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Towards Precipitation Enhancement through Cloud seeding in Kenya

Presented by J.N Ngaina,Department of Meteorology,

University of Nairobi

IGAD water dialogue Forum– 8-10th December 2014, at the Kenyatta International Conference Centre (KICC)

Prof. N. J Muthama, Prof. J.M. Ininda, Dr. A.O Opere and Mr. B.K Mutai

Introduction Precipitation significantly influences quality of human life through

availability of fresh water

High precipitation variability (locally, seasonally, and possibly long-term)

implies that in some years, there is excess rainfall while in others, there is

rainfall deficit

Competition for fresh water resources from key sectors will lead to a sharp

increase in water stress.

Precipitation enhancement through cloud

seeding provides an alternative measure to

improve water supply and alleviate existing

and projected water scarcity due to climate

change

This study sought to assess potential of

enhancing rainfall through cloud seeding by

considering the likely presence of seedable

conditions

Materials and Methods Data

Rainfall, minimum & maximum temperature (1971-2011)

Aerosol transport and dispersion

MODIS Aerosol and Cloud (2001 to 2012 )

Methodology

Short –Cut Bartlett test

Rainfall anomaly index (RAI)

Time series Analysis (trend)

HYSPLIT backward trajectory analysis

Composites techniques

Correlation analysis

Results

Data Quality control Delineation of dry and wet years

0

0.5

1

1.5

2

2.5

LOD

WA

RM

AR

SA

BIT

GA

RIS

SA

ELD

OR

ET

KIS

UM

UN

AR

OK

DA

GO

RE

TI

MA

KIN

DU

VO

ILA

MU

MO

MB

AS

AC

OLC

HE

NY

AH

UR

UR

UK

AK

AM

EG

AN

AK

UR

UM

TW

AP

AK

ISII

WA

JIR

MA

ND

ER

AM

OY

ALE

TH

IKA

Sam

ple

var

ian

ce (

Rai

nfa

ll)

F critical F value

0

50

100

150

200

250

300

35019

71

1973

1975

1977

1979

198

1

198

3

198

5

198

7

198

9

199

1

199

3

199

5

199

7

199

9

200

1

200

3

200

5

200

7

200

9

2011

Rai

nfa

ll A

no

mal

y In

de

x (

%)

RAI

T-75

T-125

Scenario

type

Anomaly range

(%)

Frequency

(n = 40) Years

Dry ≤ 75 18

2005, 1974, 1998, 1975, 1987, 1980, 1985,

1996, 1976, 1973, 1981, 2010, 2011, 1993,

1983, 2007, 2003, 1979, 1971

Normal 75 ≤ ≤ 125 14

2001, 1991, 1988, 2000, 2008, 1999, 1995,

1990, 1992, 1990, 2004, 1978, 1984, 1972

Wet > 125 9

1997, 2006, 1977, 1982, 2002, 2009, 1994,

1989

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Space time distribution -Rainfall

Slope of regression line of precipitation in Kenya

station dry wet station dry wet

Lodwar -0.03 0.13 Lamu -0.35 -0.05

Marsabit -0.14 -0.14 Mombasa -0.17 -0.18

Garissa 0.34 -0.10 Kakamega -0.50 -0.07

Kisumu -0.04 0.15 Wajir -0.09 -0.04

Dagoretti -0.02 0.14 Malindi -0.68 -0.18

Makindu 0.21 0.15 Nyeri -0.22 -0.15

Voi 0.33 0.17

0.0

10.0

20.0

30.0

40.0

50.0

60.0

56 57 58 59 60 61

62

63

64

65

66 67

68

69 70 71 72 73

To

tal R

ain

fall

(m

m)

Pentad Number

Garissa

Kisumu

Dagoretti

b)

0.0

5.0

10.0

15.0

20.0

25.0

30.0

56 57 58 59 60 61

62

63

64

65

66 67

68

69 70 71 72 73

To

tal R

ain

fall

(m

m)

Pentad Number

Garissa

Kisumu

Dagoretti

a)

0100200300400500600700

Dag

ore

tti

Gar

issa

Kak

ame

ga

Kis

um

u

Lam

u

Lod

war

Mak

ind

u

Mal

ind

i

Mar

sab

it

Mo

mb

asa

Nye

ri

Vo

i

Rai

nfa

ll (

mm

)

Dry

Wet

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Space time distribution Aerosol Optical Depth

Slope of regression line for Aerosol Optical Depth in KenyaStation dry wet Station dry wet Station dry wet

Nyahururu 0.12 -0.005 Kisii 0.16 -0.001 Meru 0.16 0.000

Naivasha 0.22 -0.003 Kisumu 0.16 -0.001 Mombasa 0.21 0.001

Dagoretti 0.18 -0.007 Kitale 0.12 -0.002 Msabaha 0.19 0.005

Eldoret 0.12 -0.002 Lamu 0.24 0.003 Mtwapa 0.17 0.000

Embu 0.13 0.001 Lodwar 0.37 -0.002 Nakuru 0.12 -0.005

Garissa 0.22 -0.004 Makindu 0.17 -0.001 Nanyuki 0.16 0.000

Kabarak 0.12 -0.005 Malindi 0.19 0.005 Narok 0.14 -0.002

Kakamega 0.14 -0.001 Mandera 0.04 -0.003 Nyeri 0.13 0.001

Marsabit 0.27 -0.004 Moyale 0.10 -0.005 Voi 0.11 0.003

0

1

2

3

4

5

6

7

Nya

hu

ruru

Nai

vash

a

Dag

ore

tti

Eld

ore

t

Em

bu

Gar

issa

JKIA

Kab

arak

Kak

ame

ga

Kis

ii

Kis

um

u

Kit

ale

Lam

u

Lod

war

Mak

ind

u

Mal

ind

i

Man

de

ra

Mar

sab

it

Me

ru

Mo

mb

asa

Mo

yale

Msa

bah

a

Mtw

apa

Nak

uru

Nan

yuki

Nar

ok

Nye

ri

Vo

i

Ae

roso

l Op

tica

l De

pth

(U

nit

less

) Dry

Wet

Space time distribution Cloud Top Temperature

Slope of regression line for cloud top temperature in Kenya

Station dry wet Station dry wet Station dry wet

Nyahururu 0.32 0.33 Kakamega 0.78 0.75 Mandera 0.31 0.37

Naivasha 0.41 0.82 Kisii 1.18 0.67 Marsabit 0.16 0.68

Dagoretti 0.44 0.43 Kisumu 1.00 0.90 Meru 0.61 0.49

Eldoret 0.78 0.65 Kitale 0.32 0.47 Mombasa 0.39 -0.06

Embu 0.58 0.48 Lamu 0.65 0.52 Moyale 0.27 -0.12

Garissa 0.91 0.91 Lodwar -0.09 0.28 Msabaha 0.36 0.36

JKIA 0.82 0.76 Makindu -0.04 0.54 Mtwapa 0.81 0.91

Nanyuki 0.80 0.87 Voi 0.13 0.81 Malindi 0.38 0.88

Narok 0.82 0.63 Nyeri 0.51 0.80 Nakuru 0.89 0.36

-20

-15

-10

-5

0

5

10

15

Nya

hu

ruru

Nai

vash

a

Dag

ore

tti

Eld

ore

t

Em

bu

Gar

issa

JKIA

Kab

arak

Kak

ame

ga

Kis

ii

Kis

um

u

Kit

ale

Lam

u

Lod

war

Mak

ind

u

Mal

ind

i

Man

de

ra

Mar

sab

it

Me

ru

Mo

mb

asa

Mo

yale

Msa

bah

a

Mtw

apa

Nak

uru

Nan

yuki

Nar

ok

Nye

ri

Vo

iClo

ud

To

p T

em

pe

ratu

re (

OC

)

Dry Wet

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Space time distribution Winds

Wind patterns at 700hPa during a) dry and b) wet years during OND season

a) b)

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HYSPLIT backward trajectory analysis

Trajectory analysis for selected locations during a) dry 2005 b) wet 2006 years

a) b)

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Relationship between atmospheric aerosols and rainfall

AOD/ Precipitation r (dry Year) t computed t tabulated r (wet Year) t computed t tabulated

Dagoretti 0.09 0.37 2.12 -0.38 -1.63 2.12

Garissa 0.27 1.10 2.12 -0.24 -0.98 2.12

Kakamega 0.38 1.65 2.12 0.71 4.08 2.12

Kisumu 0.08 0.32 2.12 0.24 0.99 2.12

Lamu -0.56 -2.67 2.12 0.08 0.34 2.12

Lodwar 0.66 3.47 2.12 0.45 2.02 2.12

Makindu -0.12 -0.50 2.12 0.34 1.43 2.12

Malindi -0.47 -2.13 2.12 -0.47 -2.13 2.12

Marsabit 0.40 1.75 2.12 0.36 1.53 2.12

Mombasa -0.64 -3.31 2.12 -0.17 -0.69 2.12

Nyeri 0.49 2.25 2.12 -0.30 -1.26 2.12

Voi -0.05 -0.20 2.12 0.50 2.34 2.12

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Decreasing trend of rainfall over most stations willnecessitate an alternative to enhance rainfall.

Aerosols indicated potential presence of seedablecondition especially during dry years when expectedrainfall would be minimum.

It is necessary to comprehensively address the risks,Benefits and Ethical issues in weather modification

And develop policy approaches that can help theimplementation and conduct of future experiments andoperations

Efforts to improve planned modification is necessary

Conclusion

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THANK YOU

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