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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: HealthEnvironmentalSocial 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 40Longitude (°E)
12
10
8
6
4
2
Latit
ude
(°S)
Bukoba
ShinyangaArusha
Tanga
NIAMorogoroDodoma
Tabora
Mbeya
Songea
Kilwa
Mtwara
Kigoma
Figure 1
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
00.20.40.60.8
11.21.41.6
Jan
Feb
Mar
Apr
May Jun
Jul
Aug
Sep
Oct
Nov
Dec
Months
NDVI
Mean NDVI
DIA MEAN NDVI 1982-2005
00.20.40.60.8
11.21.4
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
NDVI
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
NDVI
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.
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
0.006-++-MTWARA
0.063-++-SONGEA
0.019+++-KILWA
0.018-+--MBEYA
0.009-++-DIA
0.013++--MOROGORO
0.015----DODOMA
0.645-+--TABORA
0.034-+++TANGA
0.243-+++KIGOMA
0.038----ARUSHA
0.365-+--SHINYANGA
0.089--+-BUKOBA
SON R2SONJJAMAMDJFSTATION
Table 1
Tabora rainfall time seriesFigure 5
TABORA Avg DJF TIME SERIESy = 0.5122x + 158.11
R2 = 0.007
050
100150
200250300
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
Years
Rai
nfal
l (m
m)
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
nfal
l (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
nfal
l (m
m)
Avg JJA Linear (Avg JJA)
TABORA Avg SON y = -0.8333x + 55.896R2 = 0.0431
0
20
40
6080
100
120
140
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
Years
Rai
nfal
l (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 grazingForest fire & deliberate fire for clearing farmsOnly 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’
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 1980-2000y = 1.4544x + 337.13
320
330
340
350
360
370
380
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
YEARS
CO2
(PPM
)
CO2Linear (CO2)3 per. Mov. Avg. (CO2)
SEASONAL CO2 MAM 1980-2000y = 1.4233x + 336.94
320
330
340
350
360
370
380
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
SEASONAL CO2 JJA 1980-2000
y = 1.4583x + 335.8
320
330
340
350
360
370
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
PPM
SEASONAL CO2 SON 1980-2000
y = 1.4801x + 336.34
320
330
340
350
360
370
380
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
PPM
Figure 7
(a) (b)
(c) (d)
SEASONAL TREND OF CO2 CONT’
Figure 3 shows thatOn 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
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
CO2
(ppm
)CO2Linear (CO2)3 per. Mov. Avg. (CO2)
Figure 8
BURNED / DROUGHT
BARE LAND DUE TO DROUGHT/BURNING
ERODED BARE LAND AFTERFLOODS IN DODOMA
Figure10
(a) (b)
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.Institute proper land use plans.
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