamega rainfall shows significant peak at f = 0.1667 that corresponds to a period of 6 months. The other peaks are repetition of this cycle. Cross density for Sondu (1JG01) discharge and Kericho rainfall shows significant peak at f = 0.1667 that corresponds to a period of 6 months. Cross density for Yurith 1JD03 discharge and Kisumu rainfall shows dominant peak at f = 0.1676 that corresponds to a period of 6 months. The results obtained show that the peaks occurred at 6 months cycle in Yala and Sondu River. These reflect the strong influence and coupling of seasonal changes in precipitation on discharge.

69 AF91

68 AF91 spectral density against frequency.

Monthly total flow data was used to run the single spectral analysis. The data was standardised before carrying out any analysis. The results for the single spectral analysis and the cross spectrum are discussed separately. In general the results for the single spectral analysis shows damped harmonic oscillations. The results reveal that we have two spectral peaks for most of the drainage basins during a year. The other spectral peaks observed are harmonic repetitions of the 6 and 12 cycles. This can be explained by the fact that we have two rainfall seasons (MAM and SOND) in a year and during this rain seasons we expect to have the highest discharge. This is depicted in the plots below.

Yala (1FG01) has significant peak at f=0.0833 that corresponds to a period of 12 months. The other peaks are repetitions of the harmonic cycles that occur at 24, 60 and 180 months. Yala (1FG02) has a significant peak at f=0.1667 that corresponds to a period of 6 months. The other peaks occur at 12, 24, 60 and 180 months that correspond to the harmonic repetition of the 6 months cycles. This 2 peaks in a year with a period of 6 months can be explained by the fact that we have two rainfall maxima in a year during MAM and SOND. Sondu (1JG01) has a significant peak at f = 0.1676 that corresponds to a period of 6 months. The other significant peaks are harmonic repetition of the cycles. Yurith (1JD03) shows a significant peak at f = 0.1704 that corresponds to a period of 6 months. The other peaks are repetition of this cycle.

67 AF91

Seasonal Differences in Flow – MAM-SON The plots for the MAM/SON seasonal differences (i.e MAM-SON) are as shown below. For the Yala River, mean flow in the 1970s, early 80s and in the 90s is higher in SON than in MAM. Sondu and Yurith Rivers reflect the same features (except for the 1990s for which there is no record to evaluate) but they are somewhat more muted, and is more dominated by MAM discharge (higher) than SON discharge (lower).

Seasonal Differences in Discharge

1FG01 diff. 1FG02 diff. 1JG01 diff. 1JD03 diff. 1HA14 diff. 1GD03 diff.

160.000

120.000

80.000

40.000

0.000

-40.000

-80.000 Seasonal Discharge Difference (m3/s)

-120.000

-160.000 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 Year

3.6 Spectral Analysis In order to examine the similarities in the characteristics of the rainfall and discharge records, monthly total rainfall (mm) and discharge (cumecs) from 1961 to 1990 were subjected to univariate and multivariate time series analyse which included single spectral and cross-spectral analyses.

The presence of trend in a time series makes it difficult to examine the behaviour of the cyclical, seasonal and irregular components. The time series is therefore made stationary before they are subjected to cyclical and cross-spectral analysis. A stationary time series has constant mean and variance. Spectrum analysis can either be single spectrum (Fourier) that has only one series of dataset or it can be cross spectrum that deals with identifying the cyclicity in two related datasets. Cross spectrum analysis is an extension of single spectrum to the simultaneous analysis of two series. The purpose of cross spectrum is to uncover the correlation between two series at different frequencies. Since the purpose of spectrum analysis is to decompose a complex time series with cyclical components into few underlying sinusoidal functions of particular wavelength, then we cannot ignore frequency (f) and period (T). The cycles appear as peaks in the plot of

66 AF91

Sondu (1JG01) SON Total Flow (m3/s)

12000 1JG01 10000 /s) 3 y = -14.821x + 4334.6 Linear (1JG01) 8000 6000 4000 2000 Discharge (m Discharge 0 SON:61 SON:63 SON:65 SON:67 SON:69 SON:71 SON:73 SON:75 SON:77 SON:79 SON:81 SON:83 SON:85 SON:87 SON:89 SON:91 SON Season

Yurith (1JD03) SON Total Flow (m3/s) 1JD03 8000 Linear (1JD03) 7000 6000 y = -27.7x + 3310.3 5000 4000 3000 2000 1000 Discharge (m3/s) Discharge 0 SON:61 SON:63 SON:65 SON:67 SON:69 SON:71 SON:73 SON:75 SON:77 SON:79 SON:81 SON:83 SON:85 SON:87 SON:89 SON:91 SON Season

65 AF91

YALA (1FG 01) SON Total Flow (m3/s)

YALA-1FG 01 7000 Linear (YALA-1FG 01) 6000 y = -19.695x + 3672.2 /s)

3 5000 4000

3000 2000

Discharge (m 1000 0 SON:61 SON:64 SON:67 SON:70 SON:73 SON:76 SON:79 SON:82 SON:85 SON:88 SON:91 SON:94 SON:97 SON Season

Yala (1FG02) SON Total Flow (m3/s)

8000 y = 16.601x + 3206.1 1FG02 7000 Linear (1FG02) 6000 /s) 3 5000 4000 3000

Discharge (m Discharge 2000 1000 0 SON:61 SON:64 SON:67 SON:70 SON:73 SON:76 SON:79 SON:82 SON:85 SON:88 SON:91 SON:94 SON:97 SON Season

64 AF91

Seasonal Flows - SON The seasonal flows show a high coherency in the six rivers for the short rains season as well (SON), and similar to the period for MAM, the mean trends are different. The trend for the SON total flow is positive for 1FG02 and negative for 1FG01, 1HA14, 1GD03 and 1JD03. The trend for the SON total flow for 1JG01 is almost constant. As for the MAM season, these differences are accounted for mainly by high discharge outliers in the datasets, but also are partly due to land and river use changes within the respective basins.

Seasonal Flows SON

1FG 01 - Yala 1FG02 - Yala 1JG01 - Sondu 1JG03 - Yurith 1HA14 - Awach 1GD03 - Nyando

250

200

150

100 Discharge (m3/s)

50

0 1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 Year

63 AF91

62 AF91

61 AF91

Seasonal Flows - MAM The seasonal flows show a high coherency in the six rivers (see figures below). The mean trends are, however, different. The MAM total flow shows an increasing trend in 1FG02 and 1JG01, and a decreasing trend in 1FG01, 1HA14 and 1GD03. The trend for MAM total flow over 1JD03 seems to be almost constant. These differences are accounted for mainly by high discharge outliers in the datasets, but also are partly due to land and river use changes within the respective basins.

Seasonal Flow - MAM

1FG 01 - Yala 1FG02 - Yala 1JG01 - Sondu 1JG03 - Yurith 1HA14 - Awach 1GD03 - Nyando

250

200

150

100 Discharge (m3/s)

50

0 1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 Year

60 AF91

3.5 Monthly and Seasonal Mean Flow The figures below show the monthly and seasonal mean flows for the six rivers in Lake Victoria basin (Kenya).

Monthly Mean Flow

500 Yala 1FG01 Yala 1FG02 450 Sondu 1JG01 Yurith 1JD03 400 Awach 1HA14 Nyando 1GD03 350

300 /s) 3

250

Discharge (m 200

150

100

50

0

1 1 2 3 4 5 6 7 8 9 0 1 2 2 3 4 5 6 7 8 9 0 1 2 3 3 4 5 6 7 8 9 0 1 2 3 4 4 5 6 7 8 9 6 7 9 6 :6 :6 6 6 :6 :6 6 : :6 :7 :7 7 7 7 7 7 7 :7 7 : 8 8 8 8 8 8 8 8 8 :8 8 : 9 9 9 9 9 9 9 9 9 :9 : t: t: : : : : t: t: : : : : : : : : : : : : : : : : : : : : : n c v g y e y r r b n c v g y e y r r b n c v t t g y e y r r b n c v t t g y c p l p a c p l p a c p l p a c p l a e o u u n a e a e o u u n a e a e o u u n a e a e o u u O e A O e A O e A O e J D N A J u M M F J D N A J u M M F J D N A J u M M F J D N A J S J S J S J S

Period (months)

Seasonal Mean Flow

300

250

200 YALA-1FG 01 /s) 3 YALA1FG 02 1JG01 150 1JD03 1HA14

Discharge (m Discharge 1GD03 100

50

0 JJA:63 JJA:68 JJA:73 JJA:78 JJA:83 JJA:88 JJA:93 JJA:98 DJF:66 DJF:71 DJF:76 DJF:81 DJF:86 DJF:91 DJF:96 DJF:61 SON:64 SON:69 SON:74 SON:79 SON:84 SON:89 SON:94 SON:99 MAM:67 MAM:72 MAM:77 MAM:82 MAM:87 MAM:92 MAM:97 MAM:62 Season

59 AF91

3.4 Mean Flows, 1961-1990 Mean and median flows for the period 1961-1990 show that highest discharge occurs in MAM long rains season, and declines gradually through JJA with a peak in August, to SON short rains season with a peak in November. These mean flows generally obscure important, high discharge events particularly in the short rains season (SON).

Mean Flows, 1961-1990

1FG01-Yala 1FG02-Yala 1JG01-Sondu 1JD03-Yurith 1HA14-Awach 1GD03-Nyando

200.00

180.00

160.00

140.00

120.00

100.00

80.00 Discharge (m3/s)

60.00

40.00

20.00

0.00 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Month

Median Flows, 1961-1990

1FG01-Yala 1FG02-Yala 1JG01-Sondu 1JD03-Yurith 1HA14-Awach 1GD03-Nyando

200.00

180.00

160.00

140.00

120.00

100.00

80.00 Discharge (m3/s) Discharge

60.00

40.00

20.00

0.00 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Month

58 AF91 RANKED MEAN ANNUAL FLOW, SONDU RIVER-1JG01, 1961-1991

Five wettest years (ranks 1 to 5) are: Six dryest years (ranks 26 to 31) are: 90.000 1990, 1978, 1982, 1977 and 1968 1976, 1969, 1980, 1965, 1986and respectively. 1984 respectively. 80.000

70.000

One standard deviation of the 30-year 60.000 mean (1961-90), mean = 46.893

50.000

40.000

30.000 Mean Annual Discharge (m3/s)

20.000

10.000

0.000 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 Rank

SONDU-1JG01

RANKED MEAN ANNUAL FLOW, YURITH RIVER - 1JD03, 1961-1991

60.000 Six wettest years (ranks 1 to 6) are: Six dryest years (ranks 26 to 31) are: 1990, 1978, 1982, 1968, 1962 and 1983, 1969, 1980, 1965, 1985 and 1963 respectively. 1986 respectively. 50.000

40.000 One standard deviation of the 30-year mean (1961-90), mean = 31.677

30.000

20.000 Mean Annual Discharge (m3/s)

10.000

0.000 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 Rank

Yurith-1JD03

57 AF91 RANKED MEAN ANNUAL FLOW, YALA RIVER-1FG 01, 1961 - 1999

60.000 Four wettest years (ranks 1 to 4) are: 1962, 1978, 1998, and 1993 respectively. Five dryest years (ranks 35 to 39) are: 50.000 1980, 1984, 1987, 1996 and 1965 respectively.

40.000

One standard deviation of the 30-year 30.000 mean (1961-90), mean = 29.682

20.000 Mean Annual Discharge (m3/s)

10.000

0.000 1 2 3 4 5 6 7 8 9 101112131415161718192021222324252627282930313233343536373839 Rank

YALA-1FG 01

RANKED MEAN ANNUAL FLOW, YALA RIVER-1FG 02, 1961-1999

70.000

Four wettest years (ranks 1 to 4) are: Five dryest years (ranks 35 to 39) are: 60.000 1978, 1977, 1988 and 1996 1980, 1984, 1987, 1996 and 1965 respectively. respectively.

50.000

40.000 One standard deviation of the 30-year mean (1961-90), mean = 32.543

30.000

Mean Annual Discharge (m3/s) 20.000

10.000

0.000 1 2 3 4 5 6 7 8 9 101112131415161718192021222324252627282930313233343536373839 Rank

YALA-1FG 02

56 AF91

55 AF91

54 AF91

3.3 Analogue Years The analogue year’s plots were carried out in order to distinguish the years with near normal discharge, above normal discharge and below normal discharge. This was accomplished by getting the cumulative long term annual means (LTM) and then comparing it with the cumulative annual means. Any values lying near the LTM were considered to have near normal flows (NN) whereas any values lying above had above normal flows (AN). For values lying below the LTM, they were assumed to have below normal flows (BN). The results are as shown below.

Yala (1FG01) shows that the year 1981 and 1990 had normal discharge while 1965 was the driest year. The years 1980, 1984, 1987, 1996 and 1997 were relatively dry years with low flow. The highest flow was in 1962. High flows were also recorded in 1963, 1964, 1968, 1978, 1979 and 1998 among others. Yala (1FG02) recorded 1994 as the year with near normal flow. The years with low flows were 1961, 1965, 1973, 1984, 1986 and 1987. The highest flow was in 1978, and generally, 1979, 1990 and 1998 had high flows. Sondu (1JG01) recorded normal flows in 1974, 1981 and 1989. The years with low flows included 1965, 1969, 1972, 1976, 1980, 1984 and 1986. The years with high flows included 1962, 1963, 1964, 1968, 1977, 1978, 1988 and 1990. Yurith (1JD03) recorded normal flow in 1966. The years with low flows included 1965, 1980, 1983, 1985, 1986 and 1991. The highest flow was recorded in 1990. Relatively high flows were also recorded in 1963, 1964, 1968, 1977 and 1978.

53 AF91

Annual Mean Flow, Sondu River 1JG01

90.00

80.00

70.00

60.00

50.00

40.00

Mean Annual Flow (m3/s) 30.00

20.00

10.00

0.00 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 Year

Sondu River 1JG01 Mean Flow Sondu River 1JG01 5yr Moving Mean

Annual Mean Flow, Yurith River 1JD03

60.00

50.00

40.00

30.00

Annual Mean Flow (m3/s) 20.00

10.00

0.00 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 Year

Yurith River 1JD03 Mean FLow Yurith River 1JD03 5yr Moving Mean

52 AF91

Moving Averages

Annual Mean Flow, Yala River 1FG01

60.00

50.00

40.00

30.00

Annual Mean Flow (m3/s) 20.00

10.00

0.00 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 Year

Yala River 1FG01 Mean Flow Yala River 1FG01 5yr Moving Mean

Annual Mean Flow, Yala River 1FG02

70.00

60.00

50.00

40.00

30.00 Annual Mean Flow (m3/s) 20.00

10.00

0.00 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 Year

Yala River 1FG02 Mean Flow Yala River 1FG02 5yr Moving Mean

51 AF91

50 AF91

49 AF91

48 AF91 flow in MAM 78 while 1JG03 was in MAM 90. The lowest seasonal flow was in the years 67, 80, and 87 in 1FG01/1FG02, 1JG01 and 1HA14 respectively. In 1JD03 MAM 91 had the lowest flow.

The descriptive statistics for the monthly means are as follows;

For the case of monthly mean flow, 1GD03 and 1HA14 recorded the highest flow in July 66 whereas 1JG01 and 1JD03 had the highest flow in April 1990. 1FG01 had the highest flow in Dec 61 while 1FG02 had the highest flow in May 78. The minimum flow in 1JG01 was Feb 1961 while in 1FG02 it was in Dec 61. The lowest flow in 1JG01, 1JD03 and 1GD03 was in Dec 67. 1HA14 recorded the lowest monthly flow in Feb 74.

3.2 Time Series Plots Time series plot (1/1/61 – 30/9/99) were used to check for trend, cyclicity and seasonality. The results obtained shows that Yala (1FG01) recorded the highest daily flow of 194.053m3/s on 11/28/61 and the lowest daily flow of 2.06 m3/s on 11/25/93.Yala (1FG02) recorded the highest daily flow of 226.84 m3/s on 5/13/78 and the lowest daily flow of 0.001 m3/s on 1/28/62. The highest daily flow for 1HA14, 1GD03, 1JG01 and1JD03 is 56.689 m3/s (5/15/78), 726.897 m3/s (10/22/65), 571.191 m3/s (4/9/90) and 388.948 m3/s (4/9/90) respectively. Similarly the lowest daily flow in 1GD03, 1JG01 and 1JD03 are 5.2 m3/s (9/3/83), 2.621 m3/s (2/3/67) and 1.407 m3/s (4/6/85) respectively.

Plots for the monthly flow totals, seasonal flow totals and annual flow totals were carried out for the six rivers of study. Trend lines for the annual totals for 1FG02 and 1JG01 shows an increasing trend while in 1FG01, 1HA14, 1GD03 and 1JD03 shows a decreasing trend in general. These trend lines are sensitive to high discharge outliers. The moving averages plots (see below) help to resolve the issue of different trends lines due to high discharge outliers. They show that discharge in the early sixties was above normal, was normal or near normal from mid-1960s to mid-1970s, above normal in the late 1970s, below normal in the 1980s, and normal to above normal in the 1990s.

47 AF91

After filling the missing data by the two methods (i.e. linear interpolation and MOVE.1 technique) the annual, seasonal and monthly means were calculated for the six rivers. Also the seasonal differences for MAM and SON were obtained.

3. RESULTS We present here the results for the rivers co-located or proximal to the study sites (Yala for Kisumu site, and Yurith and Sondu for the Kericho site). Results for the Nyando River are presented where necessary in order to provide the regional perspective of the hydrological characteristics of the area.

3.1 Mean Flows The descriptive statistics for the annual mean flow are shown below;

From the above results of annual mean flow, 1FG01, 1FG02 and 1JD03 recorded the lowest annual flow in 1965 while 1HA14 and 1GD03 recorded the lowest annual flow in 1976 and in 1984 respectively. In addition the years 1969, 1971, 1973, 1976, 1980 and 1986 had low flows. The highest annual flow was recorded in 1978 over 1GD03, 1JG01, 1FG01 and 1DG02. 1HA14 had the highest flow in 1966 while 1JD03 had similar occurrence in 1990.

The descriptive statistics for the seasonal mean flow are as follows;

The seasonal mean flows (DJF, MAM, JJA, SON) showed that 1FG01 had the highest flow in DJF 62,1HA14 and 1GD03 in JJA 66. 1FG02 and 1JG01 recorded the highest

46 AF91 have also analysed hydrological data for the Nyando basin which lies north of the Sondu- Miriu basin (Figure 2).

Hydrological Station Data and Lake Level Data Daily discharge data (m3/s) for six main rivers draining into L.Victoria Kenyan side was collected from the ministry of water. The six main rivers selected for study and the length of the data set are shown in Table 1 below.

Table 1. Hydrological station location data. ID NAME LONGITUDE LATITUDE ALTITUDE PERIOD 1FG01 Yala 34.540278 0.086111 1400 1/1/61-30/9/99 1FG02 Yala 34.265278 0.043056 1180 1/1/61-31/7/97 1GD03 Nyando 34.959722 -0.125 1170 1/12/67- 31/12/91 1HA14 Awach 34.804167 -0.047222 1180 21/6/61- 31/12/88 1JG01 Sondu 35.00833 -0.393056 1500 24/1/62- 29/9/91 1JD03 Yurith 35.074 -0.473 1/3/69-30/6/90

Also data for the lake level (1HB04) was considered running from 1/10/64 to 31/8/2000.

Data Treatment One of the major problems with the hydrology data sets were the data gaps, which, in some instances were quite large, ranging from several days to months and in a few cases even years. This problem was addressed by filling in data gaps of seven or less days by interpolation, and for periods exceeding seven days, by the MOVE.1 technique (Maintenance Of Variance-Extension, type 1). The MOVE.1 method gives room for record extension and was first used as a means of record extension by Hirsch (1982). It assumes that linearity exists between the concurrent flows at the short and the long-term stations. It produces stream flow estimates at the short term station with a statistical distribution similar to that expected if the stream flow had actually been measured (Helsel and Hirsch, 1992 P. 277) and will thus correctly estimate the probability of extreme high or low stream flow.

In this study, 1FG01 was filled with 1FG02 by using the MOVE.1 method and vice versa so that both data sets run from 1/1/61-30/12/99. For the case of 1HA14, it was filled with 1GD03 and vice versa so that both data sets runs from 1/11/62 – 31/12/91. Similarly 1JG01 was filled with 1JD03 and vice versa so that both data sets run from 1/1/61 – 29/9/91. The MOVE.1 correlation coefficients and the length of matching data sets are as shown below;

Pair wise Rivers Length of matching data set Correlation coefficient (r) 1HA14 and 1GD03 5 years 0.70453 1FG01 and 1FG02 10 years 0.8679 IJG01 and 1JD03 3 years 0.9680

45 AF91 health issues related to food and nutrition, and to diseases associated with lack of safe sources of water (Karanja, 2002). Some of these health problems arise out of the fact that the lake is also a repository for human, agricultural, mining and industrial waste. The water for domestic consumption for these communities is mainly taken without treatment (Bwathondi et al., 2001). Following Bradley (1972), a reanalysis of domestic water use in East Africa indicates that although more homes are supplied with piped water, the supply is not regular and is unevenly distributed; as a result water is sought from alternative sources many of which are not treated (Thompson and Cairncross 2002). It is estimated that about 20% of the Kenyan rural population has access to safe water compared to 8% for those in the Lake Victoria Basin (LVB) – Kenya sector (LBDA 1997). The poor treatment (or lack of it) and disposal of domestic wastewater and sewage (and raw sewage from pit-latrines littered around the urban centres and most rural areas close to rivers, streams, and wetland areas) is a public health as well as an environmental problem. The disposal of untreated or partially treated wastewater results in faecal contamination of surface and ground water and prevalence of pathogenic organisms.

Environmental Changes Climate variation has certainly contributed to the modern condition of Lake Victoria (Lehman, 1997), and the lake has apparently been influenced by the “global warming” trend evident in the high-elevation tropics. The driving mechanism seems to be anomalously high sea-surface temperatures in the tropical ocean persisting for at least 30 years, about the time frame during which Lake Victoria has experienced its most dramatic changes. The lake is now one-half a degree (°C) warmer than in 1960s (Bugenyi and Magumba, 1996), in harmony with changes in surface temperature at tropical elevations above 1000 m world-wide. Changes in radiative heat transfer functions have led to elevated water temperatures. Slackened winds have caused less intense mixing, and the generally humid conditions and elevated lake levels have possibly encouraged chemical weathering of the apatite-rich soils (Lehman, 1997).

2. THE STUDY SITES The study sites are as follows: Kenya – Kericho (malaria), Kisumu (cholera); Uganda – Kabale (malaria), Gaba (cholera); Tanzania – Bugarama (malaria), Chato (cholera).

Hydrological and hydrodynamic data for Lake Victoria basin are scarce, fragmentary and often not easily accessible. All the rivers in the Uganda and Tanzania sites are unguaged, and there are no other gauged rivers in close proximity to the sites that can be used as proxies for hydrological characteristics of those sites. The only hydrological data available for the Gaba (Uganda) site is the Nakivubo channel, a man-made channel for transporting wastewater and stormwater from Kampala City to Lake Victoria.

In Kenya, we have been able to obtain discharge data for rivers co-located with the Kericho site (Rivers Yurith and Sondu-Miriu in the Sondu-Miriu basin, Figure 2). For the cholera site in Kisumu, Kenya, the closest gauged river is the Yala River which is about 20km distance from the site. From climatological analysis, the Yala River can be considered to be a viable proxy for the hydrological characteristics of the Kisumu site (Lake-edge area, Figure 2), which has many relatively smaller, but ungauged, rivers. We

44 AF91

Figure 2: The river drainage basins of Lake Victoria (from Shepherd et al., 2000).

Some of the inflowing rivers from the catchment have been modified by activities involving irrigation (Rivers Nyando, Yala, Kagera), valley dams constructions (Rivers Sondu-Miriu, Yala) and for others, the flood plains and wetlands have been degraded (most of the affluent rivers). This leads to reduction of inflow (Scheren, et. al., 2000; Lowe-McConnell, 1994; Awiti and Walsh, 2002).

Water-related Health Issues Lake Victoria is a source of affordable protein food in the form of fish, and a source of water for the communities that live around it. Surprisingly, the health status of populations living around the lake is far below what it should be, with the most common

43 AF91

Figure 1. General features of the Lake Victoria Basin

Hydrological Characteristics of Lake Victoria Basin The water balance in Lake Victoria is dominated by rainfall on the lake and evaporation, and River Nile outflow, with river inflow making minor contributions (Spigel and Coulter, 1996). More than 80% of the water is derived directly from rain onto the lake surface, and evaporation from the lake itself accounts for a significant amount of its annual water loss (Johnson et al., 2000). The Kagera River contributes about 7% of the total inflow. Lake Victoria has a flushing time of 140 years and a residence time of 23 years (Bootsma and Hecky, 1993). Surface water temperatures are between 24°C and 28°C (Ochumba, 1996), and evaporative cooling during the dry season is important in the heat balance and mixing regime (Talling, 1966).

The waters of Lake Victoria and its shoreline are shared between three countries – Kenya (6%), Uganda (43%) and Tanzania (51%). Additionally, the catchment of the principal affluent river, the Kagera, runs through the countries of Rwanda and Burundi. There are eleven main rivers draining into Lake Victoria: Nzoia, Yala, Nyando, Sondu-Miriu, Gucha, Mara, Gurumeti, Duma, Simiyu, Magoga, Isonga and Kagera (Figure 2) (Shepherd et al., 2000). Of these, only two are shared by more than one country; the Kagera is shared by Tanzania, Rwanda, Burundi and Uganda, while the Mara is shared by Kenya and Tanzania. The only surface outlet is the Nile River, which has the Owen Falls hydroelectric power station at its source.

42 AF91 timescale of variability of 5 to 6 years. This is also a dominant timescale for the “El- Niño-Southern Oscillation (ENSO)” phenomenon and for “Sea-Surface Temperatures (SSTs)” fluctuations in the equatorial Indian and Atlantic Oceans. Rainfall variability is closely linked to both ENSO and SSTs in the Indian and Atlantic Oceans, and it tends to be enhanced in East Africa during ENSO years (Ropelewski and Halpert, 1987; Ogallo, 1989).

The Lake Victoria Basin: Physical Features Lake Victoria (lying 0o 21’N – 3o 0’S) is, by area, the second largest lake in the world and the largest in Africa, being almost twice the size of Lakes Tanganyika (32,900 km2) and Malawi (28,760 km2; Government of Malawi, 1998) (Figure 1). It is perched high (1134m above sea level) on the African craton between the western and eastern rift valleys (Johnson et al., 2000). This equatorial lake has a surface area of 68,800 km2 and an adjoining catchment area of 184,000 km2 (Figure 3.1). Lake Victoria is, however, a relatively very shallow Lake, with maximum depth of 80-90 m compared to Tanganyika and Malawi whose maximum depths are 1470 m (Capart, 1949; Tiercelin and Mondeguer, 1991) and 700 m (Johnson and Ng’ang’a, 1990), respectively. Kenya, Uganda and Tanzania border the Lake and share 6%, 43% and 51%, respectively, of the Lake surface.

The Lake Victoria Basin: Socio-economic Characteristics The Lake catchment is mainly (80%) an agricultural catchment (Majaliwa et al. 2000). Subsistence agriculture, pastoralism and agro-pastoralism currently supports about 21 million people in the basin, with average incomes in the range of US$90-270 per annum (World Bank, 1996). The lake basin as a whole (lake and catchment) provides for the livelihood of about one third of the combined population of the three East Africa Community Partner States, and about the same proportion of the combined gross domestic product.

41 AF91

Rainfall A1 A2 CCRNIES min max min max 1991 2000 3.35 330.63 3.35 330.63 2001 2010 1.52 379.83 0.93 319.02 2011 2020 0.74 363.09 1.89 353.16 2021 2030 0.99 277.20 3.16 303.58 2031 2040 4.06 294.96 0.53 307.47 2041 2050 1.83 290.97 2.48 295.74 2051 2060 5.10 286.20 3.00 312.15 2061 2070 2.17 342.00 2.10 370.38 2071 2080 1.46 314.70 0.99 287.13 2081 2090 1.89 287.28 1.08 324.78 2091 2100 0.90 283.35 1.31 321.42

Rainfall A1 A2 CSIRO min max min max 1991 2000 0.00 181.80 0.00 195.57 2001 2010 0.00 247.50 0.00 191.58 2011 2020 0.00 206.21 0.00 214.52 2021 2030 0.00 212.69 0.00 183.68 2031 2040 0.00 270.54 0.00 200.29 2041 2050 0.00 250.91 0.00 205.16 2051 2060 0.00 214.52 0.00 205.41 2061 2070 0.00 198.18 0.00 209.65 2071 2080 0.00 219.06 0.00 235.59 2081 2090 0.00 261.36 0.00 253.61 2091 2100 0.00 231.60 0.00 249.33

The on going work is to generate simulations of time series for the variables from the ranges that will be found to be acceptable for the region. These will be used in place of the GCM climate projections.

OUTPUT 5: HYDROLOGICAL ANALYSIS - RESULTS

1. Introduction Climatology Meteorologically, equatorial eastern Africa is one of the most complex sectors of the African continent (Nicholson, 1996). The large-scale topical controls, which include several major convergence zones, are superimposed upon regional factors associated with lakes, topography and the maritime influence (Nicholson, 1996). As a result, the climatic patterns are markedly complex and change rapidly over short distances (Nicholson, 1996). The inter-annual variability of rainfall is remarkably coherent throughout most of eastern Africa despite quite diverse climatic mean conditions. The largest portion of this variability is accounted for by the “short rains” season of October-December. Rainfall variability in the region shows strong teleconnections to the rest of Africa and to the global tropics. Rainfall in eastern Africa is strongly quasi-periodic, with a dominant

40 AF91

Seasonal Rainfall (mm) for Kabale

Kabale- (JF) Kabale - (MAM) 400 600 y = -0.3498x + 840.96 y = -0.5769x + 1485.4 500 300 400 200 300 200 100 100 0 0 1962 1967 1972 1977 1982 1987 1992 1997 2002 1962 1967 1972 1977 1982 1987 1992 1997 2002

Kabale - (JJA) Kabale - (SOND) 300 700 y = 0.4258x - 727.59 y = 0.999x - 1555.4 600 500 200 400 300 100 200 100 0 0 1962 1967 1972 1977 1982 1987 1992 1997 2002 1962 1967 1972 1977 1982 1987 1992 1997 2002

4. Temperature time series analysis On average, analysis of the temperature time series for the stations (although not shown here) do not show any significant increasing or decreasing trend. This will be done in more details to bring out any differences on a month by month or season by season basis.

5. GCM Climate Projections Rainfall and temperature projections have been obtained from the IPCC Data Centre. The models are CSIRO and CCSRNIES with a horizontal resolution of 64*56 and 64*32 grid points respectively. The scenarios extracted were A1 and A2 for both the models. These projections have been used to provide a range for the maximum and minimum values for each of the variables i.e. rainfall and temperature for use with Monte Carlo simulations. These values have been compared to others obtained in the region using other statistical methods. The rainfall values obtained from these models are given below. The temperature values give a range of 28.5-30.2 degC for minimum temperatures while that for the maximum temperatures is 29.4-31.2 degC.

39 AF91

Seasonal Rainfall (mm) for Kampala

Kampala - (JF) Kampala - (MAM) 500 800 y = -0.6407x + 1400.4 y = -2.8728x + 6107.6 400 600 300 400 200

100 200

0 0 1962 1967 1972 1977 1982 1987 1992 1997 2002 1962 1967 1972 1977 1982 1987 1992 1997 2002

Kam pala - (JJA) Kam pala - (SOND) 600 1000 y = 0.6518x - 1081.1 y = -0.5565x + 1556.5 500 800 400 600 300 400 200 100 200

0 0 1962 1967 1972 1977 1982 1987 1992 1997 2002 1962 1967 1972 1977 1982 1987 1992 1997 2002

Seasonal Rainfall (mm) for Entebbe

Entebbe - (JF) Entebbe - (MAM) 400 1200 y = -0.2329x + 636.08 y = 2.8379x - 4921.7 1000 300 800 200 600 400 100 200 0 0 1962 1967 1972 1977 1982 1987 1992 1997 2002 1962 1967 1972 1977 1982 1987 1992 1997 2002

Entebbe - (JJA) Entebbe - (SOND) 600 1200 y = 0.9109x - 1547.2 y = 1.7686x - 2990.9 500 1000 400 800

300 600

200 400

100 200

0 0 1962 1967 1972 1977 1982 1987 1992 1997 2002 1962 1967 1972 1977 1982 1987 1992 1997 2002

38 AF91

Seasonal Rainfall (mm) for Bukoba

Bukoba- (JF) Buk oba - (M AM ) 600 1400 y = -2.7419x + 5727.8 y = -4.9486x + 10659 500 1200 1000 400 800 300 600 200 400 100 200 0 0 1962 1967 1972 1977 1982 1987 1992 1997 2002 1962 1967 1972 1977 1982 1987 1992 1997 2002

Bukoba - (JJA) Buk oba - (SOND) 500 1200 y = -2.0561x + 4243.2 y = -2.3689x + 5315.9 400 1000

800 300 600 200 400 100 200 0 0 1962 1967 1972 1977 1982 1987 1992 1997 2002 1962 1967 1972 1977 1982 1987 1992 1997 2002

Seasonal Rainfall (mm) for Mwanza

Mwanza- (JF) Mwanza - (MAM) 500 800 y = -0.7676x + 1739.2 y = -0.4764x + 1345 400 600 300 400 200 200 100

0 0 1962 1967 1972 1977 1982 1987 1992 1997 2002 1962 1967 1972 1977 1982 1987 1992 1997 2002

Mwanza - (JJA) M w anz a -(SOND) 300 1000 y = -2.3879x + 5159.7 y = -0.4948x + 1029.8 800 200 600

100 400

200 0 0 1962 1967 1972 1977 1982 1987 1992 1997 2002 1962 1967 1972 1977 1982 1987 1992 1997 2002

37 AF91

shows an increasing trend for all the seasons except in JF. Kabale has decreasing trend in the seasons JF and MAM and an increasing trend in JJA and SOND. On the average, time series analysis for the entire series (1960-2001/2/3) shows a decreasing trend for all the stations except Kabale and Entebbe. In all the stations except Mwanza, MAM receives more rainfall than SOND.

Seasonal Rainfall (mm) for Kisumu

Kisumu - (JF) Kisumu - (MAM) 400 1000 y = -0.4344x + 1023.7 y = -0.8478x + 2232.8 800 300 600 200 400 100 200

0 0 1962 1967 1972 1977 1982 1987 1992 1997 2002 1962 1967 1972 1977 1982 1987 1992 1997 2002

Kisumu - (JJA) Kisumu - (SOND) 500 1000 y = 0.6197x - 996.52 y = -0.7995x + 2017.3 400 800

300 600

200 400

100 200

0 0 1962 1967 1972 1977 1982 1987 1992 1997 2002 1962 1967 1972 1977 1982 1987 1992 1997 2002

Seasonal Rainfall (mm) for Kericho

Kericho - (JF) Kericho - (MAM) 400 1000 y = 0.0864x + 0.916 y = -1.7166x + 4016.3 300 800 600 200 400 100 200

0 0 1962 1967 1972 1977 1982 1987 1992 1997 2002 1962 1967 1972 1977 1982 1987 1992 1997 2002

Kericho- (JJA) Ke r icho - (SOND) 1000 1000 y = -1.7717x + 4003.8 y = -0.9929x + 2482.3 800 800

600 600

400 400

200 200

0 0 1962 1967 1972 1977 1982 1987 1992 1997 2002 1962 1967 1972 1977 1982 1987 1992 1997 2002

36 AF91

Kampala Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Mean 66.55 67.93 142.5 166.1 119.6 65.68 61.79 84.65 105.6 132.6 138.7 88.83 Median 57.46 54.35 139.7 167.7 107.2 68.75 47.85 85.75 99.71 113.2 127 82.5 Minimum 3.6 7.3 19.6 55.5 19.9 9.2 16.3 1.6 39.9 57.8 18.5 7.6 Maximum 223.7 188.5 311.8 307.4 219.8 163.6 175.5 159.5 181.5 335 449 185.7 Lower quartile 35.6 27.7 99.2 113.8 73.6 33.7 41.7 58.6 69.8 89.3 75.6 62 Upper quartile 83.4 98.5 173.9 191.6 166.6 88.7 77 109.8 142.1 160.3 182.2 119.8 Standard deviation 49.01 47.35 62.35 59 57.07 37.09 36.91 38.63 42.47 68.2 90.6 44.81 Coefficient of variation 73.65 69.7 43.76 35.5 47.72 56.46 59.74 45.63 40.2 51.4 65.3 50.44 Skewness 1.16 0.70 0.46 0.40 0.15 0.52 1.74 -0.08 0.21 1.30 1.50 0.34 Kurtosis 1.79 -0.27 0.30 -0.10 -1.12 0.11 2.84 -0.46 -1.09 1.20 2.90 -0.56

Entebbe Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Mean 88.78 86.08 192.7 265.9 259.4 110.4 72.7 73.83 75.84 140.7 166.3 129 Median 74.7 75.5 174.8 270.5 242.9 95.7 48.38 60.1 68.47 133.1 158.8 111.1 Minimum 7.7 5.6 7.2 117.9 86.1 31.6 12.3 9.3 21.5 26.4 27 36.1 Maximum 234.1 199.1 390.6 516.3 465.8 206.8 252.2 210.5 193.3 326.8 384.6 512.2 Lower quartile 44.67 42.85 128.4 174.9 198.6 68.95 34.3 42.1 39.05 87.3 117.8 70 Upper quartile 119.3 110.5 266.9 306.7 315.1 149.8 98.35 98.25 104.7 195.5 195.1 168.1 Standard deviation 56.54 53.21 93.2 96.2 90.3 51.23 56.83 46.9 43.41 73.2 80 86.5 Coefficient of variation 63.69 61.82 48.4 36.2 34.8 46.39 78.18 63.53 57.24 52 48.1 67.1 Skewness 0.75 0.56 0.30 0.60 0.40 0.48 1.48 1.04 0.82 0.60 0.80 2.40 Kurtosis 0.09 -0.65 -0.70 0.00 -0.30 -0.96 1.78 0.67 -0.22 -0.20 0.80 8.00

Kabale Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Mean 62.61 88.49 116.3 144.9 84.26 27.36 25.66 63.54 100.8 111 115.6 87.31 Median 56.39 80.26 113.5 149.1 69.6 16.5 10.1 52.86 94.8 104.8 112.1 76.2 Minimum 10.3 9.3 33.78 31.9 0 0 0 2.7 18.1 12.4 21.3 30.9 Maximum 204.2 183 194.6 271.2 216.3 133 120.4 143.1 181.3 238.5 261.5 172.5 Lower quartile 32.22 51.8 83.75 128.3 41.43 4.65 0 25.7 72.1 84 83.6 60.8 Upper quartile 83.6 120 154.2 166.9 125 43.65 48.45 93.4 118.6 130.7 132.8 105.7 Standard deviation 40.24 48.88 42.96 47.42 59.36 29.61 33.63 42.54 37.08 46.78 49.07 38.89 Coefficient of variation 64.26 55.23 36.95 32.74 70.45 108.2 131.1 66.94 36.78 42.16 42.46 44.54 Skewness 1.37 0.38 -0.05 0.13 0.74 1.73 1.32 0.35 0.29 0.89 0.95 0.56 Kurtosis 2.98 -0.81 -0.90 1.08 -0.40 3.41 0.73 -0.96 -0.25 1.58 1.35 -0.55

3. Seasonal rainfall analysis In the seasonal analysis, the seasons have been given as MAM (March, April, May), JJA (June, July, August), SOND (September, October, November, December) and JF (January, February). Kisumu shows a downward trend for all the seasons except the JJA season. Kericho shows a downward trend except in JF, which has an almost constant trend. Bukoba and Mwanza both show downward trends for all the seasons. Kampala shows a downward trend for all the seasons except the JJA season. In contrast, Entebbe

35 AF91

Kericho Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Mean 81.99 88.33 142.2 246.2 234.9 168.1 150.6 178.4 156.3 135.1 131.8 81.22 Median 71.2 71.5 142.2 239.6 227.4 147.8 132.2 169.2 145.8 127.4 106.2 55.65 Minimum 2.7 10.6 24.4 72.5 116.4 76.9 82 80.3 58.8 21.7 18.6 7.1 Maximum 224.8 192.9 331.9 487.7 441.2 363.4 274.3 331.3 322.8 282.9 418.7 287.7 Lower quartile 38.6 35.3 84.4 172.1 165.3 98.6 100.5 126.1 107.9 102.5 68.2 41.5 Upper quartile 111.6 136.3 187.3 290.8 282.2 216.1 200.8 224.9 214.4 168.6 162.2 108.5 Standard deviation 60.01 54.93 76.7 102.5 81 81.1 59.3 62 66.9 57.58 92.1 62.35 Coefficient of variation 73.19 62.18 53.9 41.6 34.5 48.3 39.4 34.8 42.8 42.63 69.9 76.77 Skewness 0.79 0.38 0.50 0.60 0.70 1.00 0.60 0.70 0.50 0.21 1.50 1.86 Kurtosis -0.19 -1.13 0.00 0.10 0.30 0.00 -1.00 -0.20 -0.50 0.25 2.10 3.44

Bukoba Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Mean 141.3 170.2 222.9 336.4 283.4 68.83 45.43 63.67 98.45 156.3 193.1 179.8 Median 138.1 185.3 232.8 343.3 287.4 65.5 32.1 50.1 87 150.6 178.6 167.4 Minimum 45.4 26.2 19.2 117.4 119.7 0 0 7.5 27.9 44.7 25.1 13.8 Maximum 286.1 302 357.5 630.2 517.7 256.7 172.4 172.7 221.5 311.4 477.9 361.9 Lower quartile 90.05 95.4 205.2 205.9 190.6 31.6 13.72 32.28 53.33 96.9 132.5 119.4 Upper quartile 183.9 245.2 267 411.5 366.4 90.25 53.9 86.33 132.1 222.5 253 242.2 Standard deviation 65.52 89.1 78.9 132.7 100.4 54.81 46.78 40.62 51.52 74 87.4 89.8 Coefficient of variation 46.37 52.3 35.4 39.4 35.4 79.63 103 63.79 52.32 47.4 45.3 50 Skewness 0.32 -0.10 -1.00 0.20 0.30 1.29 1.36 0.78 0.70 0.50 1.10 0.30 Kurtosis -0.64 -1.30 0.60 -0.60 -0.90 2.61 0.83 0.03 -0.33 -0.80 2.00 -0.60

Mwanza Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Mean 97.57 128.3 150.2 180.9 73.66 14.63 12.4 24.95 27.6 87.06 168.3 157.8 Median 73.2 108.3 144.2 184.6 57.8 7.2 1.4 18.9 19 84.3 166.7 150.1 Minimum 11.4 9.6 21.8 31.4 2.4 0 0 0 0 9.4 32.4 24.9 Maximum 231.3 289.5 385 362 182.8 116.3 71.4 104.8 82.3 250.7 399.3 378.1 Lower quartile 47.08 69.38 99.6 95.8 42.48 0 0 0 9.9532.95 126.6 102.1 Upper quartile 146.2 188.6 188.7 223.6 112.2 16.3 11.63 44.42 48.5 126.9 199.1 195.7 Standard deviation 63.48 74.02 86.9 81.1 49.97 24.11 21.65 29.58 24.34 60.2 79.9 78.1 Coefficient of variation 65.06 57.71 57.8 44.8 67.84 164.8 174.6 118.5 88.19 69.15 47.5 49.5 Skewness 0.72 0.53 0.70 0.20 0.52 2.82 1.81 1.28 0.81 0.74 0.80 0.90 Kurtosis -0.81 -0.72 0.30 -0.60 -0.71 8.61 1.80 0.87 -0.59 0.15 1.20 0.80

34 AF91

2. Annual and monthly rainfall analysis It can be seen from the table below that Kericho and Bukoba receive relatively high amounts of rainfall with relatively higher coefficients of variation, while Kabale and Mwanza receive the lowest. The summary statistics for the rainfall amounts are given in the table below. The rainfall ranges are; Kisumu (1028-1828mm), Kericho (897- 2420mm), Kabale (755-1282mm), Kampala (820-1929mm), Entebbe (1194-2142mm), Bukoba (884-2736mm), and Mwanza (697-1543mm).

Annual Rainfall

Kisumu Kericho Kabale Kampala Entebbe Bukoba Mwanza Mean 1369 1795.91027.9 1236.6 1593.4 1959.8 1123.4 Median 1344 1807 1017 1225 1570 2073 1125.6 Minimum 1028 897 755 820 1194.1 884 696.8 Maximum 1828 2420 1282 1929 2142.6 2736 1543.3 Lower quartile 1226 1517 925 1104 1431 1805 970.6 Upper quartile 1469 2123 1119 1342 1753.5 2254.0 1242.2 Standard deviation 195.0 373.0 132.1 219.6 228.2 490.0 222.4 Standard error of mean 36.0 68.0 23.7 39.4 41.0 88.0 39.9 Coefficient of variation 14.0 21.0 12.8 17.8 14.3 25.0 19.8 Skewness 0.73 -0.20 0.29 0.83 0.26 -0.58 0.03 Kurtosis 0.09 -0.63 -0.63 1.48 -0.40 -0.34 -0.73

Monthly Rainfall

Kisumu Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Mean 68.7 98.5 160.8 223.7 161.6 77.8 67.7 84.3 87.1 90.3 150.2 99.7 Median 54.8 83.0 165.6 222.2 152.6 66.0 62.8 81.6 84.9 83.7 138.9 87.1 Minimum 1.0 9.9 13.8 94.2 70.8 34.0 13.3 17.7 18.8 13.2 47.4 8.6 Maximum 204.3 257.8 306.0 397.2 358.0 173.1 194.6 159.7 152.3 195.1 449.4 292.8 Lower quartile 31.5 41.9 101.7 161.5 104.0 46.2 32.3 56.9 54.4 51.9 79.3 68.2 Upper quartile 96.8 146.3 223.9 290.5 197.1 104.4 88.8 113.4 113.2 140.2 172.1 127.8 Standard deviation 52.8 72.5 78.2 82.8 71.9 40.1 41.1 40.7 37.0 51.4 91.4 57.1 Coefficient of variation 76.9 73.7 48.7 37.0 44.5 51.6 60.7 48.3 42.6 56.9 60.9 57.2 Skewness 0.87 0.66 -0.30 0.20 0.80 1.03 0.97 0.14 0.08 0.49 1.60 1.46 Kurtosis 0.01 -0.69 -0.80 -0.90 0.20 0.25 1.25 -0.90 -0.83 -0.75 2.60 3.03

33 AF91

Probability Distributions of Annual rainfall (1961-1990) for selected stations in Kenya, Uganda, Tanzania

Mwanza Kisumu Kericho

Probability Plot (+95% Confidence Limits)

Bukoba Entebbe Kabale

Kampala

32 AF91 analysed on a seasonal basis in an effort to determine the trends of the dry and wet seasons. A time series analysis was done for the temperature data also.

Rainfall Analysis – Observed data 1. Probability distributions Probability distributions were fitted to the annual rainfall series and it was found that the normal and gamma distributions fitted well for all the stations. The gamma distribution was fitted to the annual rainfall in Kisumu, Kampala, Entebbe and Kabale. The stations Kericho, Bukoba and Mwanza followed the normal distribution. These distributions can be seen in the following diagrams.

31 AF91

What is the mortality from malaria in the health centre? This is very minimal in this centre, unless the patient is severely sick. But in that case the patient is refered to Kabale. But if properly treated and early, people should not die of malaria. In 2003 for example, we had eight deaths from malaria and pneumonia and these were mostly children.

What problems does the centre experience in the treatment of malaria? Supply of drugs is poor, but World Vision helps us with this. Staffing is poor – for instance the doctor only comes here rarely, and is in charge of the whole sub-district.

What do patients have to pay? The services are completely free.

How prepared are you for malaria epidemics? We try to be always stocked with drugs like fansidar and quinine for injection. But of course sometimes we are unable to. Like now we have received a consignment of drugs from the National Medical Stores which does not include chloroquine. This is dangerous.

We also run outreach education sessions twice a week, in which people are taught how to prevent malaria. This outreach is for the whole sub-county.

How do you treat the malaria? We use chloroquine and fansidar but the efficacy of this treatment is diminishing. Many people come back with high temperatures after this first line of treatment.

OUTPUT 4: CLIMATE DATA ANALYSIS

Introduction The climate data has been analysed for the meteorological stations that are close to the study sites in each of the three countries. These data are rainfall amounts, and maximum and minimum temperatures. The period of analysis for the rainfall and temperature data is 1960 to 2001 except for Kisumu and Kericho where the temperature data is from 1989 to 2001. Climate projections obtained from the IPCC Data Centre, for rainfall and temperature have also been extracted for two climate models, for this region upto the period 2100.

The meteorological stations are: • Kenya; Kisumu and Kericho • Uganda; Kampala, Entebbe and Kabale • Tanzania; Bukoba and Mwanza

Probability distributions have been fitted for the annual rainfall and the statistics calculated both on an annual and monthly time step. The goodness-of-fit of the distributions were tested using parametric and non-parametric tests. The data was also

30 AF91

On treatment, the majority reported that they use the health unit. Eight said they can use herbs if the malaria is not severe. They generally believe that the ‘mubirizi’ can cure malaria. Other herbs are ‘rukaka’, ‘mukazi murofa’, and these can even be mixed together. However, they observe that some people have a problem of using dirty water to mix these herbs.

How do you prevent malaria? The responses were much the same as in the other FGD, but added that even ventilators of the house should be protected, and that treatment should start early.

What interventions are there in Kamwezi? Bed nets subsidised by World Vision Spray of houses near water sources by the district health department Bed net supplies target pregnant women mostly

Who are most affected by malaria? • Malaria affects all but mostly children and pregnant women

What recommendations can you make to tackle malaria? • Bed nets be left to children and pregnant women because they are very vulnerable • Provision of safe water, mosquito nets • Provision of enough drugs • More community spraying

Interview with Tushabomwe Ben, Senior Clinical Officer at Kamwezi Health Centre Four (21-5-04)

When is malaria most common? Malaria is not high in the rainy season because mosquito breeding is not virulent. But towards the end of the rain it begins to rise. Breeding of the mosquitoes is more in the dry season. After rain there is stagnation of water, even in the banana shambas where the mosquites hybernate.

History: In 2001 there was a very high incidence of malaria after el nino. It was as high as 4,500 cases even in January. In 1998, April and May were also bad.

Who are the most affected groups? Children and women are affected most because their immunity is lower. They are poorly fed. I think for various reasons the men eat better than the women and children. Men rarely get sick and come to hospital. We can spend a whole month without admitting a man. Among those admitted, the majority are children. About 90% of malaria admissions are of children. People here grow a lot of food but they sell most of it to Rwanda. Then they are malnourished.

29 AF91

‘Nets are uncomfortable and they disturb a lot’ One male participant reported to have two nets. One for him and the wife, and one for the six children.

If there was only one net in the house, who should use it? One female said she and her husband should use it, and two males said they and their wives should use it. Only one said the children should have the net.

Why do you give priority to adults for the use of the net? The kids are many The kids are in different rooms and it is difficult to decide who to give it to Children will fight for it Children will spoil it It is better to protect the adult because he is the bread winner and he can also treat the kids However, one participant advised that if there is only one net in the house, it should be used by a pregnant woman or children because these are more vulnerable. Only two of the participants present had bed nets in their houses.

Why do some people prefer to go to private clinics? • There is no medicine in the government hospital • They give you only chloroquine in the government hospital, but in the private you get stronger medicine like chloromphenical • Lines are long in government hospitals • The government hospitals tend to give preference to the children, so the adult may have to go to private clinic • Government hospitals write for you the drug and you end up buying it in the private clinic • Sometimes government hospitals make too many lab tests and keep changing drugs • There was a time when people started to refuse paying for lab tests if no malaria was found, and this made the government hospital to temporarily stop lab services. But the situation has changed recently.

However, it was observed that some private clinics will prescribe medicine even when there is no malaria just in order to make money. Government health units are also credited because most times people get cured of the malaria.

How do you treat the malaria? None of the participants present admitted to self-medication, but at the same time said that it is a problem in the community. They said people resort to it because of costs, and sometimes the dose is even not completed. ‘The prescription may be for fansidar and chloroquine, but then you have money for only chloroquine and you only buy that one’. Or ‘sometimes the government hospital may prescribe fansidar and chloroquine but they only give the chloroquine and they do not have the fansidar. So you go away and only use the chloroquine since you do not have the money for the fansidar’.

28 AF91

Swamps are disappearing Banana plantations are getting destroyed

How does climate change affect people? Decreased food People die of malaria Rainy seasons are shorter Increased poverty.

How do poor people cope with malaria? • Go to health facility • Use herbal medicines such as ‘omubirizi’ • Sell land to treat malaria – 13 out of 30 people said they had ever sold land for treatment • Sell animals – goats (5 people said they had done so) • Sell coffee ‘My daughter and I got very sick in 1997 and went to the local clinic but we were not cured. So we went to Kabale Hospital by selling a piece of land at 60,000/-. Eventually we were cured at a cost of 45,000/-. But i even had to borrow some more money to supplement what I had in order to meet other related expenses like transport’ (Male participant, 56 years, also Chairman LC1)

‘My child of 6 years and I got sick in 1995 and had to go to Dr Biryabarema in Kabale town. We had to pay 30,000/- for the child and 30,000/- for me. Transport was 5,000/- for each of us. We were admitted for 3 days and we got better. I had to sell a piece of land at 80,000/- to get the money’ (Male participant, 50 years)

The first participants above said that as a safeguard against such an event, he bought goats which he can sell instead of having to sell land. He says he has also improved the hygiene around the house. The second one has cultivated coffee and has reared five goats as a safeguard against such an event.

Bed net use It was reported that bed net use is still very low in the area because of cost. Although World Vision is helping with nets at a subsidised cost of 4,000/-, this cost is still considered to be high. The nets are also few. One participant observed that poverty is a hinderance to the use of bed nets; ‘How can you buy a mosquito net when you have no money for the children’s school needs?’ (Female, 45 years)

One reported that some of the subsidised net were even taken back by World Vision because many people could not afford them.

What are the people’s attitudes to bed nets? Although they want the nets, they have some misgivings about them as follows: ‘Treated nets cause diseases such as flu (senyiga)’ ‘Nets cause heat and sweating at night’

27 AF91

However, it was agreed that bicycles and radios are not indicators of wealth because they have become very cheap.

What are the causes of malaria? • Stagnant water • Mosquitoes • Drinking dirty water • Not sleeping under mosquito nets • Broken pots and tins • Bushes • Not closing the house early One participant observed ‘Some people believe that eating mangoes or pineapples causes malaria, but actually the fact is that mosquitoes bite you when you are looking for the mangoes or other fruit then you get the malaria’.

What are the symptoms of malaria? • Shivering • High temperature • Joint pains • Lack of appetite • Headache

When is malaria most common? The participants believe that malaria incidence is highest during the dry season, high when the rains has just reduced, and low during the wet season. Some know the connection between temperature and malaria incidence and said ‘water warms in the dry season and the mosquito larva grow faster’. And another said that the mosquitoes also suffer during the rainy season.

MONTH SEASON MALARIA ACTIVITY January Dry Very high Harvesting February Dry Very high Ploughing March Rain Low Planting April Rain Low Weeding/Planting May Rain Low Weeding June Rain High Harvesting July Dry Very high Harvesting August Dry Very high Ploughing September Rain Low Planting October Rain Low Weeding/Planting November Rain High Weeding/Harvesting December Dry Very high Harvesting

Is there climate change? The participants believe there is a climate change and the signs are: Increased diseases Vegetation is disappearing

26 AF91

What is the mortality like from malaria? None of the people present had lost anyone in the family due to malaria. But they reported that there are many people dying of malaria, mostly the young children

What intervention programmes are available in your community? They report that there is none at the village level, but say that they hear of some interventions which they have not had access to. For example: Radio campaigns ICRAF neem tree trials (International Centre for Research Agro Forestry) NEMA is encouraging swamp restoration

What recommendations can you make for fighting malaria? • Improve the quality of health care • Supply safe water to villages • Government should provide chemicals to be used to kill mosquitoes in stagnant water • Malaria campaigns should be conducted from grass root • Provide enough drugs • Create sensitization groups • Encourage people to drink boiled water • Make mosquito nets more affordable • Drain stagnant water • Provide correct medication to patients • Encourage bush clearing • Promote afforestation • Promote use of preventive drugs • NGOs in malaria prevention should be supported • Government should fight corruption • Government should enough information on advantages and disavantages of the different malaria strategies • Swamps near settlements should be reclaimed • Government should educate the people about malaria prevention

Focus Group Discussion in Rukiga County, Kamwezi Sub County

What are the indicators of wealth? • Property • Quality of dressing (well dressed) • How one sleeps (have a matress, a net) • Good feeding (a balanced diet) • A good permanent house • Quality farming • Education

25 AF91

How do you get information on malaria? Radio Newspapers Television Health educators

What brought about the emergence of malaria in Kabale whey you did not have it before? Swamp reclamation and Deforestation

What signs show that there is climate change in Kabale? Mornings are warmer Less rain Less fog

Why do some people prefer to go to private health units? Services are quicker There are enough medicines in the private There is better personal care in the private There is no corruption in the private

Why do some people prefer to go to government health units? Services are cheaper Public health units have the skilled doctors, personnel They have the necessary equipment They provide educative information

To what extent are herbs used in the treatment of malaria? Herbs have for long been used but their use is decreasing. This is because (i) they have not been promoted in anti malaria campaigns (ii) herbs are now not believed to cure malaria.

Herbs commonly used include ‘Omuhoko’ which is mixed with other plants. Mix with hot water and cover yourself for steaming.

Also ‘Mukazi murofa’ (dirty woman) which is mixed with water and can be showered.

How is malaria prevented? • Drain stagnant water • Avoid broken tins, pots etc near the house • Close the house doors and windows early in the evening • Use mosquito nets • Clear bushes • Use anti mosquito sprays • Take anti malarial tablets regularly for prevention • Pour oil on stagnant water

24 AF91

The indicators of wealth were categorised as follows into the wealthy, medium and poor.

SOURCE OF INCOME Wealthy Medium Poor Big business Taxi driving Boda boda transport White collar jobs Small business Small subsistence farming Skilled jobs Small scale farming Porter, casual labour Large scale farming Artisans Dependants LEVEL OF INCOME Wealthy Medium Poor Above 500,000/- per month 100,000/- to 500,000/- Below 100,000/-

TYPE OF HOUSE Wealthy Medium Poor Brick/block permanent house with Mud wall and iron sheet roof Grass thatched iron sheet or tile roof Big size Medium size Hut Semi permanent Small size Temporary LEVEL OF EDUCATION Wealthy Medium Poor Tertiary Ordinary level None at all University Advance level Primary OWNERSHIP OF MEANS OF TRANSPORT Wealthy Medium Poor Vehicle Motor cycle Bicycle ACCESS TO MEDICAL CARE Wealthy Medium Poor Private health unit Main hospital Public health unit Self medication Herbs OWNERSHIP OF LIVESTOCK Wealthy Medium Poor Fresian cows Local cows Local chickens in small number Large herd Small herd Piggery Local goats OWNERSHIP OF LAND Wealthy Medium Poor Above 5 plans 2-5 plans Below 2 plans A ‘plan’ was approximated to be equal to about 0.15 acre

When is malaria commonest? MONTH MALARIA SEASON FARM ACTIVITY January High Dry Harvest February Rain Harvest March Rain Cultivation April High Rain Planting May High Little rain Weeding June High Dry Weeding July Dry Harvest August Dry Harvest September Rain Cultivation October Rain Planting November High Rain Weeding December High Dry Weeding

23 AF91

• Cost in private units • Long lines in government units • Shortage of drugs • Cost of drugs, for instance those prescribed but not available in the public health unit • Distance to health unit. Some people don’t return for review because of distance • Poorly trained or untrained practitioners make poor diagnosis and poor treatment

Do private practitioners have access to refresher training? It is not always available to them because they are usually very busy. But the courses could be available through NGOs.

Focus Group Discussion for Representatives of Kijuguta, Nyabikoni, Rutooma, Rwakaraba, Bugongi and Kirigime (20-5-04)

Community mapping The participants were asked to draw maps of their areas showing relevant features such as water sources, health units, valleys and hills, stagnant water, etc. Three groups were formed according to where they live, and three maps were drawn for Kijuguta, Nyabikoni and Kirigime.

What are the indicators of wealth in your community? • Source of income • Level of income • Type of house • Level of education • Ownership of a means of transport • Access to medical care • Ownership of livestock • Ownership of land • Electricity • Enough food at least 3 meals a day • Type of occupation • TV Ownership • Access to newspapers

22 AF91

• Active surveillance on trend of malaria through Highland Malaria project • Promote bed net use with assistance of NGOs like World Vision. These have helped to avail the bed nets at subsidized prices. Now the treated nets cost 5,000/-. • Selective spraying of breeding sites and homes, especially those near stagnant water • Undertake home visiting to identify breeding sites • There is a department of environmental protection at the district for environmental rehabilitation. • Most hit areas are in Rukiga, eg Ruhinda, Kamwezi, Kashambya, Rwamucucu. These are low lying areas and they border with Ankole

2. Interview with Enock Tumwesigye, Senior Clinical Officer In Charge Of Surveillance

What sort of data is kept by this department? Monthly reports are available only from 1985, and weekly are available from 2002. Data is collected at parish level

What trends emerge from the available data? Data shows that usually after heavy rains there is an upsurge of malaria. Data also shows that the bad years were 1991, 1996, 1998 and 2001. The worst was 1998. However, the apparent increase in number of malaria cases may be a result of better data collection and the increasing number of health units which supply the data. So it may be the reporting that is increasing. The scrapping of cost sharing may be a cause of more people visiting the health units. The severity of malaria is calculated in the basis of the cases as a percentage of population. (The interviewee provided us with a soft copy of the malaria data)

3. Interview with Twine J.B - Clinical Officer at a private clinic – Kabale Medcare Services

Why do some people prefer private clinics?

• Treatment in government health units is inadequate • The lines in government health units are too long, so time is wasted • Services are delayed • Government health units tend to have weaker drugs • Workers’ attitude in government health units is poor. Some are rude maybe because of big numbers • Government health units tend to have drug shortages

What are people’s attitudes to private clinics? Cost of treatment tends to be high

What are the constraints to accessing treatment?

21 AF91

What is the incidence of malaria in Kabale? In Kabale Malaria is endemic (almost all cases are common through out). Malaria commonest about 3-4 weeks after end of rain season. Malaria cases increased from 3000 in the recent past to 6000 cases now (May 2004).

What is the perception of climate change in Kabale? Swamp reclamation is partly responsible for the emergence of malaria because it seems to have caused climate change. ‘When I first came in 1985 we used to have due on our heads in the morning, but this no longer happens’. There now seems to be less rain, and these days you can do with less heavy clothing. Historically Kabale used not to have malaria, but the climate change that is being experienced may have caused it.

How do you manage the flow of information on malaria? All the 74 health units in the district make weekly reports to the district. There are Community Resource Persons who are trained in malaria and the management of anti malarial drugs. There is a project known as the Highland Malaria (HIMAL) project which started in 2002.

Malaria treatment: Cost: Whether poor or rich everybody gets treatment from the public health units because there is no more cost sharing. The treatment, first line treatment is chloroquine and fansidar. Quinine is given as a second line treatment for complicated malaria, and so far this treatment is okay. It is better to use weaker drugs, especially at the beginning, in order to avoid creating resistance to drugs.

Why do some people prefer to use private health units if public units are free? • In private people are attended to more quickly • Some people have a negative attitude to free things • Private clinics tend to give a bigger range of drugs to treat malaria, so they are perceived as being more effective. They also tend to give patients more drugs and this makes them feel more psychologically satisfied. • Private practitioners tend to give a wide range of drugs to cover many possible diagnoses • Reception in private is better

What is your attitude as government to private clinics? We promote partnership with them, and this partnership is embedded in the National Health Policy.

What interventions are in place to fight malaria? • Advocate for prompt malaria treatment because a delay in malaria treatment causes problems • Supply health units with anti-malarials • Community education and advocacy using voice of Kigezi

20 AF91 imitatation by the rest of the community. Dr. Opondo proposed that they come up with projects which can be sold to donors. Mr. Atito suggested a dairy cattle project while the PHT said people could be mobilised for a horticultural project and get assistance e.g. a water pump because areas adjacent to the Lake such as Kaloka are very fertile.

Prof. Wandiga suggested that small localised projects were more likely to have an impact instead of regional large scale ones. The proposed projects with proper details should be sent to Dr. Opondo. Mrs. Aruwa said that she has a women group, which is registered, has an account and also buys and sells maize but they applied for assistance to construct a borehole but had not yet received any assistanc. She was told by Mrs. Nambaka of KWAHO to follow up her application and she would be assisted.

Dr. Olago inquired about the conditions of health facilities in the area. The PHT said that the government health facilities offer free treatment for all cholera patients and that all cases are now regarded as an emergency. However, people have to walk up to ten kilometers to access a health facility and this is a problem. Mr. Mwoso also lamented the lack of cholera diagnosis facilities in health centres because people are referred to district/provincial hospital in Kisumu. Mrs. Nambaka said that the area also lacks enough latrines and only KWAHO and Plan Kenya assist in providing these facilities in the area.

At the end of the meeting, Prof. Wandiga presented certificates of participation to the participants.

List of participants. Participant Address Telephone 1. Calesine Nambaka Box Kwaho, 40, Kombewa 057-22648/0733713253 2. Christabel Ombok Box 1, Kombewa - 3. Peter Mwoso Owano Box 1, Kombewa - 4. Jechoniah Onyango O. Box 24, Reru - 5. Adino Peter Omuga Box 15, Kombewa - 6. Juliana Auma Aruwa Box 96, Maseno - 7. Collins O. Otunga Box 255, Kombewa - 8. Gordon Owiti Omunga Box 82, Kombewa - 9. Walter O. Ongoro Box 60, Kombewa 0733236060 10. Dorka Akinyi Agoro Box 1, Kombewa - 11. Atito Lodfick Omondi Box 84, Kombewa 0734846688 12. Florence A. Okello Box 82, Kombewa -

OUTPUT 3: PROGRESS REPORT OF THE UGANDA AIACC STUDY FOR JANUARY-JUNE 2004

Kabale Focus Group Discussions and Key Informant Interviews - May 2004

1. Interview with Dr. Ndabakulekeza, acting director of district health services, Kabale.

19 AF91 one chews premature sugarcane, is heavily rained on e.t.c. Dr. Opondo asked, “what are the other coping mechanisms apart from creation of awareness?” Mr. Mwoso said that when they are creating awareness they can also mobilize the community to initiate their own sanitary and safe water projects which they can also support as a community.

Mr. Atito said that creation of awareness on projects in the area particularly the boreholes are very important because some people refuse to pay even as little as one shilling per jerican. Calesine said that due to poverty, the government and other organisations should give financial assistance e.g. loans to increase the local people’s income. Prof. Wandiga said that we have become very dependent on government and donors and so we need to do things on our own. He gave an example of planting eucalyptus trees which could produce telephone and electricity poles selling at Kshs. 10,000 each. He said, “kujitegemea (i.e. relying on our own resources) will help us kick out poverty”. On this note, Mr. Atito said that people can also rear chicken and also plant tomatoes but people have not realized this potential they have been used to dependance on handouts. He said 2-3 people could start this project as an example and others would follow. He said there is good potential of rearing chicken because most of the chicken eaten in hotels in Kisumu come from outside the district.

Dr. Opondo said that Africanow, an NGO, has a bee-keeping project which could also be explored. She said that people should act as models by starting the business and others could follow. Mr. Adino said that things are difficult because even small businesses need money/capital. He said that the bee keeping project in the area could be difficult because their animals graze in the bush and bees could attack these animals. Prof. Wandiga remarked that once there is a will on the part of the communities, there are many organizations which were ready to assist. Mrs. Agoro said, “sio ati ni vile tumekaa tu tukingojea tusaidiwe lakini tunajaribu hata kulima lakini huko kwetu ni kame na mvua ni kidogo”. Prof. Wandiga said that we must not necessarily rely on rain and he gave an example of Israel which is dry but does well in horticulture. Mrs. Agoro still insisted, “hata tukijaribu namna gani, chakula inaisha tu hivyo kwa ukosefu wa mvua”. Mr. Atito said that NGOs in the area tend to rely on women groups, which are formed without vision e.g. during political rallies, and this is why they fail. He said that teachers within Kombewa formed a group and through UNICEF they have a borehole from which they are able to earn some extra income.

The pastor said that the bad attitude of people in Luoland of relying on others e.g. politicians reduces their motivation towards eradicating poverty. Prof. Wandiga replied that we have to kick out poverty by ourselves. Mrs. Okello said that we should also come up with a solution for laziness because some people have land but do not cultivate them. She also said that Plan-Kenya donated seeds to some people in the area but they in turn sold them to others instead of planting. Prof. Wandiga stressed the need of using their own resources instead of relying on others. Dr. Olago said that churches in the area are many and can take up projects. Dr. Githeko said that if they organised themselves and came up with projects, then it can be possible to approach various organisations in Kisumu such as the Lions Club and Rotary Club for assistance. Mr. Atito said that they could identify a few people in a small area whose projects could be assisted to encourage

18 AF91

OUTPUT 2b : 2ND COMMUNITY STAKEHOLDERS MEETING REPORT: 4TH JUNE 2004, KISIAN, KISUMU.

Introduction The meeting started at 11 a.m. Prof. Wandiga gave an overview of the project. Dr. Opondo gave a presentation on the vulnerability to cholera in Kisumu. Dr. Olago presented the analysis on climate and hydrological and extremes in the Lake Victoria basin while Dr. Githeko made a presentation on the impacts of climate change on human health.

Discussion (Q & A Session) After the presentations, Prof. Wandiga invited the participants to ask questions or give comments on the incidence of cholera in their communities in the light of the presentations.

Mr. Mwoso asked, “What causes the change of climate so that climate affects cholera and malaria since there is need to identify what causes climate change so that we can survive”. Prof. Wandiga said we should not worry much about the politics surrounding global warming but we should ask ourselves what we should do to survive because some countries like America whose air pollution contribute to global warming might not change their lifestyle. Dr. Opondo said that we could only make ourselves cope and adapt to these changes.

The PHT said that many people are not aware of the conditions that cause cholera epidemics hence the need for creation of awareness and interventions measures e.g. by NGOs on use of protected boreholes/water sources because some people just use water from open sources. Those who use water from open sources do it often because they cannot afford to pay the already subsidized cost of collecting water from the boreholes. He said that KWAHO has installed water projects in the area and many more NGOs should come up and do the same. Dr. Opondo asked, “is it that people do not want to pay for borehole water or is it because they don’t have money?” The PHT said that protected water sources are usually managed by local women’s’ groups and so some people by paying for borehole water would benefit only particular women’s’ groups. This is due to lack of understanding and also a bad attitude since the amount paid for collecting water is really meant for maintenance of the borehole. Ms. Nambaka, who works for KWAHO said concurred saying that people continue to refuse to pay for water from boreholes and would rather collect water from unprotected sources. Therefore the participants can create awareness on use of protected water sources. Prof. Wandiga suggested that health officers and the community could work together to see proper use of protected water sources. Mr. Mwoso said that there is need to mount capacity building to enable all people to be acquainted with knowledge on impacts/influence of climate change to cholera. All the participants agreed on the need for creation of awareness.

Poverty in the area is also another reason why they refuse to pay and prefer river/lake water which is free. The PHT said that when creating awareness they need to emphasize on diseases because some people think cholera is airborne and that malaria comes when

17 AF91 agreed that family planning would reduce clearing of forests and it also reduces the economic burden of preventing malaria, because fewer ITNs would be required by smaller households. The councillor also said that people living in fragile areas should be resettled to less fragile areas to avoid environmental degradation.

At the end of the meeting, Prof. Wandiga presented certificates of participation to the participants.

List of participants. Participant Address Telephone 1. Ev. William Kiripan A.C.K Church, Box 181, ______Kericho 2. Paul Koech Box 19, Sosiot, Kericho ______3. James Kimeto Box 70, Sosiot, Kericho ______4. Benjamin K. Tele Box 165, Sosiot, Kericho 0722810661 5. Mrs. Kerich Kerich Teldet Primary, Box 4, ______Sosiot 6. Lillian Tuwei Teldet Primary, Box 4, ______Sosiot 7. Monica Yegon Box 31, Kabianga 0721753066 8. Susan Morogo Box 31, Kabianga 0733564069 9. Richard Kirui Box 34, Kabianga 0734745545 10. Leah Chesengeny Box 12, Kiptugen ______11. Rebecca Kirui Box 12, Kiptugen ______12. Kimtai arap Chelulei Box 56, Kiptugumo 0721386229 13. Kemei Kemei Box 1776, Kericho 0722265801 14. Lydia C. Rop Box 1776, Kericho 0722920205 15. Moses ole Kiriongi Box 9, Sosiot, Kericho 0722319432 16. Mary Odongi Sosiot health centre 0722742621 17. David Wandera Sosiot health centre 0722810661 18. Paul Koech Sosiot health centre 0720849958

NOTE: The last three participants are members of staff at Sosiot health centre and they came in as observers and were not present in the 1st community stakeholders meeting. Those who were absent were: 1. William Kirwa – because he feared attending the meeting in his former work station where he was in-charge but forced to transfer by the community. 2. Emmy Chepkorir – on maternity leave 3. Paul Keter – no reason

16 AF91 mpaka ng’ombe wanatoroka na kwa hivyo herbs huwa sawasawa kwa sababu mimi ninapokunywa hizo herbs hukaaa sana bila kupata malaria, miaka 2-3 bila kwenda hospitali”. (i.e. in Baringo, the use of herbs has been very effective in treating malaria and boosting immunity because when one uses medicinal herbs it can take 2-3 years before one contracts malaria again). He said that indigenous knowledge is very important and so there is need for greater scientific collaboration between traditional healers and western trained medical doctors. He said that medicinal herbs are popular because health centers are sparsely distributed in Baringo and some people have to walk up to 150 kilometers to reach to the nearest health facility.

However, a word of caution from the chief who said that nowadays there are also many quack herbalists promoting various types of herbs purely for monetary gain. Dr. Githeko indicated that with medicinal herbs there is the problem of quality control. However, towards this end KEMRI has a whole centre for research and this should continue to be expanded.

The councilor said that in his area, when people have malaria they take some traditional leaves and “vomit out the malaria”. Mrs. Kerich, a teacher, said that some people do not traditional herbs because they associate them with devil worship hence there is a need for government policy on traditional herbs.

Prof. Wandiga said that there is need to plant trees that have medicinal value and those that act as mosquito repellants. Thus if people plant trees such as eucalyptus, this could be exported as telephone and electricity poles, used as firewood and act as mosquito repellants. Dr. Githeko said that there used to be sacred hills, swamps e.t.c. but now they are destroyed hence the need to restore them. Mrs. Kemei said that people clear bushes because of poverty hence need for donation of seedlings so that people can plant trees.

Rebecca said that traditionally people had herbal medicine which cured malaria but young people don’t like it because they are used to modern medicine. Prof. Wandiga suggested that a Strategic Action Plan be that we teach people e.g. on the use of medicinal herbs through tree planting and after a period of one year an assessment on its impact can be done. The chief supported this suggestion and requested that the participants in the workshop should live by example and should seek to disseminate the information from the workshop to at least another 10 persons. Mrs. Kemei supported saying they should be donations of seedlings with both medicinal and economic value would be easier to implement because “pesa ni sabuni ya roho”.

Dr. Githeko said that they should reduce global warming by planting trees because they act as “climate buffers”. Another participant said that another adaptation strategy would be to do family planning because by reducing children you avoid clearing of forest since if you have ten children you have to subdivide the small piece of land to them. However, one of the participants said that in Kalenjin, it is a cultural problem because some men believe that they must have male children and thus the large families. One of the participants said there is a politician who also opposed family planning during a public gathering saying that it would reduce the number of votes in the area. The participants

15 AF91 them.

Mr. Kimeto said that malaria is more serious than HIV/AIDS hence the need for the government to heavily subsidise it. For example, the government should issue ITNs at subsidized price of Ksh. 100 to make them accessible to more people in the community.

Mrs. Kemei asked whether a malaria vaccine could be developed because its treatment is increasingly becoming expensive. Dr. Githeko replied saying that experiments on vaccines have been going on for the last twenty years which when tried on mice work, however it does not work on monkeys, because the malaria parasite is very “tricky”.

The councilor narrated how recently, a three-month old child died of malaria while being taken to Kericho District hospital because there was no nearby health facility. Moreover, the child was being taken to the hospital only by the mother and without money because the father was engaged in a drinking spree.

While closing the first session prior to the tea break, Dr. Olago said that the research team planned to meet high level policy makers and would present the ideas discussed in this session to them.

Building Socio-economic scenarios Dr. Opondo guided the participants on building socio-economic scenarios after tea break. Several issues where raised during this exercise. One of the participants, a P.H.O lamented that people are now destroying vegetation more than ever e.g. by cutting down trees and forests to plant tea and this has an impact on the incidence of malaria as temperatures increase in the highlands.

Dr. Githeko said that people in the highlands can control malaria through environmental management e.g. by planting eucalyptus trees which repel adult mosquitoes and Mwarubaini trees which kills mosquito larvae.

Mrs. Kemei said, “we have used all the available anti-malarial drugs but not only are they expensive but sometimes they are not effective, so what should we do?” Prof. Wandiga said that countries such as China, Japan and Korea rarely used Western medicine. They rely on indigenous knowledge and therefore it is important for us Africans not to necessarily rely on Western knowledge because there are even local herbs that treat malaria.

Mr. Wandera, a clinical officer said that there are some drugs such as Oronda given by the government that are generic and are not very effective in combating malaria. Thus it was agreed that there should be health education on proper use of drugs including new ones. Monica who is a nurse also supported this.

The councilor and Mrs. Kemei suggested that if possible, leaves of certain trees could be identified and be used as mosquito repellants. The pastor contributing to the debate on indigenous medicines stated that, “huko kwetu Baringo kuna wakati mbu wanaingia

14 AF91

OUTPUT 2a : 2nd COMMUNITY STAKEHOLDERS MEETING REPORT: 3RD JUNE 2004, SOSIOT, KERICHO.

Introduction The meeting commenced at about 11.30 am. Prof. Wandiga gave an overview of the project. Researchers presented research findings. Dr. Opondo gave a presentation on the vulnerability to malaria in Kericho. Dr. Olago presented the analysis on climate and hydrological and extremes in the Lake Victoria basin while Dr. Githeko made a presentation on the impacts of climate change on human health.

Discussion (Q & A Session) After the presentations, Dr. Olago invited the participants to ask questions or give comments on the incidence of malaria in their communities in the light of the presentations. One of the participants who is a local Chief expressed his concern that there is no microscope in the dispensary in his administrative location. He wondered whether the researchers could advise the government or other organizations such as KEMRI e.t.c. on the lack of equipment for diagnosing malaria. He stated that, “it is serious that the incidences of malaria have really increased in the area yet they cannot roll back malaria because of poverty”.

Mr. Kirionki, a Public Health Officer (P.H.O), said that what they are experiencing is a reality i.e. global warming, but due to poverty, people are not given proper medicines. They are given medicines that are dumped in Kenya by the western countries and yet some of them have refused to sign agreements on global warming. He also said, that it appears futile spraying insecticides on stagnant waters to eradicate mosquitoes and yet a change in climate (increase in temperature) was most likely to lead to malaria epidemics. Dr. Githeko responded that it is true that people are polluting the environment everyday thus contributing in some way to global warming. However, what is important is that affected communities need to cope by adapting to the impacts of climate change.

Prof. Wandiga said that we should not believe that nothing could be done in spite of world politics on global warming because we for sure know that climate is changing and malaria is with us. He paused, “So what should we do? Shouldn’t we use insecticide treated ITNs (ITNs), built proper houses that can keep away mosquitoes, while politicians continue to negotiate how to reduce global warming?

Another participant, who is a locally elected councilor, said that some shopkeepers at the local trading center got their shops painted with advertisements of ITNs yet there are no ITNs available in those shops. Mrs. Kemei, a medical laboratory technician explained that those who got their shops painted did it and stocked the ITNs then mainly because they wanted their shops painted. At the same time not only is the profit margin for ITNs very low, most of the people in the community cannot afford to buy them. For instance, the retail price for ITNs is Ksh. 350 and the wholesale price is Ksh. 300 when transportation costs are include it means that some shopkeepers could end up without any profit. Mrs. Kemei also said that even those who can afford to buy ITNs might not be able to treat them, because this is an extra cost over and above the Ksh. 350 paid for

13 AF91

Agenda Minutes Action SESSION and an MA student took up this place. • Tanzania hopes to get the recently graduated MA student to continue with PhD on the project. • Acknowledgement that Michael has something to offer and contribute to the project. • Request from Prof. Mutua to inform Tim Downs about the pending requests for hydrological data.

• Vote of Thanks.

12 AF91

Agenda Minutes Action 17th JANUARY • Need to acknowledge Bob Watson and therefore a moral 2004 obligation to deliver information that can inform policy and so it is not just an academic exercise. Cholera in • The 4th IPCC Assessment is due in April 2004-2008 and Uganda – Dr. outputs from AIACC projects is expected to be inputted Bwire into this Assessment. (Andrew might be on this panel).

Plenary Discussion

• Check what Kenya has done to ensure consistent across the three countries.

Gap of community involvement needs to be included in Tanzania and Uganda. • Authorship of papers – integrity of authors needs to be protected and so only those who have written the papers can be the authors, but acknowledgment must be done for other team members and AIACC. • Ugandan team needs to give a time frame on when they intend to hold the • Socio-economic analysis – Uganda and Tanzania need to Community Stakeholders Meeting. beef up the FGDs. Tanzania started with group discussions (PRAs); 1 FGD on elderly people and local traditional healers. • The ideas on coping mechanisms should be participatory • Thus the Ugandan PhD student needs to integrate the AIACC data into the and therefore need to consult the communities during the PhD thesis and acknowledge AIACC. FGDs to give them ownership, this is particularly critical during the implementation of SAPs. • Uganda – Community Stakeholders’ Meeting not yet undertaken instead it has been the National Workshop that was reported as the former?? • Michael to visit all three countries to share his expertise. Need to work out a • It was emphasized that resources in the AF91 project are timetable so that his visits are not haphazard. not limitless, but rather are tied to the budget. • Action to be taken by Prof. Wandiga.

• However, leftover “savings” from fields for contingency.

• Capacity Building – is at the essence of AF91, currently 1

MA student has graduated, 1 PhD student is still working on CLOSING the thesis. A PhD student was removed from the project

11 AF91

Agenda Minutes Action Presentation • Overview of the socio-economic analysis progress. Discussion (Q & A Session) • Overview of the socio-economic progress. • Clarification = the socio-economic data analysis not to be done centrally but in the respective countries. Since one of the key objectives of this project is capacity building, in- country analysis should be encouraged. There appears to have been a misunderstanding on the part of the Tanzanian team. • Differentiation between localized and climate induced cholera clarified. • Historical data on cholera for correlating with hydrological Draft Papers of data would be useful. Articles • The Dakar presentation for Tanzania should be for those activities undertaken after July 2003.

Discussion of Draft Papers • Socio-economic – to inform policy makers = 3 papers drafted

• Assessing the state of health East Africa = 1 paper drafted

• Hydrology of the Lake Victoria Region = 1 paper drafted

• Climate = 1 paper drafted • Synthesis Paper (i.e. where all the issues on socio- economic, health, climate and hydrology) = towards the end of the year, but build-up towards this paper has to begin now. • Documentation lacking for African scholars in spite of the good research being undertaken in the continent. Capacity building must be continuous and publications are one way. rd • The 3 Assessment IPCC Report – Developing countries will be most vulnerable to climate induced diseases and yet there is a dearth of information to inform policy and confront the realities on the ground and so AIACC came in to fill this gap.

10 AF91

Agenda Minutes Action 6. Similar to No. 2 above • Need for a meeting to discuss this??? 7. Critical indicators to help identify vulnerability. • Maggie to send Dan the data on Kabianga and Sosiot so that he can play Need to identify the envelop of vulnerability i.e. the around with the data to establish relationships with hydrology. indicators from hydrology and climate. • Michael to use NDVI to think about the indicators.

• Faith and Michael to test the TOP and TOPO models to determine their 8. Socio-economic scenarios – create dummy variables? appropriateness in the project. • Descriptive inclusion of socio-economic variables to input into the TOP/TOPO models if they work after Faith and Michael have tried them out. • Maggie (with assistance from Dan) to download and see how it works • Water Poverty Index available at Wallingford Institute for Hydrology • Socio-economic teams in the respective countries to explore the various statistical techniques. • Use of Factor Analysis and other statistical tests. • Dan (15th of February 2004).

9. Validation and sensitivity • Andrew

10. Statistical and multiple regression analysis – similar

to No. 2 above

11. Changes in risk magnitude

• Agreed to list down factors contribute to risk, establish

their spatial distribution and weight each factor and create • Michael to work with Maggie, Richard and Robert.

layers and have a risk map.

• Cluster analysis and then discriminant analysis and then

the consequent outputs can be used to input into GIS. E.g.

distance to water source and estimate the number of

cases. 16TH JANUARY

2004

Tanzanian

Update

9 AF91

Agenda Minutes Action assumption that it meant precipitation data. • Need to custom build local scenarios before projecting on to global scenarios. • Study sites are too small for modeling – hydrological models do not perform well in <100 km². • Important to understand what the other users (in the project) want when running the hydrological model, i.e. to get a clear requirement of the hydrological output. Work plan for 1. Statistical downscaling of climate models – ECAM & • Recommendation that we use MM5 because it lends itself to comparisons the next 8 MM5 are more or less regional than global, but have been with other African regions. months found to be consistent with the African situation. However ECAM not good for predictions. • Action to be taken by Faith Githui by 2nd February 2004). • Downscaling to take 2 weeks • Downloading of other models after the MM5

2. Time Series Data – Output has to be summarized for users, and also to identify analogue years for drought and wet events. • Dan to forward the coordinates of the sites to Faith so that she can identify • Climatology (daily data of stations) of study sites needed the appropriate meteorological stations for data collection. for health analysis. But it is necessary to identify the co- ordinates of data required. • Temperature is a challenge because it is not yet in soft • Andrew to do the correlations – outputs from Faith expected in February form. 2004. • Correlation analysis of the different data sets – this analysis has already been done and can be used to answer the WHAT IF? Question i.e. simulating the future

3. Downscale outputs • Similar to the analogues referred to above.

4. Regional climate models • Already discussed under No. 1 above. • Action to be taken by Faith. 5. Selected climate change

• Also tied to No. 2 above.

• Outputs by early March 2004.

8 AF91

Agenda Minutes Action Andrew Githeko (i) the relationship between El Nino and cholera outbreaks (ii) The interplay of land use and climate variability in the Highlands • Presentation of the health data analysis so far undertaken re: showing the trends of cholera and malaria in the six study sites vis-à-vis temperature patterns. Discussion (Q & A Session) • Therefore it is important to project vulnerability by considering socio- • Need to have a broader definition of epidemics by taking economic characteristics of households (community and individual levels) into consideration both the mortality and morbidity rates. and medical infrastructure, roads etc to determine vulnerability and this can • Malaria and cholera are not medical issues but more socio- then be factored into climate change. These issues need to be emphatically economic dependent. For instance, the difficulties of stated, even if they cannot be factored into a model. getting to hospital and lack of drugs in hospitals which leads to self-medication which in turn lowers resistance to disease. • Suggestion of using money for FRAs and RAs to collect medical data. Michael willing to lend a hand in compiling the medical data. • Data on malaria and cholera cases for Uganda still lacking • Robust data on health for Uganda can be availed since Dr. Bwire in now part and data from mission hospitals are better because not of the team. affected by policy changes and they usually keep good records.

Update on Hydrology – Dr. Dan Olago Presentation • Overview of research questions, objectives and methodology related to climate and water data and • Filling in the data gaps not difficult and is currently being undertaken by one preliminary analysis. of the students. • Indication of data gaps to be filled. • Hydrological data can be fed into the malaria model and thus generate a more accurate prediction of epidemics • “Where are we now?” – pace has been slow due to among other factors poor co-ordination between the various actors and not keeping to the work plan. Discussion (Q & A Session) • No hydrological data from Uganda and Tanzania of the

7 AF91

OUTPUT 1: MINUTES OF RESEARCH COORDINATION MEETING AT COLLINE HOTEL - MUKONO, KAMPALA, 15TH – 18TH JANUARY 2004

Participants • Prof. Shem O. Wandiga • Prof. Francis Mutua • Dr. Dan Olago • Prof. Paul Mugambi • Mr. Michael Marshall • Ms. Faith Githui • Mr. Magezi- Akiiki • Dr, Maggie Opondo • Mr. Robert Kabumbuli • Mr. Timothy Baguma • Ms. Robina Nanyanya • Dr. Richard Kangalawe • Mr. James Gathuri • Dr. Pius Achola • Dr. Andrew Githeko • Dr. Godfrey Bwire • Mrs. Noel Abuodha

Agenda Minutes Action

15TH JANUARY 2004

Update on Presentation Health – Dr. • Mapping of the incidence of cholera and malaria, such as

6 AF91

5 AF91

• Multi-criteria evaluation of viable strategies to identify preferred adaptation options. • Produce strategic action plans (SAPS) for pilot community adaptation. • Resource mobilization follow-up. • Implementation of priority adaptation/mitigation actions from SAPs. • Monitoring and evaluation of SAPs. • Modification of actions as required SAPs. • Field manual: “Integrated capacity building for community-based adaptation to climate-induced malaria and cholera health risks” • Dissemination workshop. • Final report writing.

F. EXPECTED DIFFICULTIES

We do not anticipate any difficulties in carrying out the remaining tasks.

G. LESSONS LEARNED Using international databases to fill the gaps of locally unavailable health data. For example, data on cholera was not available locally and the household surveys did not yield much because cholera is a climate-dependent episodal disease. However, after accessing cholera data from the WHO database, we have been able to neatly correlate cholera incidences with climate and hydrological data.

H. PUBLICATIONS

1.1 “Vulnerability to Climate Induced Highland Malaria in East Africa” by Shem O. Wandiga, Maggie Opondo, Dan Olago, Andrew Githeko, Pius Z. Yanda, Richard Kangalawe. 1.2 “Vulnerability and Adaptation to Climate Change-Induced Malaria and Cholera in the Lake Victoria Region” by Pius Z. Yanda, Richard Kangalawe and Rehema Sigalla. 1.3 “Variability of Malaria Epidemics in East Africa: Need For Policy Change and New Adaptation Strategies” by Shem Wandiga, Andrew Githeko, Maggie Opondo. Paper presented at the ANCAP Regional Workshop, 26th-27th April 2004, Kampala , Uganda. To be published in Workshop Proceedings.

4 AF91

(i) Organization of workshops and meetings. (ii) Disbursement of funds. (iii) Co-ordination of data collection, entry and analysis. (iv) Reviewing of journal paper.

C. DIFFICULTIES ENCOUNTERED Availability of all key informants did not always synchronise with the field visits necessitating revisits which were not 100 per cent effective. Lack of accessibility to some of the health data because records had been destroyed by fire. Translation of climate change concepts into the local languages proved to be a challenge. The SWAT model for constructing of socio-economic scenarios did not yield any useful results.

E. TASK TO BE PERFORMED IN THE NEXT EIGHT MONTHS The following tasks will be undertaken in the next eight months:

Data Analysis: • Downscale the outputs of the climate models and scenarios of changes in extreme events (the El-Nino years and La Nina years) and to assign probabilities to these. • Use some regional climate models that are available (e.g Ssemazi, et.al.). • Selected climate change models will be used to estimate possible changes to baseline conditions i.e. perturbations to temperature and precipitation. • Use a range of increasing and decreasing synthesis of extreme seasonal rainfall and temperature scenarios, and some of the observed extremes and trends. • Assemble all critical indicators (data) available to help identify vulnerability and adaptation measures for creating scenarios. • Construct socio-economic scenarios using appropriate model, whichever can better handle the socio-economic data alongside other components such as health data, climate data, land-use and hydrology. • Validation and sensitivity testing of the climate and health data. • Apply statistical and multiple regression analysis to correlate health, socio-economic, hydrology, habitat and climate data. • Estimate changes in risk magnitude using statistical models of P, T, vs. M,C. • Uncertainty analysis of estimated risk scenarios. • Statistical correlation of climate data (P, T) and health data (M, C). • Time series analysis, climate-disease correlations and parametric modelling and GIS risk maps. • Research group meeting to discuss results and flag data gaps. • Complete manuscripts for 2 journal articles. • Primary data collection from risk groups. • Workshop with key policy makers and international agencies (eg UNEP, WHO). • Meeting with pilot communities to present initial results and initiate strategic planning for Uganda and Tanzania. • Formation of local working groups. • Identification of all possible community based M, C risk adaptation/mitigation strategies.

3 AF91 sensitivity testing of the climate and health data has been done. Assessments of the present and future hydrology of the Lake Victoria have been undertaken. Transformation of socio- economic data into GIS format has been done. Construction of socio-economic scenarios using the SWAT model was attempted but the results were not meaningful and therefore it will not be one of the models to be utilized in the project. The process of identifying another suitable model is on-going.

B. TASKS PERFORMED AND OUTPUTS I. Meetings 1.1 Regional Research Group Meeting was held in Mukono, Uganda from 15-19 January 2004. The purpose of this meeting was to enable the researchers to present the research results for socio-economics, hydrology, climate and health studies. This meeting also enabled the review and assessment of project activities and preparing a work plan for the next eight months of the project. During this meeting the researchers also prepared outlines for possible paper publications (Output 1).

1.2. Workshop meetings with pilot communities in Kenya to present initial results and initiate strategic planning were held in June 2004. During these workshops some of the possible community based malaria and cholera risk adaptation/mitigation strategies were identified. (Output 2a & b).

II. Data Collection 2.1 Qualitative primary data collection in Uganda and Tanzania using key informant interviews and focus group discussions was done. (Output 3).

2.2 Hydrological Data for Kenyan rivers has been done.

III. Data Analysis (Output 4, 5 & 6) 3.1 Statistical downscaling of climate models. 3.2 A statistical analysis was ran for the time series data in order to estimate the probability distribution functions for temperatures and precipitation for each decade (baseline variability for 1960-70, 1970-80, 1980-90, 1990-2000). 3.3 Downscaled the outputs of the climate models and scenarios of changes in extreme events (the El-Niño years and La Niña years) and assigned probabilities to them. 3.4 Assembled all critical indicators (precipitation, temperature, cholera incidences, malaria incidences) available to help identify vulnerability and adaptation measures for creating scenarios. 3.5 Validation and sensitivity testing of the climate and health data was partially undertaken. 3.6 Assessments of the present hydrology of the Lake Victoria region done. 3.7 Construction of socio-economic scenarios using SWAT model was attempted and abandoned. 3.8 Digitalization of socio-economic data into GIS maps completed.

IV. Administrative Outputs The following administrative activities were carried out:

2 AF91

PROGRESS REPORT FOR JANUARY TO JUNE 2004

PROJECT NUMBER: AIACC AF_91

PROJECT TITLE: CAPACITY BUILDING TO EVALUATE AND ADAPT TO CLIMTE CHANGE- INDUCED VULNERABILITY TO MALARIA AND CHOLERA IN THE LAKE VICTORIA REGION PRINCIPAL INVESTIGATOR: SHEM O. WANDIGA

SUPPORTING ORGANIZATIONS: GLOBAL SYSTEM FOR ANALYSIS, RESEARCH AND TRAINING (START), THIRD WORLD ACADEMY SCIENCES (TWAS), UNITED NATIONS ENVIRONMENT PROGRAMME (UNEP)

A. SUMMARY:

The third phase of the project commenced on schedule and so far about one-third of the tasks planned for the next eight months (as from January 2004) have been completed. These tasks comprised mainly of meetings and completion of data collection and data analysis. The activities for this phase kicked off with a Regional Research Group Meeting in Uganda in January 2004. This meeting sought to review and assess project activities. Apart from this a participatory meetings with the local communities have taken place in Kenya. Researchers on the project also participated and presented papers at the AIACC Annual Regional Workshop held in Dakar, Senegal and the African Network for the Chemical Analysis of Pesticides Workshop in Kampala, Uganda. The latter was a regional workshop on malaria eradication. The project has also prepared an outline of a synthesis paper and another one for journal publication.

Qualitative data collection (focus group discussions and key informant interviews) have now been completed for Uganda and Tanzania. Also completed is hydrological data collection.

The statistical downscaling of climate models has been done. At the same time a statistical analysis for the time series data for estimating the probability distribution functions for temperatures and precipitation for each decade (baseline variability for 1960-70, 1970-80, 1980-90, 1990-2000) has been run. The downscaling and assigning of probabilities to the outputs of the climate models and scenarios of changes in extreme events (the El-Niño years and La Niña years) has been completed. Further, all the critical indicators (precipitation, temperature, cholera incidences, malaria incidences) available to help identify vulnerability and adaptation measures for creating scenarios have been assembled. The validation and

1