SEASONAL VARIATION OF CARBON DIOXIDE, RAINFALL,NDVI AND IT’S ASSOCIATION TO LAND DEGRADATION IN TANZANIA Juliana J. Adosi Tanzania Meteorological Agency P.O.Box 3056 Dar Es Salaam Tanzania. INTERNATIONAL WORKSHOP ON CLIMATE AND LAND DEGRADATION ARUSHA TANZANIA 11-15/12/2006
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SEASONAL VARIATION OF CARBON DIOXIDE, RAINFALL,NDVI AND ITS ASSOCIATION TO LAND DEGRADATION IN TANZANIA Juliana J. Adosi Tanzania Meteorological Agency.
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SEASONAL VARIATION OF CARBON DIOXIDE, RAINFALL,NDVI AND IT’S
ASSOCIATION TO LAND DEGRADATION IN TANZANIA
Juliana J. AdosiTanzania Meteorological Agency
P.O.Box 3056Dar Es Salaam
Tanzania.
INTERNATIONAL WORKSHOP ON CLIMATE AND LAND DEGRADATION
ARUSHA TANZANIA 11-15/12/2006
ACKNOWLEGEMENT
Lonnie Thompson, University of Ohio, USA Douglas Handy, University of Massachusetts, USA Dave in USA
Georg Kaser, Thomas MÖlg, Nicolas Cullen, University of Innsbruck, Austria
Blessing Siwale, DMC Harare
Dr. Mohamed Mhita, Emanuel Mpeta, Tharsis Hyera: TMA Tanzania
1.0 INTRODUCTION
Over the recent decades many parts of the world have experienced high frequency of extreme weather events such as floods and droughts which have been linked to climate change.
The main indicator of climate change has been the rise in global temperature which has principally been linked with increase of carbon dioxide in the atmosphere. Instrumental records indicate an increase of ≈0.3-0.6°C in global mean surface temperature over the last 100 years (IPCC, 1992; 1995; 2001).
INTRODUCTION CONT’
A study on the space time characteristics of minimum and maximum temperatures over Tropical East Africa indicate positive trend (Ogallo, 1993; King’uyu, 1994); Scenario of Climate Change (Matari and Hyera, 1996); Trends and Variability of Surface Temperature Over Tanzania, (Adosi, 2002);
Rise in global temperature will cause the melting of glaciers and polar ice caps which may lead to sea level rise resulting to problems like:
Health Environmental Social and economic.
1.1 OBJECTIVE
The main objective of the study is to examine the seasonal variation of CO2, rainfall, NDVI and it’s association to land degradation in Tanzania. To archive this the following is going to be done:
Find annual and seasonal variation of CO2. Find time series and seasonal variation of
NDVI and rainfall. Relate the above to their association with
land degradation.
TANZANIA STATION DISTRIBUTION FOR RAINFALL AND NDVI
28 30 32 34 36 38 40
Longitude (°E )
12
10
8
6
4
2L
atitu
de (
°S)
Bukoba
ShinyangaArusha
Tanga
N IAM orogoro
D odom a
Tabora
M beya
Songea
K ilw a
M tw ara
K igom a
Figure 1
Figure 2
2.0 DATA AND METHODOLOGY
Mean monthly data for CO2 for Mahe (Seychelles) carbon dioxide baseline station, NDVI from DMC Harare, rainfall from TMA were used in this study
The data was subjected to standard statistical methods of time series analysis, which included trend and spectral analysis on monthly, seasonal and annual time scale.
3.0 RESULTS AND DISCUSSION
MONTHLY NDVI 1982-2005.
ARUSHA MEAN NDVI 1982-2005
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Months
ND
VI
Mean NDVI
DIA MEAN NDVI 1982-2005
0
0.2
0.4
0.60.8
1
1.2
1.4
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Months
ND
VI
Mean NDVI
TABORA MEAN NDVI 1982-2005
0
0.5
1
1.5
2
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Months
ND
VI
Mean NDVI
MTWARA MEAN NDVI 1982-2005
0
0.5
1
1.5
2
2.5
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Months
ND
VI
Mean NDVI
(a) (b)
(C) (d)
Figure 3
MONTHLY NDVI CONT’
From figure 3 the NDVI shows high values in May / December when the land has maximum foliage cover after the long / short rains in areas with bimodal rainfall distribution ( Musoma, Bukoba, Arusha, Tanga and Dar es Salaam).
In areas with unimodal rainfall distribution the high values in NDVI are observed in March/April (Kigoma, Tabora, Dodoma, Morogoro,Songea) the period of maximum foliage cover.
TABORA SEASONAL NDVI TIME SERIES 1982-2005.
Figure 4
(a) (b)
(c) (d)
Seasonal analysis of NDVI in the country shows that the vegetation index decreases and increase in some seasons.
A significant decrease has been noted in Tabora SON with R2≈0.6446, figure 4.
Arusha and Dodoma has negative trend in all seasons, table 1. This signifies a decrease of rainfall or deforestation. Grazing is dominant in these regions. With a reduction of vegetation the consumption of CO2 is reduced, hence accumulation in the atmosphere.
TABORA SEASONAL NDVI CONT’
SEASONAL NDVI TREND CHARACTERISTIC IN TANZANIA 1982-2005
STATION DJF MAM JJA SON SON R2
BUKOBA - + - - 0.089
SHINYANGA - - + - 0.365
ARUSHA - - - - 0.038
KIGOMA + + + - 0.243
TANGA + + + - 0.034
TABORA - - + - 0.645
DODOMA - - - - 0.015
MOROGORO - - + + 0.013
DIA - + + - 0.009
MBEYA - - + - 0.018
KILWA - + + + 0.019
SONGEA - + + - 0.063
MTWARA - + + - 0.006
Table 1
Tabora rainfall time series Figure 5
TABORA Avg DJF TIME SERIESy = 0.5122x + 158.11
R2 = 0.007
0
50
100
150
200
250
30019
81
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
Years
Rain
fall
(mm
)
Avg DJF Linear (Avg DJF)
TABORA Avg MAM RAINFALL y = 0.0846x + 102.38R2 = 0.0003
0
50
100
150
200
250
300
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
Years
Rai
nfa
ll (m
m)
Avg MAM Linear (Avg MAM)
TABORA Avg JJA RAINFALLy = 0.0509x + 0.0413
R2 = 0.0383
0
2
4
6
8
10
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
Years
Rai
nfa
ll (m
m)
Avg JJA Linear (Avg JJA)
TABORA Avg SON y = -0.8333x + 55.896R2 = 0.0431
0
20
40
60
80
100
120
140
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
Years
Rai
nfa
ll (m
m)
Avg SON Linear (Avg SON)
Increasing trend in DJF, MAM, JJA but decreasing NDVI in SON
(a) (b)
(c) (d)
Rainfall time series shows that rainfall is increasing in some parts of the country while the NDVI is decreasing (Shinyanga, Morogoro, Mbeya, Tabora) may be due to:
deforestation going on in the area. Over grazing Forest fire & deliberate fire for clearing farms Only Songea has positive trend in NDVI and
rainfall in MAM and JJA. Tanga and Kigoma has a decrease in rainfall but
an increase in NDVI in DJF, MAM and JJA table 1 and 2.
Tabora rainfall time series Cont’
RAINFALL TREND SIGN.
Table 2
3.0 Monthly concentration of CO2
bimodal distribution is
evident with minimum in January and May the time when we have high values in NDVI. This pattern may be associated with seasonal variation in the vegetation cover through (photosynthesis).
CO2MONTLY MEAN 1979-2000
350
351
352
353
354
J F M A M J J A S O N D
CO2
Figure 6
3.2 SEASONAL TREND OF CO2
SEASONAL CO2 DJF 1980-2000
y = 1.4544x + 337.13
320
330
340
350
360
370
380
19
80
19
82
19
84
19
86
19
88
19
90
19
92
19
94
19
96
19
98
20
00
YEARS
CO
2 (
PP
M)
CO2Linear (CO2)3 per. Mov. Avg. (CO2)
SEASONAL CO2 MAM 1980-2000
y = 1.4233x + 336.94
320
330
340
350
360
370
380
198
0
198
2
198
4
198
6
198
8
199
0
199
2
199
4
199
6
199
8
200
0
SEASONAL CO2 JJA 1980-2000
y = 1.4583x + 335.8
320
330
340
350
360
370
19
80
19
82
19
84
19
86
19
88
19
90
19
92
19
94
19
96
19
98
20
00
PP
M
SEASONAL CO2 SON 1980-2000
y = 1.4801x + 336.34
320
330
340
350
360
370
380
198
0
198
2
198
4
198
6
198
8
199
0
199
2
199
4
199
6
199
8
200
0
PP
M
Figure 7
(a) (b)
(c) (d)
SEASONAL TREND OF CO2 CONT’
Figure 3 shows that On seasonal basis : (SON) has the largest trend ≈ (1.48) MAM has the lowest trend ≈ (1.42) This can be related with the vegetation
cover, SON the land is bare, forest fire and most of the people prepare their farms by burning. While in MAM the land has maximum foliage cover.
3.3 CO2 ANNUAL VARIATION.
CO2 is increasing at ≈ (1.42) ppm per annum.
MAHE CO2 1980-2000
y = 1.4209x + 337.14
320
330
340
350
360
370
380
19
80
19
82
19
84
19
86
19
88
19
90
19
92
19
94
19
96
19
98
20
00
CO
2 (
pp
m)
CO2Linear (CO2)3 per. Mov. Avg. (CO2)
Figure 8
FIRE: FOREST & FARM CLEARING IN MOROGORO
Figure 9
(a) (b)
BURNED / DROUGHT
BARE LAND DUE TO DROUGHT/BURNING
ERODED BARE LAND AFTERFLOODS IN DODOMA
Figure10
(a) (b)
DROUGHT IN TZ 2005Figure11
OVER GRAZING
Figure12
FLOODS AND DROUGHTS
FLOODS DAR ES SALAAM 2006DROUGHT DAR ES SALAAM 2005
Figure13
KILIMANJARO’S FOREST
THICK FOREST MOORLAND
Figure14
(a) (b)
KILIMANJARO FIRE
FOREST RECOVERINGLAND VULNERABLE TO DEGRADATION
Figure 15
(a) (b)
KILIMANJARO GLACIER IN DANGERFigure 16
17/2/1993 21/2/2000
Mt. KILIMANJARO GLACIER
Figure 17
(a) (b)
KILIMANJARO CRATER AERIAL VIEW
EARLIER
CURRENT (JULY 2005)
CRATER
Figure 18
(a)(b)
LAND DEGRADATION AT 500hPa KILIMANJARO(5895m)
20-30m
Figure 19
(a) (b)
40-50m
Overview of Met. station locations:
Northern Icefield (NFI) station
Vertical wall station
Slope glacier station
LAND DEGRADATION AT 500 hPa CONT’
NIFEIF
Crater
Figure 20
(a)
(b)
(SIF)
CONCLUSION
This study shows that: An increase of CO2 will cause increased
land degradation due to increase in frequency and intensity of severe weather and extreme climatic events (floods & droughts)
Increased land degradation will lead to:-Reduced retention of soil moisture-Increased soil erosion
5.0 RECOMMENDATIONS.
To reduce land degradation planting of trees to capture desertification is recommended.
New and affordable energy source should be sought to reduce distraction of forests.