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

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

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.

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

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’

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 1980-2000y = 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

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 GRAZINGFigure12

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 500hPaKILIMANJARO(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.Institute proper land use plans.

THANK YOU