REPORT ON SOWING PERIOD AND FLOODING …sns97aal/eSIAC/Cameroon_risk...1 REPORT ON SOWING PERIOD AND FLOODING RISKS IN DOUALA, CAMEROON BY GERVAIS DIDIER YONTCHANG Cameroon Meteorological

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REPORT ON SOWING PERIOD AND FLOODING

RISKS IN DOUALA, CAMEROON

BY

GERVAIS DIDIER YONTCHANG

Cameroon Meteorological Department

PROJECT WORK FOR PART FULFILMENT OF THE e-SIAC COURSE

(STATISTICS IN APPLIED CLIMATOLOGY APRIL – AUGUST 2010)

MAINTAINED BY THE MET. OFFICE COLLEGE AND THE SSC WITH SUPPORT

FROM THE UK MET. OFFICE AND WMO.

August 2010

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TABLE OF CONTENTS

REPORT ON SOWING PERIOD AND FLOODING RISKS IN DOUALA,

CAMEROON.......................................................................................................................... 1

TABLE OF CONTENTS ........................................................................................................... 2

A. BACKGROUND: .......................................................................................................... 3

1). Extreme climate events increasingly important for Cameroon .................................... 3

2) Location and climate of Douala, Cameroon in Africa ................................................... 3

B. OBJECTIVES: .................................................................................................................. 4

i. Assessment of the annual distribution of sowing dates ............................................. 4

ii. Assessment of the flooding risks during the rainfall peak period .............................. 4

iii. To quantify the risk of having floods with crop failure ......................................... 4

iv. Towards development of a possible „cautious strategy‟ ........................................ 4

C. METHODOLOGY: ........................................................................................................... 5

1. Find the dates when sowing are possible using the suitable definitions ........................ 5

2. Assessment of the risk of flooding with crop failure ..................................................... 5

3. Boxplots for visual comparison, descriptive statistics as dates and Comment on the

summaries. ......................................................................................................................... 5

4. Some conclusions for the client (Ministry of agriculture) based on these summaries

and risks calculations. ........................................................................................................ 5

D. DATA AND DEFINITIONS USED: ............................................................................... 6

1. Daily rainfall data for the period of 1950 to 2006 collected at the Douala

international Airport........................................................................................................... 6

2. DATA reformatted using SSC-Stat tool before it could be imported from EXCEL to

INSTAT ............................................................................................................................. 6

E. RESULTS: ...........................................................................................................................

1) - Find the dates when sowing and flooding is possible using suitable definitions: ....... 7

2. Assessment of the risk of flooding with crop failure: .................................................... 8

3. RISK OF FLOODING WITH CROP FAILURE .......................................................... 9

4. Text boxes showing the chance (proportion) of experiencing floods and crop failure

during the month of August: ............................................................................................ 13

5. Boxplots for visual comparison, descriptive statistics as dates and Comment on the

summaries: ....................................................................................................................... 16

F. IMPLICATIONS: ............................................................................................................ 21

1. The “sowing strategy” definition that I would recommend to a farmer ...................... 21

2. The advantages............................................................................................................. 21

3. The disadvantages ........................................................................................................ 21

G.APPENDICES: ................................................................................................................ 22

Appendix A: References and sources documentation ..................................................... 22

Appendix: Daily data for Douala, Cameroon 1950-2006 ................................................ 27

Day numbers in the year ...................................................................................................... 29

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BACKGROUND:

1). Extreme climate events increasingly important for Cameroon

Climate extremes such as droughts and floods have increased in frequency and severity in

Africa over the past 30 years (Nicholson, 1987). The probability of extreme climate events is

becoming increasingly important for Cameroon because of the inherent vulnerability of its

agricultural system and even the associated risk of the potential spread of plant, animal as

well as human diseases (malaria, typhoid, cholera, etc.) that are highly linked to climate

variability and climate change.

2) Location and climate of Douala, Cameroon in Africa

Cameroon lies within latitudes 1°45΄N to 13°΄N and Longitude 8°25΄E to 16°28΄E. It is

situated on the west coast of Africa, running north to south from Sahara desert to the Atlantic

Ocean. The country is bounded on the north by Lake Chad; on the east by Chad and the

Central African Republic; on the south by the Republic of the Congo, Gabon, and Equatorial

Guinea; and on the west by the Bight of Biafra (an arm of the Atlantic Ocean) and Nigeria

(Figure 1). The country is shaped like an elongated triangle, and forms a bridge between West

Africa and Central Africa. Cameroon has a total area of 475,442 km2. Yaoundé is the capital,

and Douala is the largest city. The major industries (petroleum refineries, fishing industries,

international air transports and others) are located in this region.

Douala city is situated at the junction of the southern coastal plain region with dense

equatorial rain forests and the west area of high forested mountains of volcanic origin, where

lies Mount Cameroon Mountain (4095m), the highest peak in western Africa and an active

volcano. One of the country‟s most fertile soils is found in this region. July and August are

seen to be the peak of the rainy season, with a lowest total of 207.4mm in July 2003 and one

year (August1966) with over 1240mm. The rains are usually from January to December,

though December, January and February are occasionally dry. The mean maximum relative

humidity occurs here due to its proximity to Atlantic Ocean (exposure to the warm Guinean

Gulf‟s currents).

Here, the type and intensity of the weather is determined by the meridional variations in the

depth of the monsoon layer (moisture). The southwest (SW) monsoon flow at lower levels,

source of humidity to a large extent determined by the large scale atmospheric circulation and

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sea-air interaction over much of the tropical Atlantic sector.(Lamb,1983). Douala Cameroon

(about 4ºN) is well usually marked by a zone with deep active and convective clouds,

thunderstorms and squall lines (SW winds) and heavy precipitations. The African Waves are

often associated with Squall lines that form, develop and dissipate within the waves.

Figure 1: Location of Douala, Cameroon in Africa

B. OBJECTIVES:

The main objective of the study is to investigate the sowing period variability and flooding

risks in the city of Douala. These will be achieved by addressing the following specific

objectives:

i. Assessment of the annual distribution of sowing dates;

ii. Assessment of the flooding risks during the rainfall peak period;

iii. To quantify the risk of having floods with crop failure for each week in the month of

August, where 100mm of rain or more in a day has caused flooding and crop

destruction. Again we compute the number of years in the time series when at exactly

one day in the week in question has registered 100mm or more of rain. If the event

was observed at least once, then we define that a week experiences a crop failure.

Week 1 is from day number 212 to day number 218; Week 2 is from day number 219

to day number 225; Week 3 is from day number 226 to day number 232; Week 4 is

from day number 233 to day number 239.

iv. Towards development of a possible „cautious strategy‟.

5

C. METHODOLOGY:

The methods used to achieve the objectives mentioned above are the following:

All these results describe the rainfall data for Douala, Cameroon and were computed (plots,

graphs, tables, figures, risks or variability calculations) using Instat+ v3.36 and Excel

packages. To define the events of interest (start of the season, dry spell) the climatic>>Events

menu options from INSTAT were used. Then the sowing dates were extracted and analyzed

based on the below definitions.

1. Find the dates when sowing are possible using the suitable definitions:

a)- First date from 1st March getting more than 40mm in 1,2,3 or 4 days;

b)- First date from 1st April getting more than 40mm in 1,2 or 3 days;

c) – First date from 1st May getting more than 40mm in 1 or 2 days;

d) – First date from 1st June getting more than 40mm in 1 day

2. Assessment of the risk of flooding with crop failure

e) – Fist occasion after 1st August with more than 100mm totalled over 2 consecutive days

and no dry spell of 3 days or more in the next 30 days. If the event was observed at least once

we define that a year experiences a flood in low-land areas.

f) – First occasion after 1st August with more than 100mm totalled exactly over one day. If

the event was observed at least once we define that a year experiences a general flood with

crop failure.

g) – The average rainfall total for August will be compared with the average totals for the

other months to get a general idea as to how wet the month is.

Then the month will then be broken down in 4 weeks as specified above, where the average

total for each week will be looked at and the highest rainfall totals will be highlighted.

After this, a count of the number of days with at least 100mm in exactly 1 day will be looked

at for each week, then find the average number of times per week each of these occurred over

the past 57 years. This will give the probability of the event of flooding, and therefore the

level of risk for encountering crop failure in either week 1, 2, 3 or 4 will be estimated.

3. Boxplots for visual comparison, descriptive statistics as dates and Comment

on the summaries.

4. Some conclusions for the client (Ministry of agriculture) based on these

summaries and risks calculations.

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D. DATA AND DEFINITIONS USED:

1. Daily rainfall data for the period of 1950 to 2006 collected at the Douala

international Airport

Daily rainfall data for the period of 1950 to 2006 collected at the Douala international

Airport which was provided by the Cameroon meteorological service. The accumulated

number of years of daily data is 57, because almost all the data was available. A rainy day in

this instance is considered to be a day with at least 0.85mm of rainfall. The data described are

the daily rainfall values from 1950 to 2006. The mean annual total rainfall over this period

was 3895mm with an average of about 220 rainy days. The highest annual total was

5327.6mm in 1956; the lowest total recorded was 2595.9mm in 1984.

The daily rainfall data was originally provided in EXCEL format by the Meteorological

office. It was then examined to ensure its quality. A thorough inspection was done in an effort

to clean the data from any erroneous values whenever necessary. There where very few

missing data and no negative values. The measurement units‟ millimeters were consistently

used throughout the dataset. The data had to be reformatted.

2. DATA reformatted using SSC-Stat tool before it could be imported from EXCEL to

INSTAT

DATA was reformatted using SSC-Stat tool before it could be imported from EXCEL to

INSTAT where supplementary checks were conducted to ensure adequate coding for non-

leap years and proper calendar year settings. The coding used fro non leap years was 9988

represented as ***.

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E. RESULTS:

These results describe the rainfall data for Douala, Cameroon. This section gives all the

results (plots, graphs, tables, figures, risks or variability calculations, using Instat+ v3.36). It

is also the space for “analysis of the events”. To define the events of interest (start of the

season, dry spell) the climatic>>Events menu options from INSTAT were used. Then the

sowing dates were extracted and analyzed based on the given definitions.

1) - Find the dates when sowing and flooding is possible using suitable definitions:

Table 1: Sowing & flooding dates YEAR 1st

Mar

1st

Apr

1st

May

1st

Jun

1st

Aug

floods 1st

Mar

1st

Apr

1st

May

1st

Jun

1st

Aug

flood

s 1950 5-Mar 3-Apr 24-May 3-Jun 22-Aug 10-Sep 65 94 145 155 235 254

1951 23-Apr 23-Apr 4-May 8-Jun 1-Aug 1-Sep 114 114 125 160 214 245

1952 11-Mar 14-Apr 4-May 1-Jun 14-Aug 14-Aug 71 105 125 153 227 227

1953 24-Mar 29-Apr 1-May 7-Jun 21-Aug 18-Sep 84 120 122 159 234 262

1954 5-Mar 13-Apr 12-May 7-Jun 4-Sep 12-Sep 65 104 133 159 248 256









1963 5-Apr 5-Apr 7-May 4-Jun 14-Aug 14-Aug 96 96 128 156 227 227



1966 7-Mar 1-Apr 5-Jun 5-Jun 4-Aug 23-Aug 67 92 157 157 217 236










1976 2-Apr 2-Apr 7-May 5-Jun 1-Aug - 93 93 128 157 214 0




1980 17-Mar ** 11-May 2-Jun 5-Sep 6-Sep 77 *** 132 154 249 250



1983 28-Mar 1-Apr 9-May 22-Jun 2-Aug - 88 92 130 174 215 0



1986 29-Mar 2-Apr 3-May 4-Jun 11-Aug 3-Nov 89 93 124 156 224 308




1990 24-Apr 24-Apr 3-May 2-Jun 9-Aug 25-Aug 115 115 124 154 222 238 1991 8-Apr 8-Apr 1-May 21-Jun 5-Aug - 99 99 122 173 218 0

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1992 8-Mar 14-Apr 6-May 13-Jun 5-Oct 5-Oct 68 105 127 165 279 279









2001 1-Mar 2-Apr ** 3-Jun 20-Sep 23-Sep 61 93 *** 155 264 267



2004 25-Mar 4-Apr 3-May 9-Jun 0-Jan - 85 95 124 161 0 0



Earliest 1-Mar 1-Apr 1-May 1-Jun 1-Aug 1st-Aug 61 92 122 153 214 214

Latest 24-Apr 29-Apr 5-Jun 29-Jun 5-Oct 3-Nov 115 120 157 181 279 308

2. Assessment of the risk of flooding with crop failure:

Graph of the Average monthly rainfall for years 1950 - 2006 at Douala International

Airport

0

200

400

600

800

1000

1200

1400

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Month

Rain

fall (

mm

)

Average minimum monthly rainfall

Average monthly rainfall

Average maximum monthly rainfall

Graph (i): Average monthly rainfall for years 1950- 2006 (Douala)

According to graph (i) (see also table1, page 25 of appendix), August is the wettest month of the year

with an average of 739.5 mm over 57 year period.

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Graph (ii): Total rainfall in August for years 1950- 2006 (Douala Airport)

Graph (ii) shows the yearly totals of rainfall for the month of August throughout the years.

The year with the highest totals was 1966 with 1240 mm and the year with the lowest total

was 1954 with a mere 248.3 mm of rainfall.

3. RISK OF FLOODING WITH CROP FAILURE

Table 3: showing the yearly totals of rainfall within the month of August

Year Week 1 Week 2 Week 3 Week 4

1950 244,4 81,1 135,7 231,3

1951 240,8 132,1 82,9 117,5

1952 92,7 94,4 292,4 188,9

1953 125 115,9 121 399

1954 58,9 40,6 31,5 77,1

1955 121,1 271,8 130,9 125,2

1956 154,4 252 273,4 37,8

1957 318,8 206,8 185,7 271,5

1958 161,7 243,7 127,7 134,6

1959 320,6 262,4 195,7 220,8

1960 211,5 508,5 292 77,5

1961 23,4 270,3 334,9 197,9

1962 363,9 217,5 168,4 152,2

1963 227,2 117,5 209 145,6

1964 38,3 32,3 740 52,2

1965 286,5 155,1 317,6 210,3

1966 301,5 229,2 290,5 444,4

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1967 97,3 175,8 198,8 369,9

1968 26,4 126,9 65,5 58,2

1969 95,5 144,8 189,8 193,9

1970 187,7 170,4 198,4 140

1971 452,2 211,7 138,7 239,2

1972 160,9 296,5 167,9 66,4

1973 187,4 232 109,4 137,6

1974 170,2 67,7 497,9 140,3

1975 70,3 82,7 134,6 211,3

1976 139 162,3 74 110,8

1977 92,5 257,8 26,1 416

1978 219,2 143,6 268,2 189,8

1979 189,6 97,6 126,7 92,1

1980 136,4 91,7 82,3 106,7

1981 242,9 132,3 219,2 285,3

1982 156,8 73,7 96,8 267,3

1983 152,8 84,4 60,4 104,5

1984 117,8 177,6 94,9 50,9

1985 164,8 125,4 83 201,7

1986 100 194,1 42,6 133

1987 50,4 90,3 310,9 323,5

1988 84 95 59,9 259,3

1989 136,7 213,9 352,4 200,3

1990 205,9 226,2 127,3 211,8

1991 305,4 131,6 206,6 199,1

1992 129,1 49,9 59,2 72,8

1993 225,5 149,9 133,7 174,8

1994 129 240,3 248,1 117,6

1995 188,3 105,3 171,6 126,1

1996 102,4 58,6 272,4 64,3

1997 340,6 124,1 195 37,1

1998 94,4 47 209,8 170,3

1999 227,4 42,3 61,4 110

2000 205,7 72,4 265 291,2

2001 143 151,5 83,6 55,1

2002 253,8 189,5 252,2 159,1

2003 175,1 161,8 281,3 165

2004 1,5 55,1 57,1 43,9

2005 179,5 134,7 125,8 200,6

2006 348,9 21,2 288,1 161,6

Total 9977 8640,8 10565,9 9742,2

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Graph (iii): Yearly totals rainfall in August for week 1, 2, 3 & 4

Table (3) and graph (iii) show that week 2 and 4 are those with the least amount of rainfall

while week 3 and 1 are the wettest.

Assessment of the weekly risk of flooding with crop failure:

To quantify the risk of experiencing a flood for each week in the month of August, when

100mm or more rain in exactly one day has occurred. We compute the number of years in the

time series when at least one day in the week in question has registered 100mm or more of

rain. If the event was observed at least once, then we define that a week experienced a flood.

TABLE 4: Showing the frequency of having 100 mm or more rainfall in a day (possible flooding

with crop failure) for weeks 1, 2, 3 & 4 for the month of August over the years.

Year WEEK-1 WEEK-2 WEEK-3 WEEK-4

1950 0 0 0 0

1951 0 0 0 0

1952 0 0 1 0

1953 0 0 0 0

1954 0 0 0 0

1955 0 0 0 0

1956 0 1 0 0

1957 1 0 0 1

1958 0 0 0 0

1959 1 1 1 0

1960 1 2 1 0

1961 0 0 1 0

1962 2 1 0 1

1963 1 0 1 0

1964 0 0 3 0

1965 0 0 1 1

1966 0 0 0 1

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1967 0 0 0 1

1968 0 0 0 0

1969 0 0 0 0

1970 0 0 0 0

1971 3 0 0 0

1972 0 2 0 0

1973 0 0 0 0

1974 0 0 3 0

1975 0 0 0 0

1976 0 0 0 0

1977 0 1 0 1

1978 0 0 1 0

1979 0 0 0 0

1980 0 0 0 0

1981 1 0 0 1

1982 0 0 0 0

1983 0 0 0 0

1984 0 1 0 0

1985 0 0 0 0

1986 0 0 0 0

1987 0 0 1 0

1988 0 0 0 1

1989 0 1 2 1

1990 0 0 0 1

1991 1 0 0 0

1992 0 0 0 0

1993 0 0 0 0

1994 0 1 0 0

1995 1 0 0 0

1996 0 0 0 0

1997 1 0 0 0

1998 0 0 1 0

1999 0 0 0 0

2000 0 0 1 1

2001 0 0 0 0

2002 1 0 1 0

2003 1 0 1 0

2004 0 0 0 0

2005 0 0 0 0

2006 0 0 1 0 Total non-

zero 12 9 16 11

The frequency of possible crop failure shown in table (iv) indicate that over the past 57 years, there

were 12 years when 100 mm or more fell in week 1, compared with 9 years in week 2, 16 years in

week 3 and 11 years in week 4.

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4. Text boxes showing the chance (proportion) of experiencing floods and crop failure

during the month of August:

Simple Models - Normal Distribution, One Sample

TINt 'Flooding' Normal model, one sample Column Flooding Sample size 57 Minimum 0 Maximum 308 Range 308 Mean 214 Std. deviation 76.272 Standard error of mean = 10.103 with 56 d.f. 95% confidence interval for mean 193.76 to 234.24

From our sample of 57 years, the true mean date of the start of threatening flooding ( from 1st August, 100mm or more of rain/ 1day) is estimated to be day 214(1.August) and is highly likely to be between day 193 (11.July) and day 234 (21.August). I am 95% confident that the margin error is ± 20 days (about 3 weeks).

WEEK 1 CHANCE OF CROP FAILURE

The 95% confidence interval for the true risk of flooding with crop failure for the first week in August is from 11.4% to 33.9%.

Estimated value (proportion) = 0.211 (21.1%) s.e. = 0.054 (5.4%) The risk of crop failure during week 1 of August is (12/57): 21.05% or about 21% with a

return period of (1/0.2105 = 4.75) about 5 years. I am 95% confident that the estimate of

21.1% is within ± 11.25% of the true risk of flooding with crop failure for the first week..

One proportion - binomial model

BINomial;stats 57 12;SIMple;EXAct

Binomial model, single sample

Sample size 57

Successes 12

Proportion 0.211

Approx s.e. of proportion = 0.054

Exact results:

95% confidence interval for prop. 0.114 to 0.339

Simple normal approximation:


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The 95% confidence interval for the true risk of flooding with crop failure for the second week in August is from 7.5% to 27.9%.

Estimated value (proportion) = 0.158 (15.8%) s.e. = 0.048 (4.8%) The risk of crop failure during week 2 of August is (9/57 = 0.1579): 15.79% or about 16%

with a return period of (1/0.1579 = 6.333) about 6.5 years. I am 95% confident that the

estimate of 15.8% is within ± 10.2% of the true risk of flooding with crop failure for the

second week.


The 95% confidence interval for the true risk of flooding with crop failure for the third week in August is from 17% to 41.5%.

Estimated value (proportion) = 0.281 (28.1%) s.e. = 0.06 (6%)




Sample size 57

Successes 9

Proportion 0.158


Exact results:







Sample size 57

Successes 16

Proportion 0.281


Exact results:




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The risk of crop failure during week 3 of August is (16/57 = 0.2807): 28.07% or about 28%

with a return period of (1/0.2807 = 3.56) about 3.5 years. I am 95% confident that the

estimate of 28.1% is within ± 12.25% of the true risk of flooding with crop failure for the third

week.


The 95% confidence interval for the true risk of flooding with crop failure for the fourth week in August is from 10% to 31.9%.

Estimated value (proportion) = 0.193 (19.3%) s.e. = 0.052 (5.2%)

The risk of crop failure during week 4 of August is (11/57 = 0.193): 19.3% or about 19%

with a return period of (1/0.193 = 5.2) about 5 years. I am 95% confident that the estimate

of 19.3% is within ±10.95% of the true risk of flooding with crop failure for the fourth week.

.5. Boxplots for visual comparison, descriptive statistics as dates and Comment on

the summaries:




Sample size 57

Successes 11

Proportion 0.193


Exact results:




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Boxplots for visual comparison:

Graph (iv): Boxplots showing the distribution of sowing and flooding dates ( day numbers) for

the given definitions:

From the above boxplots, it is obvious that the bold (1st Mar) distribution presents a median

date of sowing almost on day 76 (16.March). As a matter of fact ( boxplot stdr1Lo, based on

definition „e‟) the median occurrence of flooding in low lands is on day 221 (8.August).

Boxplot of flooding with crop failure based on definition ‘f’ shows that from 1st

August,100mm or more rainfall in exactly one day is highly likely to be between day 214

(1.August) and day 280 (6-October). From 6 years out of 57 years, the general risk of having

this event is 10.53%, which is a 9.5 years return period.

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Graph (v): Boxplots showing the distribution of rainfall in August for week 1, 2, 3 & 4

Graph (v) shows that week 3 and 1 have more variations in the rainfall totals for the week.

Comparing week 3 and 1, it shows that week 3 has more variation than week 1 with almost

the same amount of outliers.

Descriptive statistics as dates:

Table 5: Statistics summaries of sowing and flooding day numbers

Conditions missin

g

count Mean SDE Min 5% 25% 40% Media

n

60% 75% 95% Maximum

1st Mar 0 57 77.86 13 61 61 68 71 76 78 86.5 104.1 115

1st Apr 1 56 100.2 7.38 92 92 94 95.8 98.5 100.4 105 114.2 120

1st May 1 56 132.1 10.2 122 122 124 125.8 128.5 130.4 138.8 154.5 157

1st Jun 0 57 162.9 8.12 153 153 156 159 161 164.8 168.5 180.1 181

1st Aug 0 57 220.5 32.2 214 214 215.5 218 221 224 227.5 250.5 279

flooding 0 57 214 76.27 0 0 222.5 227 230 237 247 273.6 308

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Table 6: Statistics summaries of sowing and flooding dates

Conditions Minimum 25% Median 75% Maximum

1stMar(bold) 1.Mar 8.Mar 16.Mar 26.Mar 24.Apr

1st Apr 1.Apr 3.Apr 7.Apr 14.Apr 29.Apr

1st May 1.May 3.May 7.May 18.May 5.Jun

1st Jun(latest) 1.Jun 4.Jun 9.Jun 16.Jun 29.Jun

1stAug(lowlands) 1.Aug 2.Aug 8.Aug 14.Aug 5.Oct

1st Aug(flooding) 1.Aug 9.Aug 17.Aug 3.Sep 3.Nov

Table 7: summaries of flooding risk with crop failure for each week in August

August RETURN PERIODS ( of flooding with crop failure)

Week 1 5 years

Week 2 6.5 years

Week 3 3.5 years

Week 4 5 years

Comment on the summaries:

Graph (v) shows that week 3 and 1 have more variations in the rainfall totals for the week.

Comparing week 3 and 1, it shows that week 3 has more variation than week 1 with almost the same

amount of outliers.

What is the distribution of sowing dates?

In Douala, farmers always sowed before the start of the peak of rainfall when the soil is saturated,

especially in August. So, the maximum or latest sowing date is the same for both farmers: day 214

(1.August). Earliest sowing date for bold farmer is day 61 (1 March) opposed to day 153 (1.June) for

latest farmer.

How wider is the season of the bold farmer compared to that of a latest farmer?

19

20

Difference in the length of growing season as experienced by bold and latest farmers

Bold farmers Latest farmers

Year Day_number Date Day_number Date Difference

1950 65 5-Mar 155 3-Jun 90

1951 114 23-Apr 160 8-Jun 46

1952 71 11-Mar 153 1-Jun 82

1953 84 24-Mar 159 7-Jun 75

1954 65 5-Mar 159 7-Jun 94

1955 61 1-Mar 166 14-Jun 105

1956 70 10-Mar 154 2-Jun 84

1957 63 3-Mar 161 9-Jun 98

1958 79 19-Mar 169 17-Jun 90

1959 76 16-Mar 159 7-Jun 83

1960 67 7-Mar 171 19-Jun 104

1961 83 23-Mar 181 29-Jun 98

1962 74 14-Mar 159 7-Jun 85

1963 96 5-Apr 156 4-Jun 60

1964 68 8-Mar 156 4-Jun 88

1965 71 11-Mar 161 9-Jun 90

1966 67 7-Mar 157 5-Jun 90

1967 103 12-Apr 168 16-Jun 65

1968 66 6-Mar 161 9-Jun 95

1969 63 3-Mar 174 22-Jun 111

1970 78 18-Mar 177 25-Jun 99

1971 78 18-Mar 165 13-Jun 87

1972 89 29-Mar 154 2-Jun 65

1973 78 18-Mar 153 1-Jun 75

1974 72 12-Mar 181 29-Jun 109

1975 68 8-Mar 159 7-Jun 91

1976 93 2-Apr 157 5-Jun 64

1977 89 29-Mar 164 12-Jun 75

1978 96 5-Apr 153 1-Jun 57

1979 63 3-Mar 153 1-Jun 90

1980 77 17-Mar 154 2-Jun 77

1981 78 18-Mar 160 8-Jun 82

1982 70 10-Mar 180 28-Jun 110

1983 88 28-Mar 174 22-Jun 86

1984 70 10-Mar 173 21-Jun 103

1985 75 15-Mar 165 13-Jun 90

1986 89 29-Mar 156 4-Jun 67

1987 67 7-Mar 178 26-Jun 111

1988 68 8-Mar 169 17-Jun 101

1989 80 20-Mar 164 12-Jun 84

1990 115 24-Apr 154 2-Jun 39

1991 99 8-Apr 173 21-Jun 74

1992 68 8-Mar 165 13-Jun 97

1993 69 9-Mar 155 3-Jun 86

1994 83 23-Mar 174 22-Jun 91

1995 77 17-Mar 157 5-Jun 80

1996 61 1-Mar 153 1-Jun 92

1997 85 25-Mar 168 16-Jun 83

1998 97 6-Apr 167 15-Jun 70

1999 88 28-Mar 170 18-Jun 82

2000 82 22-Mar 157 5-Jun 75

21

2001 61 1-Mar 155 3-Jun 94

2002 61 1-Mar 167 15-Jun 106

2003 89 29-Mar 159 7-Jun 70

2004 85 25-Mar 161 9-Jun 76

2005 71 11-Mar 157 5-Jun 86

2006 75 15-Mar 166 14-Jun 91

Minimum Mean Range Std.deviation Maximum

57 years 39 days 85.053 days 72 days 15.461 days 111 days

5.5weeks 12weeks = 3months 10weeks 2weeks 16weeks = 4 months

How often will the bold farmer have a longer growing season than the latest farmer?

This question is answered by quantifying how often the season was longer for a bold farmer than for a

latest farmer (conditional on differences being greater than zero). It will be found that the bold farmer

who sows earlier in the season will always have the benefit of a longer season, which is a full 100% of

the time.

Talking about the length of the growing season, when we subtract starting date (as day number) of the

“latest” farmer from that of the “bold” farmer we get 57 non-zero values, indicating that the former

experienced a longer growing season every single year. On average his season was 85 days longer, at

last 39 days longer and at most 111days longer, with a standard deviation of about 15.5 days.

How far is the sowing season of the bold farmer compared to the occurrence of the flooding

period?

The maximum or latest sowing date for bold farmer is day 115 (24.April). Earliest flooding

occurrence date is day 214 (1.August) according to our given definition. So, the bold strategy is at

least 99 days (almost 3 months) before the first flooding strike which is almost 2.5 weeks (19 days)

more variable, and completely off after 3rd

November (day 308).

22

F. IMPLICATIONS:

Some conclusions for the client (Ministry of agriculture) based on these

summaries and risks calculations.

At last, we could say the highest true risk of experiencing flood with crop failure is observed once

every 3.5 years during the third week of August. There is little difference between weeks 1 and 4 with

a chance of flooding with crop disruption every 5 years. The smallest risk is observed in week 2 with a

6.5 years return period.

1. The “sowing strategy” definition that I would recommend to a farmer

The “sowing strategy” definition that I would recommend to a farmer is the bold method.

2. The advantages

The advantages could be:

The greatest (range =54 days) and variable (s.d. =13 days) spread of sowing dates.

The possibility to harvest more than one type of crops within this long sowing period.

The possibility of choosing the accurate crop to match the expected season length, with

higher yield.

A crop growing during a shorter season (3 months) can be sow, with (57/57) 100 % risk

or zero risk not to meet a long dry spell.

As the threshold rainfall for sowing is high (40 mm), chance of crop failure is low even

if there is long dry spell later.

Hence, by sowing earlier, the risk of early crop failure due to flood could be greatly

reduced even in low lands because after three months, the growing stage enables more

plants‟ maturity to face flooding events and the farmer can gain in confidence in the

whole system of recommendations.

3. The disadvantages

The disadvantages could be:

Even with a better seasonal forecast, there still be at least once every three (3)

years a chance of crop destruction in Douala, during the peak period (July,

August, and September) of rainfall when the soil is saturated, especially in

August.

The flooding event in low land areas has a range of 65 days (about 2 months).

There is highest interannual variability i.e. spread in flooding dates with a

variability of 32 days (about 4.5 weeks mainly within the month of August). So

maximum awareness must be taken around day 221(8.August) and also the

farmer needs to keep himself in state of preparedness for this period so as to

take action of the 1st occasion to protect or to secure the crop). That is why I

strongly recommend to the farmers dwelling in Douala and surrounding:

“Please! Sow earlier!”.

23

G.APPENDICES:

Appendix A: References and sources documentation

Instat Climatic Guide Chapters 4-7 Roger Stern, Derk Rijks, Ian Dale, Joan Knock ed. January 2006

Confidence and significance: Key Concepts of Inferential Statistics, the University of Reading

Statistical Services Centre, February 2006

Climatic e-books (www.reading.ac.uk/ssc )

CAST for SADC ( www.ssc.rdg.ac.uk/sadc-training-pack/)

Appendix: Daily data for Douala, Cameroon 1950-2006

Fig. 1.a Time series plot of annual rainfall totals and number of rainy days at Douala, Cameroon, from

1950 to 2006. The dotted horizontal lines show one and two standard deviations above and below the

mean.

http://www.reading.ac.uk/ssc

http://www.ssc.rdg.ac.uk/sadc-training-pack/

24

PLOT OF ANNUAL NUMBER OF RAINY DAYS IN DOUALA;CAMEROON(1950-2006)

Year2010200019901980197019601950

RA

INY

DA

YS

250

240

230

220

210

200

190

The individual daily values or rainfall for every year, together with monthly summaries, are given in

the Appendix.

Fig. 2. Daily rainfall values for 1950 and 1951.

1950

1951

25

Figure 2 shows the pattern of rainfall through the year for two years. Notice the „false start‟ to the

season in each year, where a few early days of rainfall are followed by a long spell of dry days.

Fig. 3. Summary values of monthly rainfall.

1950-2006

X76gfedcb

X77gfedcb

X78gfedcb

X79gfedcb

X80gfedcb

X81gfedcb

PLOT OF THE SUMMARY STATISTICS AGAINST THE MONTHS IN DOUALA; CAMEROON

MONTHSJan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

1200

1000

800

600

400

200

0

26

Table1: Summary values of monthly rainfall

Column Min. 25% Mean Median 75% Max.

Jan 0 8.3 42.95 28.2 64.35 183.5

Feb 2.3 33.15 65.52 55 100.4 216.2

Mar 21.1 127.2 181 174.9 228.4 425.7

Apr 122.6 194.7 238.2 228.1 271.8 434.6

May 133 216 286 297 345.8 433.1

Jun 174.5 322.3 441.1 404.4 540.4 1002

Jul 207.4 555.6 695.7 689.1 867.1 1208

Aug 248.3 556.9 739.5 733.4 884.3 1240

Sep 273.9 472.7 623.1 613.3 773.6 1026

Oct 196.3 334.7 408.4 410.2 456.2 635.5

Nov 0 94.45 132.4 120.1 165.7 305.2

Dec 0 11.15 40.85 25.8 59.15 168.3

Figure 3, Table 1 and 2 show some summary values for the monthly totals. July and August are seen

to be the peak of the rainy season, with a lowest total of 207.4mm in July 2003 and one year

(August1966) with over 1240mm. The rains are usually from January to December, though

December, January and February are occasionally dry.

Table 2: minimum, mean and maximum monthly rainfall summaries

MONTH MINIMUM MEAN MAXIMUM

January 0 42.95 183.5

February 2.3 65.52 216.2

March 21.1 181 425.7

April 122.6 238.2 434.6

May 133 286 433.1

June 174.5 441.1 1002

July 207.4 695.7 1208

August 248.3 739.5 1240

September 273.9 623.1 1026

October 196.3 408.4 635.5

November 0 132.4 305.2

December 0 40.85 168.3

27

Table 2: Monthly rainfall totals in each year (1950-2006).

Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

1950 135 123 426 270 141 510 1003 743 606 346 168 56

1951 7 47 71 188 345 639 571 689 979 379 164 25

1952 74 22 191 316 420 502 954 707 777 453 130 46

1953 90 119 216 132 325 862 689 802 733 361 148 19

1954 23 142 181 340 330 711 813 248 763 493 210 69

1955 39 47 225 272 297 317 547 838 460 404 151 4

1956 182 185 273 283 263 415 1154 836 770 560 284 124

1957 184 48 220 248 359 505 750 997 657 421 112 31

1958 29 55 179 271 193 555 684 693 797 455 227 79

1959 105 37 273 195 353 493 739 1091 636 602 120 120

1960 74 43 167 193 298 371 675 1232 870 338 46 32

1961 63 5 284 271 299 298 757 947 568 308 149 8

1962 30 106 244 333 346 564 913 819 553 417 298 39

1963 20 73 58 216 331 328 869 566 475 263 99 119

1964 6 35 307 261 234 648 889 1117 805 337 176 57

1965 51 111 212 199 263 786 327 992 826 502 95 73

1966 41 39 302 349 182 407 666 1240 639 346 168 168

1967 0 107 141 201 310 1002 682 952 613 421 146 40

1968 79 67 146 190 335 556 499 498 517 239 121 28

1969 37 50 217 196 251 592 776 641 918 437 134 134

1970 27 115 142 175 216 556 881 680 854 535 113 14

1971 42 38 211 211 348 393 980 1081 616 545 93 38

1972 44 26 94 123 198 362 636 616 672 539 77 6

1973 90 103 125 304 168 310 761 668 418 315 88 100

1974 12 53 160 155 215 223 650 1025 619 382 74 8

1975 15 59 184 270 191 478 743 571 752 604 223 26

1976 36 216 119 159 346 352 845 548 347 333 155 62

1977 11 13 147 280 330 380 873 785 462 196 46 32

1978 2 79 77 226 276 441 474 904 859 526 140 15

1979 9 120 218 228 339 619 778 530 393 368 187 0

1980 66 153 237 222 412 361 564 506 1026 433 106 22

1981 0 50 139 203 225 432 1208 847 891 458 110 1

1982 120 48 115 249 381 329 646 641 806 481 29 12

1983 0 4 131 156 158 245 534 490 520 385 102 73

1984 32 32 129 294 298 175 280 534 313 452 54 2

1985 118 20 313 342 247 267 403 755 536 282 216 2

1986 7 68 179 195 324 451 936 537 598 520 305 1

1987 40 19 109 168 258 177 417 851 569 453 83 15

1988 28 36 175 266 214 393 865 588 408 444 98 124

1989 0 2 89 233 187 296 332 861 621 372 115 5

1990 43 90 53 213 230 442 656 829 540 436 209 96

1991 1 64 117 348 166 203 1091 733 398 321 34 3

1992 8 5 242 198 268 404 665 370 558 438 161 0

1993 2 61 149 260 433 304 593 897 289 348 232 50

1994 0 34 170 435 373 264 742 761 676 394 0 34

1995 7 9 165 186 380 388 610 635 715 484 156 25

1996 32 154 247 307 406 356 326 514 477 323 7 12

1997 13 4 233 250 291 378 963 637 274 410 117 17

1998 28 8 21 206 216 328 468 534 706 276 47 23

1999 86 79 76 231 275 437 814 472 470 636 120 11

2000 28 4 229 216 133 436 789 871 386 322 117 20

2001 8 71 155 255 406 753 752 524 602 304 205 46

2002 3 104 214 291 316 708 618 961 591 427 125 8

2003 153 85 106 183 190 526 207 948 339 307 104 15

2004 27 98 129 255 294 269 353 275 459 446 150 89

2005 20 62 228 170 395 393 303 670 786 322 87 14

2006 24 89 359 195 324 250 943 853 1009 385 117 39

28

Appendix: Daily data for Douala, Cameroon 1950-2006

Daily data for: Yr1950

Mon Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

---------------------------------------------------------------------------

Day. ---------------------------------------------------------------------

1 -- 32.6 -- 20.3 10.2 2.6 71.0 65.6 -- 0.1 0.1 1.6

2 -- 72.0 0.1 -- 0.9 1.7 28.0 28.0 11.0 32.0 3.8 --

3 -- -- 8.0 63.0 0.1 109.6 7.8 54.4 45.6 23.5 -- --

4 -- -- -- 1.5 -- 4.2 4.1 2.6 59.5 10.4 16.9 --

5 0.4 -- 36.2 1.7 3.0 -- 32.1 6.6 2.4 0.8 0.2 --

6 -- -- -- 4.0 -- 1.0 7.7 14.4 32.5 4.4 -- --

7 -- -- 127.8 -- 1.0 11.7 52.7 0.3 9.0 7.6 -- --

8 -- 0.7 7.2 27.9 0.1 -- 17.7 -- 1.2 1.8 4.1 --

9 -- -- -- 2.6 -- 1.7 22.8 -- 2.0 -- -- --

10 -- -- 3.3 -- 0.6 8.9 56.8 7.7 104.0 1.1 14.3 --

11 -- -- -- -- -- 3.7 21.3 44.0 4.6 1.5 11.5 --

12 -- -- 0.2 -- 1.3 14.3 215.0 14.7 15.6 10.1 -- --

13 2.3 -- 4.0 -- -- 10.5 14.4 9.1 11.6 -- -- --

14 56.0 -- -- -- 0.8 10.5 -- 43.3 17.7 0.2 13.5 --

15 9.7 -- -- 0.3 4.9 9.0 -- 11.2 10.7 29.9 16.1 --

16 -- -- 7.0 1.3 -- -- 39.8 0.2 12.5 34.0 0.3 --

17 -- -- 21.6 -- 0.7 2.8 17.5 48.5 23.7 3.9 3.9 --

18 -- -- -- -- 0.2 17.8 4.3 23.4 1.1 34.5 5.8 --

19 -- -- 0.7 -- 11.6 7.9 48.6 -- 10.7 4.3 0.5 1.2

20 -- -- 101.2 5.7 0.5 -- 4.8 3.8 0.2 1.1 -- 13.9

21 -- -- 5.5 3.7 4.1 9.4 0.3 37.8 4.6 26.8 -- 0.7

22 -- -- -- 38.4 9.1 11.5 1.7 81.4 16.5 18.2 0.7 --

23 -- -- 0.2 -- 7.3 -- 1.8 22.0 0.2 22.0 -- --

24 -- -- -- 11.5 49.6 87.1 18.3 48.1 49.8 0.4 -- --

25 2.9 4.1 62.5 12.3 0.9 0.1 14.4 9.5 126.2 -- -- --

26 27.7 -- 37.8 47.1 8.2 -- 43.9 28.7 6.3 28.4 -- 1.6

27 -- 10.8 -- -- 7.9 58.9 108.9 68.0 11.8 2.0 0.2 --

28 -- 2.5 -- 25.4 -- 115.0 49.1 8.5 7.5 46.2 -- 18.9

29 -- -- 2.6 0.5 8.2 10.5 5.2 5.5 0.6 75.8 --

30 35.5 0.7 0.6 0.2 2.3 55.2 55.3 1.7 -- 0.2 --

31 -- 1.7 17.0 32.0 1.1 -- 18.4

29

Daily data for: Yr1951

Mon Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

---------------------------------------------------------------------------

Day. ---------------------------------------------------------------------

1 -- -- -- -- 3.1 0.7 1.8 34.9 102.2 3.5 6.7 --

2 -- 0.1 0.2 -- 5.2 0.8 1.1 14.4 42.0 2.9 -- --

3 -- -- -- -- 2.6 22.6 19.8 85.1 -- 2.1 111.9 --

4 -- 32.7 -- -- 80.9 16.0 8.4 6.9 0.1 28.1 1.6 --

5 5.1 -- 0.6 6.3 -- 14.7 36.0 8.6 7.5 2.5 9.3 --

6 1.2 0.3 -- -- 18.6 0.8 17.4 31.8 9.6 3.4 8.2 --

7 -- 13.7 12.1 3.8 -- 1.8 25.4 27.8 22.5 9.2 1.6 --

8 -- -- -- 1.1 0.3 53.6 7.9 25.6 21.7 13.1 -- --

9 -- -- -- -- 0.4 29.0 3.4 6.9 32.3 11.8 0.8 --

10 -- -- -- 1.4 -- 9.2 31.6 16.2 65.3 1.5 3.4 --

11 -- -- -- -- -- 2.0 1.1 23.0 97.1 0.9 0.1 --

12 -- -- 1.3 10.3 3.3 11.5 3.3 0.8 65.5 28.0 -- --

13 -- 0.3 -- -- -- 6.4 5.3 6.0 76.4 1.4 0.1 --

14 -- -- 9.3 -- 4.5 -- -- 57.1 12.5 11.3 0.4 --

15 -- -- -- 10.5 -- 0.6 2.0 16.9 18.5 19.2 2.4 --

16 -- -- -- -- 2.4 6.0 9.4 -- 7.7 -- -- --

17 -- -- -- 0.6 2.0 4.6 26.2 0.1 -- 73.9 3.3 --

18 -- -- 16.0 -- 4.4 7.7 1.0 -- 20.0 44.9 10.3 --

19 -- -- 10.1 11.0 12.0 0.6 27.3 2.8 15.2 9.1 -- --

20 -- -- -- -- 9.0 -- 23.8 5.4 3.0 0.2 -- --

21 -- -- -- -- 19.2 4.9 -- 15.2 26.2 16.8 -- 24.8

22 -- -- -- -- 3.7 192.7 77.7 16.5 74.7 0.5 3.1 --

23 -- -- -- 51.0 21.0 217.3 1.8 11.7 44.9 35.0 -- --

24 -- -- 1.3 -- 1.7 10.7 1.9 2.0 -- -- -- --

25 -- -- 7.1 70.7 70.6 -- 63.7 24.9 12.6 -- -- --

26 -- -- -- -- 0.8 -- 40.5 41.8 40.0 3.8 -- --

27 -- -- 1.4 -- 68.9 -- 6.0 9.4 18.1 14.9 -- --

28 -- -- -- -- -- -- 6.0 66.5 31.7 35.3 -- --

29 -- -- 4.1 -- -- 30.1 33.4 47.6 5.6 -- --

30 -- -- 16.8 1.4 24.9 17.8 95.0 64.1 0.3 0.3 --

31 1.0 11.6 9.5 73.1 1.8 -- --

30

Day numbers in the year

Month: Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

--------------------------------------------------------------------------------

Day ---------------------------------------------------------------------------- 1 1 32 61 92 122 153 183 214 245 275 306 336

2 2 33 62 93 123 154 184 215 246 276 307 337

3 3 34 63 94 124 155 185 216 247 277 308 338

4 4 35 64 95 125 156 186 217 248 278 309 339

5 5 36 65 96 126 157 187 218 249 279 310 340 6 6 37 66 97 127 158 188 219 250 280 311 341

7 7 38 67 98 128 159 189 220 251 281 312 342

8 8 39 68 99 129 160 190 221 252 282 313 343

9 9 40 69 100 130 161 191 222 253 283 314 344

10 10 41 70 101 131 162 192 223 254 284 315 345 11 11 42 71 102 132 163 193 224 255 285 316 346

12 12 43 72 103 133 164 194 225 256 286 317 347

13 13 44 73 104 134 165 195 226 257 287 318 348

14 14 45 74 105 135 166 196 227 258 288 319 349

15 15 46 75 106 136 167 197 228 259 289 320 350 16 16 47 76 107 137 168 198 229 260 290 321 351

17 17 48 77 108 138 169 199 230 261 291 322 352

18 18 49 78 109 139 170 200 231 262 292 323 353

19 19 50 79 110 140 171 201 232 263 293 324 354

20 20 51 80 111 141 172 202 233 264 294 325 355 21 21 52 81 112 142 173 203 234 265 295 326 356

22 22 53 82 113 143 174 204 235 266 296 327 357

23 23 54 83 114 144 175 205 236 267 297 328 358

24 24 55 84 115 145 176 206 237 268 298 329 359

25 25 56 85 116 146 177 207 238 269 299 330 360 26 26 57 86 117 147 178 208 239 270 300 331 361

27 27 58 87 118 148 179 209 240 271 301 332 362

28 28 59 88 119 149 180 210 241 272 302 333 363

29 29 60 89 120 150 181 211 242 273 303 334 364

30 30 -- 90 121 151 182 212 243 274 304 335 365 31 31 -- 91 -- 152 -- 213 244 -- 305 -- 366

First 1 32 61 92 122 153 183 214 245 275 306 336

Last 31 60 91 121 152 182 213 244 274 305 335 366

Documents

REPORT ON SOWING PERIOD AND FLOODING …sns97aal/eSIAC/Cameroon_risk...1 REPORT ON SOWING PERIOD AND FLOODING RISKS IN DOUALA, CAMEROON BY GERVAIS DIDIER YONTCHANG Cameroon Meteorological