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Climate and Hydrological and Extremes in Lake Victoria Basin An Assessment of Vulnerability and Adaptation to Climate Variability and Change Impacts on Malaria and Health in the Lake Victoria Region in East Africa. Dr. Daniel Olago Department of Geology University of Nairobi Nairobi, Kenya. - PowerPoint PPT Presentation
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Climate and Hydrological and Extremes in Lake Victoria Basin
An Assessment of Vulnerability and Adaptation to Climate Variability and Change Impacts on Malaria and Health in the Lake Victoria Region in East Africa
Dr. Daniel Olago
Department of Geology
University of Nairobi
Nairobi, Kenya
Lake Victoria
BasinSITES
Kenya: Kericho (malaria) and Kisumu (cholera).
Tanzania: Bugarama village, Muleba District (malaria) and Chato Village, Biharamulo District (cholera).
Uganda: Kasese, southwest Uganda (Malaria) and Gaba, Kampala (cholera).
Objectives
1. To analyse climate variability in temperature and rainfall extremes in relation to reported and documented malaria and cholera outbreaks in order to establish the coupling sensitivities and critical climate thresholds.
Data and Data Sources Climate data Climate data (rainfall and temperature) covering
the period 1961 to the present has been collected from the various site-related meteorological stations in the Lake Victoria basin.
Water resources data Long-term data (1961-to the present) on river
discharge has been obtained from Water Ministries and Meteorological Agencies.
Climate Data Preliminary Analysis:
Meteorological data (temperature, rainfall, evaporation) has been obtained for all six sites from the Drought Monitoring Centre, Nairobi (DMCN)
Below we outline the results obtained from Kenya, Uganda and Tanzania sites.
Climate Data Preliminary Analysis:
The stations’ locations have been laid over the digital elevation model for the region (resolution: 1km), the darker the brown shade, the higher the elevation. The elevations for the two main Meteorological stations are 1146m for Kisumu and 2148m for Kericho.
Climate Data Preliminary Analysis:Mean Monthly Rainfall
0
50
100
150
200
250
300
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Ra
infa
ll (m
m)
Kisumu Met
Kisumu NP
Kisumu Kibos-C
Kisumu Kibos-S
Kericho Kaisugu
Kericho Chagaik
Kericho Hail
Comparison of mean monthly rainfall patterns in the study regions. On average, the study regions in Kisumu and Kericho receive about 1400mm and 1940mm of annual rainfall respectively. In Kericho, there are about 7 months on average, within one year that receive more than 150mm of rainfall as compared to only about 3 months in Kisumu. This should be compared to the fact that a minimum rainfall of 150mm per month for 1 to 2 months is required to precipitate a malaria outbreak.
Temperature and Evaporation for KISUMU
10.0
15.0
20.0
25.0
30.0
35.0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Tem
per
atu
re (
oC
)
0.0
50.0
100.0
150.0
200.0
250.0
Rai
nfa
ll (m
m),
Eva
po
rati
on
(m
m)
Rainfall Kisumu TMax TMin Evaporation
Temperature and Evaporation for KERICHO
10.0
15.0
20.0
25.0
30.0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Tem
per
atu
re (
oC
)
0.0
50.0
100.0
150.0
200.0
250.0
300.0
Rai
nfa
ll (m
m),
Eva
po
rati
on
(m
m)
Rainfall TMax TMin Evaporation
Climate Data Preliminary Analysis:In Kisumu, the mean maximum temperature occurs in March and the mean minimum temperature occurs in July.
The evaporation does not vary much and almost equals the rainfall, except during the rainy seasons where the rainfall is much more than the evaporation.
In Kericho, the mean maximum temperature occurs in February and the mean minimum temperature occurs in September.
Here the rainfall is seen to exceed the evaporation by large amounts even during the non-raining seasons, unlike for Kisumu.
The pattern of humidity in both the study areas is a mirror image of evaporation. It ranges on average from 54% to 65% in Kisumu and 56% to 76% in Kericho.
Climate Data Preliminary Analysis: GCM ValidationThe gridded data set was obtained from the Climatic Data Research Unit (CRU) website http://www.cru.uea.ac.uk/cru/data/hrg.htm.
The baseline climatology is based on 1961-1990 and is averaged in 0.5 by 0.5 degree grid boxes.
The baseline climatology extracted from the website is labelled CRU CL1.0. In addition, interpolated GCM experiments to be used for validation of projections are on the same website.
The experiments used in this exercise are labelled TYN SC2.0. The experiments contain time series of projected climate over the same grid boxes.
Actual rainfall data for the period 1961-1990 was obtained from the Drought Monitoring Centre- Nairobi.
Nine stations around Lake Victoria were used in this exercise.
Climate Data Preliminary Analysis: GCM ValidationThe gridded data sets were extracted using a program that was generated in-house.
The gridded rainfall data sets are archived in units of mm/day*10. The extracted data sets were converted to mm.
The extraction was done for 9 grid boxes each of which represented one station.
Mean monthly rainfall was computed for the 1961-90 period for each of the stations using the actual time series of the station data.
Time series plots were drawn for each station in which the gridded monthly climatology was superimposed on the mean climatology from station data.
In addition, correlations and correlation coefficients were computed between the gridded climatology and the station climatology.
The correlation coefficients between the gridded data sets and the station climatology are all greater than 0.9.
Climate Data Preliminary Analysis: GCM Validation – Time Series Plots for Tanzania
BUKOBA
0.050.0
100.0150.0200.0250.0300.0350.0400.0
JAN
MAR
MAY JU
LSEP
NOV
MONTHS
ME
AN
MO
NT
HLY
R/F
AL
L
(mm
) GRIDDED DATA
STNT DATA
MWANZA
0.0
50.0
100.0
150.0
200.0
250.0
JAN
MAR
MAY
JUL
SEPNOV
MONTHS
ME
AN
MO
NT
HLY
R/F
AL
L
(mm
) GRIDDED DATA
STN DATA
MUSOMA
0.0
50.0
100.0
150.0
200.0
JAN
MAR
MAY
JUL
SEPNOV
MONTHS
ME
AN
MO
NT
HLY
R/F
AL
L
(mm
) GRIDDED DATA
STN DATA
The gridded data set compares very well with the station data for Bukoba
There is a tendency for overestimating the station climatology at Musoma in J, F, N, D
At Mwanza, the station climatology is overestimated during Jun, Aug, and underestimated in F, N, D
Climate Data Preliminary Analysis: GCM Validation – Time Series Plots for Kenya
At Kakamega and Kisii, the gridded data set underestimates the station climatology for most of the months. This underestimation is more apparent at Kisii with deviations of about 50 mm during some months.
At Kisumu, the gridded data set overestimates the station climatology during the relatively drier months of Jul to Sep and introduces a slight rainfall peak in August.
KAKAMEGA
0.0
50.0
100.0
150.0
200.0
250.0
300.0
JAN
MAR
MAY
JUL
SEPNOV
MONTHS
ME
AN
MO
NT
HLY
R/F
AL
L
(mm
) GRIDDED DATA
STN DATA
KISII
0.0
50.0
100.0
150.0
200.0
250.0
300.0
JAN
MAR
MAY
JUL
SEPNOV
MONTHSM
EA
N M
ON
TH
LY R
/FA
LL
(m
m) GRIDDED DATA
STN DATA
KISUMU
0.0
50.0
100.0
150.0
200.0
250.0
JAN
MAR
MAY
JUL
SEPNOV
MONTHS
ME
AN
MO
NT
HLY
R/F
AL
L
(mm
) GRIDDED DATA
STN DATA
Climate Data Preliminary Analysis: GCM Validation – Time Series Plots for Uganda
The gridded data set overestimates the station data during the long rains (March to May) season at Jinja and Kampala.
It underestimates the station climatology for Entebbe during the same season.
At Entebbe, the peak rainfall month during the long rains season is depicted as April when the station climatology shows May as the peak.
ENTEBBE
0.0
50.0
100.0
150.0
200.0
250.0
300.0
JAN
MAR
MAY
JUL
SEPNOV
MONTHS
ME
AN
MO
NT
HLY
R/F
AL
L
(mm
) GRIDDED DATA
STN DATA
JINJA
0.0
50.0
100.0
150.0
200.0
250.0
JAN
MAR
MAY
JUL
SEPNOV
MONTHSM
EA
N M
ON
TH
LY R
/FA
LL
(m
m) GRIDDED DATA
STN DATA
KAMPALA
0.0
50.0
100.0
150.0
200.0
250.0
JAN
MAR
MAY
JUL
SEPNOV
MONTHS
ME
AN
MO
NT
HLY
R/F
AL
L
(mm
) GRIDDED DATA
STN DATA
The differences during some months are close to 100mm.
Hydrology Data Preliminary Analysis:
Hydrological data has been obtained for Kenya sites (Sondu-Miriu and Awach River Basins).
Rivers in the Tanzania and Uganda sites are ungauaged.
Data gaps such as in river discharges were addressed with graphical or statistical (MOVE1) methods of estimation.
Some water quality data is also available and their usefulness in relation to elucidating aspects of water related health risks is being assessed.
Some examples of the datasets are given below.
Hydrology Data Preliminary Analysis:
0
10
20
30
40
50
60
70
80
90
100
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC
Month
Mea
n D
isch
arg
e (m
3/s)
Preliminary results for the Kericho area, Kenya, show that highest discharge rates in Sondu_Miriu River occur in the six months from April to September.
Peak river discharge lags two of the three observed rainfall peaks (April and August) by one month, but is coincident with the rainfall peak in November.
Hydrology Data: Spectral Analysis:The discharge data used in this case is in terms of months.
0
2
4
6
8
10
12
14
0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50
Frequency
Sondu Spectral Density
Two peaks predominate. A six months cycle (F=0.1667) is seen for the first peak.
The second peak is centred at F=0.0833 which is equivalent to a period of 12 months .
Hence seasonal and annual cycles are observed for the discharge over Sondu.
Hydrology Data: Spectral Analysis:
For the case of Awach, similar case like the one for Sondu is noticed where we have 6 month cycles(F=0.1667) and annual cycles(F=0.0833).
0
2
4
6
8
10
12
14
16
0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50
Frequency
Awach Spectral Density
Hydrology Data: Analogues
the years 1962, 1963,1964, 1968,1970, 1977 and 1978 were associated with high flows/floods.
the periods 1965, 1969,1972, 1976 and 1980 are associated with low flows/droughts.
S O N D U AN ALO G U E S
0.00
100.00
200.00
300.00
400.00
500.00
600.00
700.00
800.00
900.00
1000.00
Jan Feb M ar Apr M ay Jun Jul Aug Sep O ct N ov D ec
M O N TH S
CUMM
ULAT
IVE
DISC
HARG
EC umm. LT M
1961
1962
1963
1964
1965
1966
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
Hydrology Data: Analogues
the periods 1966,1968,1977 and 1978 were basically wet periods associated with high flows/floods
the periods 1970, 1976,1982, 1984,1986 were associated with low flows/droughts
AW AC H AN ALO G U E S
0.00
5.00
10.00
15.00
20.00
25.00
30.00
35.00
40.00
Jan Feb M ar Apr M ay Jun Jul Aug Sep O ct N ov D ec
MONTHS
CUM
MUL
ATIV
E DI
SCHA
RGE
C umm.LT M
1966
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
Hydrology Data: Flood Frequency Curve
Standard error increases at high return periods – reliability of these models decrease at high return periods.
Return period for maximum discharge for 1961-1981 is between 2 to 5 years.
Descriptive statistics for Sondu River
Mean 47.99Standard Error 2.81Median 33.88Standard Deviation 44.65Kurtosis 5.87Skewness 2.04Range 268.52Minimum 3.66Maximum 272.18Sum 12094.28Count 252.00
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
Some Problems Encountered
•GCM data: results from GCM simulations were to be used for validation of climate projections. Problem- downloading•GCMs – unsuitability for regional/local scale studies because of the course grid-size resolution. Need for downscaling.•Lack of high resolution data (DEM, landuse etc)•Scaling, for hydrology. The inputs have a much coarser resolution than the analysis size for the study area, for any meaningful results. •Lack of hydrological data for Tanzania and Uganda sites.