Climate change over Libya and impacts on agriculture

CLIMATE CHANGE OVER LIBYA AND IMPACTS ON AGRICULTURE

THESIS Submitted to Cairo University for

the degree of M.Sc. in Meteorology

BY KHALID IBRAHIM EL FADLI

B.Sc. in Meteorology, Tripoli University-Libya Under the supervision

Of Prof. Dr. M.M. Abdel-Wahab

And Prof.Dr. A. MOSLHI Dr. A. KHALIL

Faculty of Science

Cairo University-Egypt

June 2012

Reviewed by

Prof. Piero Lionello

University of Salento-Italy

Khalid elfadli ([email protected]) Director of climate & climate change department‐Libyan National Meteorological Centre

Climate Change over Libya & Impacts on Agriculture

II

Khalid Ibrahim Elfadli June2012


III


ACKNOWLEDGMENT

All praise is due to Allah before and after

I would like to express my deep appreciation and gratitude to Prof. Dr.

Mohamed Magdy Abdel-Wahab, Astronomy and Meteorology Dept., Faculty of

science, Cairo University, for facilitating many problems, for his permanent

support, encouragements, supervision, guidance and useful comments throughout

the course of current thesis, and to Dr. Alaa Abd El Raouf Khalil, Researcher,

Agricultural Research Center (ARC), Cairo-Egypt, for his kind and valued

supervision. Thanks extend for Dr. Mosulhi for his continuous support. Thanks

also extend to Prof. Piero Lionello (University of Salento-Italy), for his well

reviewing and valued comments which add more scientific brightness to this

thesis, and his statement for assessment this study is "The thesis addresses important

issues and produces useful results in term of analysis of current trends of temperature and

precipitation, and of impacts of climate change on wheat yields. The adopted crop model is

shown to be able to satisfactorily reproduce the observed behavior of the crop and suggest that

in future climate conditions its yield can experience a large reduction (It is about 50% in

2020). The thesis shows an excellent knowledge of existing literature and the analysis of present

climate tends is accurate.". Many of thanks to Dr. Manola Brunet (University Rovira

i Virgili-Spain), for her unlimited helping of providing a set of different and

various important scientific papers and related references.

Special gratitudes extend to Dr. Abedelkader Abseim for his strong

and active brotherly assistance during entire times of my study and to all who by

the helping hand extended to me for finalizing this project.

My deep gratitude is due to my parents and little family, in particular my

loyal wife, for their patience and continuous support especially in times of

difficulty.


IV


ABSTRACT According to the IPCC’s (Intergovernmental Panel on Climate Change) most recent

Assessment Report (IPCC IV 2007), effects of climate change are already observed and their

costs will mostly fall on developing countries threatening to undermine achievement of the

Millennium Development Goals, such as to reduce their poverty and to secure them with

sufficient food. Agricultural production worldwide is already increasingly under pressure in

order to meet the demands of rising populations and the support for the agriculture sector is a

major component of future development. The present work is mainly directed to get better

understanding and comprehensive knowledge regarding the evolution of temperature and rainfall

characteristics over Libya. On one hand, the analysis of time series at national and regional scale

for the last 50 years allows a diagnosis of the spatial distribution of ongoing trends. On the other

hand future evolution is described on the basis of IPCC projections.

Moreover, this thesis tries to assess the impacts of climate change on wheat yield. This is

motivated by its strategic importance for Libya where it represents the main national crop.

This analysis is based on the CERES-wheat model, which is a mechanistic, process-oriented

model for describing the development of cereal crops. In this thesis the CERES-wheat model is

employed to understand physiological processes and yield of wheat under current and future

climate features. The actual crop records are used for comparing present and future yields

(computed on the basis of GCM – Global Climate Models – outputs).

The results of the work indicate that the spatial average of the mean annual temperature has

increased at a rate of 0.31°C/decade. This is mainly due to the increase of autumn and summer

temperatures. This warming is associated with higher rates of change for minimum than

maximum temperatures (0.46 versus 0.14°C/decade). Negative trends of total annual rainfall

have been observed at most investigated weather stations of Libya.

The CERES-wheat model predicts with very high accuracy of grain yield, flowering and

physiological maturity dates of wheat cultivation. Under climate change conditions, both

flowering and physiological maturity dates are shorter than of the current climate (season of

1999/2000). Correspondingly, the grain yield will be reduced.


V


List of Contents

1. Chapter one (Introduction)…………………………………………………… 1

1.1 Climatic overview………………………………………………………………….. 2

1.1.1 Air temperature…………………………………………………………….. 3

1.1.2 Precipitation………………………………………………………………... 4

1.2 Climatic datasets and analyzing methodology…………………………………… 7

1.2.1 History and objectives……………………………………………………… 9

1.2.2 Data homogenization……………………………………………………….. 10

1.2.3 Detecting trends……………………………………………………………. 10

1.3 Agricultural production in Libya………………………………………………... 14

1.3.1 Libya production potential…………………………………………………. 15

1.3.2 Physical Landscape………………………………………………………… 15

1.3.3 Soils………………………………………………………………………… 15

1.3.4 Wheat production potential………………………………………………… 16

2. Chapter two (Climate change trends)………………………………………… 19

2.1 Introduction……………………………………………………………………… 20

2.2 Trends of maximum temperature (1961-2010)…………………………………... 21

2.3 Trends of minimum temperature (1961-2010)…………………………………… 21

2.4 Trends of average temperature (1961-2010)……………………………………... 23

2.5 Trends of diurnal temperature range (1961-2010)………………………………… 36

2.6 Trends of total rainfall (1961-2010)……………………………………………… 39

2.7 Trends of rainy days (1961-2010)………………………………………………... 52

3. Chapter three (Impact of climate change on wheat crop)…………………… 54

3.1 Impact of climate change on agricultural………………………………………… 55

3.2 Simulation of the impact of climate change on wheat crop……………………… 55

3.2.1 The Decision Support System for Agro technology Transfer……………… 55

3.2.2 CERES-Wheat Model……………………………………………………… 57

3.3 Wheat, (Triticum aestivum L.)………………………………………………….. 58

3.3.1 Grain Production of Wheat in Libya……………………………………….. 61

3.3.2 Two Small Agricultural Areas……………………………………………... 62


VI


3.4 Effect of climate change on wheat production…………………………………… 64

3.5. Data and methodology…………………………………………………………... 67

3.6 Validation of CERES-Wheat model……………………………………………... 68

3.6.1 Flowering date……………………………………………………………… 68

3.6.2 Physiological maturity……………………………………………………… 71

3.6.3 Grain yield………………………………………………………………….. 71

3.7 Simulation the impact of climate change on wheat crop……………………... 74

3.7.1 Flowering date……………………………………………………………… 74

3.7.2 Physiological Maturity……………………………………………………... 74

3.7.3 Grain yield………………………………………………………………….. 75

4. Conclusion……………………………………………………………………… 78

5. Appendixes……………………………………………………………………... 82

6. References………………………………………………………………………..

7. Arabic summary…………………………………………………………………

87

100


VII


List of Tables

Table (1.1) Meteorological Stations Identification…………………………………………….. 8

Table (1.2) land potential sub-classes by mapping unit in '000 ha for Libya…………………... 17

Table (2.1) Trends of Maximum Temperature (oC/decade), by Mann - Kendall Sen's test

(1961-2010)………………………………………………………………………... 22

Table (2.2) Trends of Minimum Temperature (oC/decade), by Mann- Kendall Sen's test (1961-

2010)………………………………………………………………………... 24

Table (2.3) Trends of Average Temperature (°C/decade), by Mann- Kendall Sen's test (1961-

2010)……………………………………………………………………………….. 26

Table (2.4) Anomalies Variability (%) for Annual Temperature °C (1961-2010) in Different

Base Line Averages………………………………………………………………... 37

Table (2.5) Trends of Diurnal Temperature Range (°C/decade), by Mann- Kendall Sen's test

(1961-2010)………………………………………………………………………… 38

Table (2.6) Trends of Total Rainfall (mm/decade), by Mann- Kendall Sen's test (1961-2010) 42

Table (2.7) Trends of Total Rainfall Ratio (“%”/decade), by Mann- Kendall Sen's test (1961-

2010)……………………………………………………………………………….. 43

Table (2.8) Anomalies Variability (%) for Annual Rainfall Totals mm (1961-2010) in

Different Base Line Averages……………………………………………………... 51

Table (2.9) Trends of Rainy Days (=>0.1 mm/decade), by Mann- Kendall Sen's test (1961-

2010)……………………………………………………………………………….. 52

Table (3.1) Comparison between measured and predicted flowering date for wheat cultivars.... 70

Table (3.2) Comparison between measured and predicted physiological maturity for wheat

cultivars …………………………………………………………………………… 72

Table (3.3) Comparison between measured and predicted grain yield for wheat cultivars……. 73

Table (3.4) Comparison between measured and predicted flowering date and percentages

difference by using SRES A1B emission scenario for wheat cultivars……………. 75

Table (3.5) Comparison between measured and predicted physiological maturity and

percentages difference by using SRES A1B emission scenario for wheat

cultivars…………………………………………………………………………….. 76

Table (3.6) Comparison between measured and predicted grain yield and percentages

difference by using SRES A1B emission scenario for wheat cultivars……………. 77


VIII


List of Figures

Figure (1.1) Geographical locations of studied meteorological stations-Libya………………. 9

Figure (1.2) Wheat production potential in Libya……………………………………………. 18

Figure (2.1) Trends of Average Temperature for period (1961-2010)………………………. 27

Figure (2.2) Annual anomalies in average temperature from 1961 to 2010 for base line

(1981 – 2010) average………………………………………………………………… 28

Figure (2.2) (continued) Annual anomalies in average temperature from 1961 to 2010 for

base line (1981 – 2010) average……………………………………………………… 29










base line (1981 – 2010) average………………………………………………………. 34

Figure (2.3) Trends of Rainfall Total (mm/decade) for period 1961-2010…………………… 44

Figure (2.4) Annual anomalies in total rainfall ratios from 1961 to 2010 for base line (1981

– 2010) average……………………………………………………………………….. 45

Figure (2.4) (continued) Annual anomalies in total rainfall ratios from 1961 to 2010 for base

line (1981 – 2010) average……………………………………………………………. 46


line (1981 – 2010) average……………………………………………………………. 47


IX



line (1981 – 2010) average……………………………………………………………. 48


line (1981 – 2010) average……………………………………………………………. 49


line (1981 – 2010) average……………………………………………………………. 50


line (1981 – 2010) average……………………………………………………………. 51

Figure (3.1) Wheat production areas in Libya………………………………………………... 64

Figure (3.2) Comparison between observed and predicted minimum and maximum

temperature and rainfall during 2000 and the average data from 2010 to 2020…….... 69


Khalid Ibrahim Elfadli 1 June2012

Chapter one

Introduction



1.1 Climatic overview

Libya is located in North Africa along the southern Mediterranean coast by 1,770

kilometers; Libya's coastline is the longest of any African country bordering the Mediterranean.

It is bound to the west by Tunisia and Algeria, the southwest by Niger, the south by Chad and

Sudan, and the east by Egypt. Libya extends over 1,759,540 square kilometers, making it

the 17th and 4th largest nation by size in the world and Africa respectively. Libya lies between

latitudes 19° and 33°N, and longitudes 9° and 26°E. The portion of the Mediterranean Sea north

of Libya is often called the Libyan Sea. Libya influenced directly and strongly by the

Mediterranean climate.

According to the Koppen definition, the Mediterranean climate is characterized by hot,

dry, sunny summers and a winter rainy season (Csa, Csb. In summer, the climate is dominated by

subtropical anticyclones, and trade winds prevail. Daily weather is greatly influenced by sea

breezes and land breezes. In winter, mid-latitude depressions bring rain. In winter, temperatures

rarely drop below 5°C and are more likely to be in the region of 12° to 13°C while in summer

averages can be up to 27°C. Frosts are very rare in a Mediterranean climate although when they

do occur they can cause great damage to crops. For this reason, vulnerable crops such as citrus

fruits are usually planted on sloping terrain rather than in the valley floors, where in a cold spell

frost are likely to occur as cold air collects in the valley bottom.

Mediterranean climate zones are associated with the five large subtropical high pressure

cells of the oceans, The Azores High, South Atlantic High, North Pacific High, South Pacific

High and Indian Ocean High. These high pressure cells shift Polar ward in the summer and

equator ward in the winter, playing a major role in the formation of the world's tropical deserts

and the zones of Mediterranean climate polar ward of the deserts. For example, the Azores High

is associated with the Sahara Desert and the Mediterranean Basin's climate (The South Atlantic

High is similarly associated with the Namib Desert and the Mediterranean climate of the western

part of South Africa. During summer, regions of Mediterranean climate (also known as Dry-

Summer Subtropical for the Case areas) are dominated by subtropical high pressure cells, with

dry sinking air capping a surface marine layer of varying humidity and making rainfall

impossible or unlikely except for the occasional thunderstorm, while during winter the polar jet

stream and associated periodic storms reach into the lower latitudes of the Mediterranean zones,



bringing rain, with snow at higher elevations. As a result, areas with this climate receive almost

all of their yearly rainfall during the winter season, and may go anywhere from 4 to 6 months

during the summer without having any significant precipitation.

1.1.1 Air temperature

The cold period in the Mediterranean occurs during (October-March) and the warm

period occurs during (April-September. The analysis of the mean annual air temperature time

series (1968-1996) shows that in the Mediterranean, during the 20th century, the following

variations occur: In the beginning of the century, approximately at the end of the first decade, the

most recent low air temperature period occurs in the Mediterranean, as in almost the whole

planet. A lot of scientists claim that the end of the “Little Ice Age” period is set down during

these years, a period, which affect the climate of the earth over four centuries. Further to, the air

temperature rises abruptly until the beginning of 1940’s and after a small drop for about a

decade, the air temperature follows an increasing trend reaching a secondary maximum in the

beginning of the 1960’s. Following, a decreasing trend appears up to the middle of the 1970’s.

Finally, in the Central and West Mediterranean the air temperature rises from the end of the

1970’s, while in East Mediterranean the warning is observed 15 years later.

This temporal pattern appears similar to the pattern in the Northern Hemisphere, with the

exception of the recent lag warming in the East Mediterranean. Although regional differences

are relatively high, most of Europe has experienced rising temperatures of about 0.8°C during the

20th century (IPCC 1996; IPCC 2001). Analysis of surface air temperature observed at stations

located in all regions of the Mediterranean basin, indicates similar patterns in the global or and

hemispheric scale; namely a cooling during the period 1955–1975 and a strong warming during

the 1980’s and the first half of the 1990’s (Piervitali et al., 1997). However, the east–west

Mediterranean difference in air and sea surface temperature trends is distinctive. In the region of

the Eastern Mediterranean, Repapis and Philandras, (1988), showed that, the march of the

mean annual air temperature is almost parallel to the respective one in the Northern Hemisphere,

from the minimum that happens in the beginning of the 20th century up to the heating of 0.6°C

observed about the middle of the century and thereafter the cooling of the decades of 1960 and

1970, after small fluctuations. The cooling observed during this period in the Northern

Hemisphere is inverted soon and since the beginning of the decade of 1980 the air temperature



exceeds the temperature levels corresponding to the middle of the century. Concerning the

Eastern Mediterranean, the cooling is more intense of about –0.6°C and even if it has been

reversed since the decade of 1980, the last two or three years seems to reach the levels of

temperature in the middle of the century. Sahsamanoglou and Makrogiannis (1992), have

proved that, during the period 1950-1988, the air temperature in the region of Western

Mediterranean presents positive trend of 0.01-0.02°C /year and the equivalent negative trend of

0.01-0.02 °C /year, in the region of the Eastern Mediterranean, as a result of the small change in

circulation observed in the region of the Mediterranean during the examined period. Also

Metaxas et al. (1991), concluded with similar results after having examined the sea surface

temperature time series for the region of the Mediterranean. Piervitali et al. (1997), found that

the mean air temperature in the Mediterranean and more specifically in the Central and Western

Mediterranean presents an increase of about 0.80°C /100 years.

Regarding Eastern Mediterranean, recent studies report that the situation has begun to

change at the beginning of the 1990s, because the cooling trend in mean and maximum

temperatures have weakened (Turkes et al., 2002). Brunetti et al. (2000), showed positive

trends for both maximum and minimum daily temperature over the period 1865–1996 in Italy,

and they pointed out that the trends are greatest in the south of the country, while Moonen et al.

(2002), demonstrated decreases in extreme cold events in central Italy.

1.1.2 Precipitation

The winter rainfall exceeds three times the summer rainfall totals. This strong

winter/summer rainfall contrast is associated with a well pronounced seasonal cycle with

summertime warm, dry conditions associated with a strong high-pressure ridge over the Balkans.

The axis of the ridge is displaced southward over Egypt by a trough which extends from the

Persian Gulf area northwestwards towards Greece and which is associated with the Indian

summer monsoon depression.

The rainy season begins in October, associated with a change in the mean-wave pattern

of the upper westerly and an upper air flow which is characterized by a trough over Europe.



Winter is characterized by cyclonic disturbances and low mean pressure in the

Mediterranean, with higher pressure to the east associated with the Siberian high.

In March and April, as the main features of the upper flow (e.g. Jet streams), begin to

move northward from their southern most winter positions, the rainy season continues until May

where the summer dry regime is established. A characteristic pattern of the spatial variability of

the precipitation in the Eastern Mediterranean appears in Greece, where at a distance of about

350 Km the annual precipitation ranges from more than 2000 mm at the highlands of

northwestern Greece to less than 400 mm at Attica and western Cyclades, while the inter-annual

precipitation variability is high as well.

Precipitation, although mainly associated with cyclonic disturbances that originate in the

Mediterranean basin is also strongly influenced by local orographic effects. The winter mean

surface pressure pattern shows features which result from these cyclogenetic aspects.

With regard to precipitation in the Mediterranean region, drying trends have been

reported (IPCC, 2001), and occurrences of long dry spells especially during summer in the

southern areas have been found (Martin-Vide and Gomez, 1999). The eastern Mediterranean

especially, shows a tendency towards drier conditions (Kutiel et al., 1996; Turkes, 1998), while

the western and central areas, although showing negative trends in the number of wet days and/or

the total rainfall amounts, indicate an increase in intense precipitation events over the period

1951–1996 (Brunetti et al., 2001; Alpert et al., 2002).

The majority of the Mediterranean region has tended toward decreasing winter

precipitation during the last few decades, mostly starting in the 1970’s and proceeding to an

accumulation of dry years in the 1980’s and 1990’s (Schonwiese et al., 1994; Palutikof et al.,

1996; Piervitali et al., 1997; Schonwiese and Rapp, 1997). The west central Mediterranean

area was experiencing a precipitation decrease during the last 50 years (IPCC, 1996; Piervitali

et al., 1997). Decreasing precipitation is also evident in large parts of the eastern Mediterranean

area. Schonwiese et al. (1994) reported a pronounced significant trend towards a drier winter

climate in the eastern Mediterranean area, for the period 1961–1990.

The formation of Mediterranean depressions is partly determined by transitory excursions

of the polar front jet and the European trough, modified by the land-sea temperature contrast

which favors cyclogenesis over warm sea waters. Depressions over the eastern basin are often

associated with cold northerly airflow and lee cyclogenesis. These relationships provide a link



between the local rain-producing pressure systems and larger-scale aspects of the general

circulation over Europe.

From 1981 to 2003 cyclone density decreases over most parts of the Mediterranean.

However, trends are not uniform and interdecadal variability is high. In fact, other studies

suggest no actually significant trend, an increase of weak cyclones in the western Mediterranean

in the period 1978-1994 (Trigo et al., 2000), a positive trend in the Eastern Mediterranean,

though not in the rainy season (Maheras et al., 2001).

The analysis of cyclone climatology in the Mediterranean region shows trends and a

moderate response to future emission scenarios. The main signal is associated with a decrease of

cyclone frequency during winter in the western Mediterranean region, presumably associated

with a northward shift of the storm track and persistent high phase of the NAO (North Atlantic

Oscillation). Such a decline of cyclone frequency is suggested to continue as greenhouse gas

concentration increases, as shown by scenario simulations (Ulbrich and Christoph 1999,

Lionello et al., 2002).

However, cyclone activity presents large seasonal and spatial variability, with large

differences from western to the eastern Mediterranean and between cold and warm season

(Lionello et al., 2006). Anagnostopoulou et al. (2006), Studying the cyclones in the

Mediterranean region, found that the Hadley Center atmospheric General Circulation Model

(HadAM3P) predicts a future decrease of the frequency of the most severe cyclones (<1000 hPa)

at the SLP level, but the future cyclones will be more intense, especially at the 500 hPa level.

According to changes in the global scale, many areas experience increases in heavy precipitation

events (Groisman et al., 1999; French et al., 2002).

Such results were presented by Brunetti et al. (2001), and Alpert et al. (2002), for the

central and western parts of the Mediterranean basin. Regarding the broad eastern Mediterranean

region, which includes part of the central Mediterranean (Italian Peninsula and Libya), and

eastern Mediterranean (Balkan Peninsula, western Turkey and Cyprus)? These two areas have

contrasting precipitation trends, with the western part showing positive trends towards increased

precipitation, larger precipitation total amounts and increases in intense rainfall events. In

contrast, the easternmost side reveals generally negative trends indicating tendencies towards a



drying climate over time. This was seen especially in the southern coastal and island stations,

which present large positive and significant trends in the maximum number of consecutive dry

days (CDD) index (Kostopoulou and Jones, 2005).

1.2 Climatic datasets and analyzing methodology

Recent weather station network in Libya has been established by the end of the 1950’s

when the standard regulations of WMO (World Meteorological Organization) related of the

Instrument and Method of Observations has been applied. It is comprised of about 50 stations (

source: Libyan National Meteorological Center), 19 stations only selected for recent

investigation that are considered not to have been highly influenced by urbanization and have

continuous and more reliable climatic records from 1961 onward. Long-term changes in air

temperature and rainfall in Libya have been analyzed using observational records of 50 years

since 1961-2010 (source: Climate & Climate Change Department-Libya). Table (1.1) lists the

meteorological stations whose climatic data are used to derive monthly, seasonal and annual

trends, anomalies and the variability of the following series:

(Maximum temperature, Minimum temperature, Average temperature , Diurnal

temperature range , Rainfall totals, and Rainy days number . ). Figure (1.1) is depicted all examined stations geographically; current study considers the

following zonal dividing for simplifying of description different climatic and statistical variables

at spatial scale:

North part composed of Nalut, Zuara, Yefren, Tripoli airport, Misurata, Sirt, Agedabia,

Benina, Shahat, Derna and Tubruk stations.

South part composed of Ghadames, Ghariat, Hon, Jalo, Gegbub, Sebha, Tazerbo and

Kufra stations.

At temporal scale, annual statistics are the statistics of all 12 months from a respective year; the

seasons are defined as follows: winter is the statistics through (December-January-February);

spring through (March-April-May); summer through (June-July-August); and autumn through

(September- October-November).



Stations Latitude

(N) Longitude

(E) Elevation (m)above

m.s.l

Agedabia 30 43 20 10 7

Benina 32 05 20 16 130

Derna 32 47 22 35 86

Gagbub 29 45 24 32 -1

Ghadames 30 06 09 29 346

Ghariat 30 23 13 35 497

Hon 29 07 15 57 263

Jalo 29 01 21 32 45

Kufra 24 13 23 18 436

Misurata 32 19 15 03 32

Nalut 31 52 10 59 621

Sebha 27 01 14 27 432

Shahat 32 48 21 53 649

Sirt 31 12 16 35 13

Tazerbo 25 40 21 05 261

Tripoli airport 32 40 13 09 81

Tubruk 32 06 23 56 50

Yefren 32 05 12 33 691

Zuara 32 53 12 05 7

Table (1.1) Meteorological Stations Identification



Figure (1.1) Geographical locations of studied meteorological stations-Libya

1.2.1 History and objectives

El Tantawi (2005) and El Kenawy (2008) are only just whose addressed and studied

the variability of important climatic parameters (temperature and rainfall) and climate change

pattern over Libya of the second half of last century (20th), and concluded valued findings for

that period, however the pace of climate change has been experienced respective increasing

especially during both of the last 30 years and last decade which represents the first decade of the

twenty-first century (WMO 2011).

Regionally the south of the Mediterranean still also experienced a clear shortage related

to the climate variability and climate change studies. In order to get better understanding and

comprehensive knowledge to date regarding to the temperature and rainfall behaviors and

characteristics over Libya present investigation has been carried out which will play a major role

and contribute for more diagnostic distribution trends of investigated time series at national and

regional scales at last 50 years and near future according IPCC projections, in addition trying to

assess the impacts of climate change on agriculture in north western of Libya. In particular this



study covers more spatial national scale, more quality instrumental datasets and examined the

hottest globally decade (2001-2010) for the first time over Libya.

1.2.2 Data homogenization

All investigated time series for monthly data (Temperature and Rainfall) for stations

under the study (19 stations) were tested by (RHtestsV3) software packages for data

homogenization. This RHtestsV3 software package is used to detect, and adjust for, multiple

change points (shifts) that could exist in a data series that may have first order autoregressive

errors. It is based on the penalized maximal t test (Wang et al. 2007) and the penalized maximal

F test (Wang 2008b), which are embedded in a recursive testing algorithm (Wang 2008a), with

the lag-1 autocorrelation (if any) of the time series being empirically accounted for. The problem

of uneven distribution of false alarm rate and detection power is also greatly alleviated by using

empirical penalty functions (Wang et al. 2007, Wang 2008b). The time series being tested may

have zero-trend or a linear trend throughout the whole period of the record. A homogenous time

series is well correlated with the base series which may be used as a reference series. However,

detection of change points is also possible with the RHtestsV3 package when a homogenous

reference series is not available.

The RHtestsV3 package includes: (1) provision of Quintile-Matching (QM) adjustments

(Wang et al. 2010) in addition to the mean-adjustments; (2) choice of the segment to which the

base series is to be adjusted (referred to as the base segment); and (3) choices of the nominal

level of confidence at which to conduct the test.Only time series (1961-2010) of temperature data

of six studying stations (Misurat, Nalut, Sebha, Sirt, Tazerbo and Yefre) were detected not

homogenous those above methods applied to re-adjust targeted non homogenous time series to

be homogenous series. Time series of rainfall data (1961-2010) of all 19 stations are founded

homogenous then no need for re-adjustment.

1.2.3 Detecting trends

An Excel template MAKESENS (Mann-Kendall test for trend and Sen’s slope estimates)

is used for detecting and estimating trends in the time series of the monthly, seasonal and annual



values of the study climatic data (temperature and rainfall) for the period 1961-2010, developed

by (Timo Salmi at el. 2002).

MAKESENS performs two types of statistical analyses. First the presence of a monotonic

increasing or decreasing trend is tested with the nonparametric Mann-Kendall test. Secondly, the

slope of a linear trend is estimated by the nonparametric Sen’s method (Gilbert 1987). These

methods are used in their basic forms; the Mann-Kendall test is suitable for cases where the trend

may be assumed to be monotonic and thus no seasonal or other cycle is present in the data. The

Sen’s method uses a linear model to estimate the slope of the trend and the variance of the

residuals should be constant in time. These methods offer many advantages that have made them

useful in analyzing climatic data. Missing values are allowed and the data need not conform to

any particular distribution. Besides, the Sen’s method is not greatly affected by single data errors

or outliers.

The Mann-Kendall test is applicable in cases when the data values xi of a time series can

be assumed to obey the model

Where f (t) is a continuous monotonic increasing or decreasing function of time and the

residuals εi can be assumed to be from the same distribution with zero mean. It is therefore

assumed that the variance of the distribution is constant in time.

We test the null hypothesis of no trend, Ho, i.e. the observations xi are randomly ordered

in time, against the alternative hypothesis, H1, where there is an increasing or decreasing

monotonic trend. In the computation of this statistical test MAKESENS exploits both the so

called S statistics given in Gilbert (1987) and the normal approximation (Z statistics). For the

study time series (50 data points) the normal approximation is used.

If n is at least 10 the normal approximation test is used. However, if there are several tied

values (i.e. Equal values) in the time series, it may reduce the validity of the normal

approximation when the number of data values is close to 10.



First the variance of S is computed by the following equation which takes into account

that ties may be present:

Where q is the number of tied groups and tp is the number of data values in the pth group. The

values of S and VAR(S) are used to compute the test statistic Z as follows

0

0

(1.3)

The presence of a statistically significant trend is evaluated using the Z value. A positive

(negative) value of Z indicates an upward (downward) trend. The statistic Z has a normal

distribution. To test for either an upward or downward monotone trend (a two-tailed test) at α

level of significance, H0 is rejected if the absolute value of Z is greater than Z1-α/2, where

Z1-α/2 is obtained from the standard normal cumulative distribution tables. In

MAKESENS the tested significance levels α are 0.001, 0.01, 0.05 and 0.1.

For the five tested significance levels the following symbols are used in the present study

as shown in the below table:

Sign. *** If trend at α = 0.001 level of significance (within 99.90% confidence

intervals) ** If trend at α = 0.01 level of significance (within 99% confidence intervals) * If trend at α = 0.05 level of significance (within 95% confidence intervals) + If trend at α = 0.1 level of significance (within 90% confidence intervals)

Where

- If the trend at α > 0.1 level of significance (within 95% confidence intervals)



To estimate the true slope of an existing trend (as change per year) the Sen's

nonparametric method is used. The Sen’s method can be used in cases where the trend can be

assumed to be linear. This means that f (t) in equation (1.1) is equal to

f(t)= Q t +B (1.4)

Where Q is the slope and B is a constant. To get the slope estimate Q in the equation

(1.4) we first calculate the slopes of all data value pairs

If there are n values xj in the time series we get as many as N = n(n-1)/2 slope estimates

Qi. If there are n values xj in the time series we get as many as N = n (n-1) /2 slope estimates Qi.

The Sen’s estimator of slope is the median of these N values of Qi. The N values of Qi are

ranked from the smallest to the largest and the Sen’s estimator is

/

/ (1.6)

A 100 (1-α)% two-sided confidence interval about the slope estimate is obtained by the

nonparametric technique based on the normal distribution. The method is valid for n as small as

10 unless there are many ties. The procedure in MAKESENS computes the confidence interval

at two different confidence levels; α = 0.01 and α = 0.05, resulting in two different confidence

intervals. At first we compute

Where VAR(S) has been defined in equation (1.2) and Z1-α/2 is obtained from the

standard normal distribution.



Next M1 = ( N - Cα ) /2 and M2 = ( N + Cα )/2 are computed. The lower and upper limits

of the confidence interval, Qmin and Qmax, are the M1 the largest and the (M2 +1)th largest of

the N ordered slope estimates Qi. If M1 is not a whole number the lower limit is interpolated.

Correspondingly, if M2 is not a whole number the upper limit is interpolated.

To obtain an estimate of B in the equation (1.4) the n values of differences xi – Qti are

calculated. The median of these values gives an estimate of B (Sirois 1998). The estimates for

the constant B of lines of the 99% and 95% confidence intervals are calculated by a similar

procedure.

Trends of all different temperatures (max., min., average, and rang temp.), and rainfall

(total rainfall and number of rainy days) time series over Libya (19 stations) were computed

from the available data, from 1961-2010, as a long-term trend. Trend during this study has been

presented as a rate of change per decade (10* Q/decade).

1.3 Agricultural production in Libya

The total area of Libya is estimated at 176 million ha. The area suitable for cultivation

approximates 2.2 million ha of which 239,000 ha dedicated to irrigated agriculture and 1.55

million ha to rain fed farming, in addition to 14 million ha of forest and range lands. Libya has a

population of 5.5 million, with a population growth rate of 4% per year (the highest in Africa); it

is estimated that 14% of the population work in the agricultural sector. The nation's population is

highly concentrated (almost ninety-percent) along its Mediterranean coast. Allocation to

agriculture in the last two decades is estimated at 757.5 million Libyan Dinars per year (2,272

million US$) of which 4.8 million US$ for agricultural research. Food security is one of the most

important issues of Libyan agricultural policy which aims at least to reach self-sufficiency for

some agricultural products which contribute largely in the diet of most of the country population

thus entailing a decrease in food imports. In Libya, although the authorities has made many

efforts towards increasing the quality of agricultural production bearing in mind that the aim is to

achieve self-sufficiency through a long-run food policy agriculture has to face a variety of

constraints which must be tackled.



1.3.1 Libya production potential

Libya extends from latitude of about 200N, deep in the Sahara, to the Mediterranean

coastline. The climate of the country is almost everywhere arid, except for a string of oases and

some small areas near the Mediterranean. The agricultural land of Libya is largely confined to

these locations. Over nine tenths of the populations concentrated in the coastal zone of the

province of Tripolitania, mostly within 300 km radium of Tripoli, and in the coastal zone of the

province of Cyrenaica, to the north of Jabal al Akhdar. The rest of Libya is a vast desert

hinterland where people and agriculture are found only in the widely scattered oases. Arable land

covers about 1.8 m ha, roughly I percent of the total land area of Libya. Wheat output during

1983-5 averaged about 180,000 tons from about 260,000 ha, giving an average yield of nearly

700 kg/ha, among the smallest in Africa. In 1984, Libya imported 670,000 tons of wheat costing

US$ 140 m, to feed a population of 3.5 m.

1.3.2 Physical Landscape

The land is largely barren. Ninety-three percent of the country's land is classified as either

arid or semi-arid. Four percent is classified as suitable for pasture, one to two percent is

categorized as arable, and about one percent is forested. Deserts, principally the Sahara,

comprise the vast majority of the country's extent. The desert is predominately comprised of

sand, sand dunes, or rock, and all three are agriculturally useless. With the absence of permanent

rivers (unlike its neighbor Egypt, blessed with the Nile), only small and scattered oases interrupt

the vast human and agricultural void throughout the country's central and southern expanse. The

largest and most important oasis is Kufra, in the southeast. It is situated above a large aquifer,

allowing for limited agriculture production and several settlements (FAO database).

1.3.3 Soils

The commonest soils of the coastal belt are xerosols, the typical soils of drier

Mediterranean climates. They are inherently fertile, but farming is difficult because of drought

and the stony nature of the terrain. In the west of Libya, the area of the coastal plain around

Tripoli has coarse- to medium-textured calcic xerosols. These are associated with lithosols,

calcaric f luvisols, calcaric regosols and cambic arenosols. Except for the lithosols, which are too



shallow for arable farming, the rest of these soils are moderately to very fertile and are suitable

for wheat Difficulties arise in some areas because of coarse textures and insufficient depth (lithic

phase). The western coast of the Gulf of Sirte, from Misurata to Bulayrat al Hasun, has salt-

affected, clayey soils (orthic solonchaks).

The southern coast of the Gulf from Bulayrat al Hasun to Marsa al Brega has loamy

gypsic xerosols associated with calearic regosols and solonchaks. These soils are shallow and are

only marginally suitable for wheat. The eastern coast of the Gulf from Bulayrat al Hasun to Ras

al Hilal has loamy calcaric fluvisols.

Associated with these are chromic cambisols, calcic xerosols and solonchaks. All these

soils are inherently fertile but the solonchaks would have to be reclaimed before they could be

cultivated. In the Libyan Desert the commonest soils are stony yermosols. Associated with them

are lithosols, shifting and stabilized sand dunes, and rock debris and desert detritus. The desert

soils are far too dry to have any potential for agriculture.

1.3.4 Wheat production potential

The suitable area for rain-fed cereal production in Libya is variously estimated between

500,000 ha and 800,000 ha. Wheat and barley compete for this limited area. In 1984 the total

area under cereals was 484,000 ha, of which wheat occupied 257,000 and barley 214,000 ha.

Between 1974-6 and 1983-5, the average area devoted to barley shrank from 374,000 ha to

215,000 ha, while output diminished from 178,000 tons to 123,000 tons. During the same period

the average area under wheat increased from 210,000 ha to 260,000 ha, and production grew

from 82,000 tons to 181,000 tons.

Wheat output has tended to increase since 1974-6. The maximum production achieved

during the period to 1985 was 210,000 tons from 248,000 ha in 1983, giving an average yield of

846 kg/ha. About 15 percent of the wheat area is under sprinkler irrigation, where the average

yield is about 2.8 tons/ha. In general, however, Libya's potential for wheat production is

constrained by a scarcity of suitable soils, by insufficient and unreliable rainfall and by limited

groundwater resources. One reason for the small yields of rainfed wheat in parts of Libya is that

wheat cultivation has been extended to areas which receive less than 250 mm of rainfall



annually. Libya's best wheat land lies north-east of Benghazi. Most of this land lies on the

plateau of the Jabal al Akhdar hills; the rest is on a narrow strip of coastal lowland to the north

fig (1.2). The area receives 250-500 mm of annual rainfall which falls mostly during wheat

growing season. It has medium to low potential for rainfed wheat and is delineated as mapping

unit P2ec table (1.2).

Nearly four fifths of these mapping units are on the plateau. Of this plateau section, three

fifths are on nearly flat to undulating terrain with fine-textured soils which crack widely in

summer (vertic cambisols) and on gently to moderately sloping terrain with medium-textured,

partly gravelly, reddish-brown soils (chromic luvisols). These soils are fairly fertile, but in some

places erosion and unreliable rainfall limit wheat production. They have medium potential (P2ec)

for rain fed wheat.

The remaining two fifths of the plateau section of the mapping unit have steep to very

steep terrain and very shallow, gravelly soils (lithosols) with no potential (N) for wheat

production. The coastal lowland strip, 20 km wide and taking up just over a fifth of the mapping

unit, consists mainly of nearly flat land with mediu-textured, stratified alluvial soils (calcaric

fluvisols); this strip has medium potential (P2c) for rainfed wheat.

Designation Description ExtentLand potential sub-

class Extent

P2ec Land with medium potential,

limited by aridity and erosion risks1087

P2ec

P2c

N

554

163

370

Table (1.2) land potential sub-classes by mapping unit in '000 ha for Libya. (Source FAO database)



Figure (1.2) Wheat production potential in Libya (Source FAO database)



Chapter two

Climate change trends



2.1 Introduction

The climate is always changing and has forever been a hot topic of discussion at all

levels. In the late 20th century, the natural sciences have increasingly focused on the problems

and risks of modern societies. Climate change is considered as the most serious environmental

challenge that threatens the developed and less developed countries. It has reached a critical

magnitude with a serious impact on society, human welfare and quality of human life. So, the

impact on the environment, food security, and socioeconomic systems, at the present time, is

seriously taken into consideration by international authorities and has been receiving

considerable recent attention from governments.

Climate change is every deviation from the normal, having significance according to the

actual use of statistical tests. It seems to be very difficult to put apart man-induced changes from

natural ones as the natural changes are not yet well understood. In addition to changes of climate,

there are other terms for describing climate (e.g. Variability, Anomaly, Trends, Oscillation,

Periodicity and Fluctuation). Climate variability means the variability inherent in the stationary

stochastic process approximating the climate on the scale of a few decades. Climatic variations

involve changes in the magnitude of the annual or decadal values and the mean is constant, while

in climatic changes, both the mean and the variance are changing with time. In climate science,

an anomaly is a deviation of a meteorological variable from the normal (mean) value. The term

trend denotes climate change characterized by a smooth, monotonic increase or decrease of

average values over the period of record.

According to the IPCC (Intergovernmental Panel on Climate Change) Fourth Assessment

Report (AR4, 2007), palaeoclimatic information supports the interpretation that average northern

hemisphere temperatures during the second half of the 20th century were likely the highest in at

least the past 1,300 years, and most of the observed increase in global average temperatures since

the mid-20th century is very likely due to the observed increase in concentrations of

anthropogenic greenhouse gases. Otherwise, annual precipitation (for land areas only) has varied

periodically since 1880. Meanwhile, the Northern Hemisphere saw large amounts of rainfall

around 1930 and in the 1950s.



2.2 Trends of maximum temperature (1961-2010)

By examining of Table (2.1) shows the following:-

In winter, all stations have experienced non-significant trend except at 6 stations which

reported significant trend where 3 of them indicated positive trend and another indicated

negative one, whilst Misurat in the middle coast experienced highest positive trend

(0.32oC/decade) and Tazerbo in the south east experienced highest negative trend (-

0.33oC/decade). While in during spring season one station reported significant negative trend (-

0.53oC/decade), in Tubruk which located at the far of the east coast, whilst 9 stations experienced

significant positive trend, it is clear that western places have stronger upward tendency than

eastern places. In both in summer and autumn seasons significant trends are clearly observed

more spatially distribution than previous seasons, strongest significant positive and negative

trends occurred in the summer were (0.43oC/decade) and (-0.61oC/decade) and reported at

Misurata and Tubruk respectively, while in autumn season all 14 stations showed strong

evidence of a significant positive trend and seem that it has more coherent series compared to

other seasons.

During annual spatial scale warm pattern prevailed at 11 significant series, which

essentially controlled by autumn and summer season patterns, whilst only 2 series have negative

significant trends at Benina and Tubruk which mainly resulted due to the relative high variability

at the inter-seasonal scale and strong downward trend at spring and summer seasons respectively.

On the contrary, cold and non-significant patterns dominated during winter season.

In general the average trend of annual maximum temperature (0.14oC/decade) for 1961-

2010 corresponds to 1946-2000 trends over Libya (Tantawi 2005). However, this behavior

comes in general agreement with the observations in the north, west and east of the

Mediterranean Basin, e.g., Italy (Brunetti et al. 2005), the Iberian Peninsula (M. Brunet et al.

2007), and the Turkey-Istanbul (Karaburun et al. 2011).

2.3 Trends of minimum temperature (1961-2010)

By examining of Table (2.2) shows the following:-



Station Winter Spring Summer Autumn Annual

Agedabia 0.16+ 0.18+ 0.14+ 0.35*** 0.22***

Benina -0.08- -0.08- -0.12- 0.00- -0.09+

Derna -0.15- -0.01 - 0.03- 0.10- -0.01-

Gagbub 0.15+ 0.17+ 0.30*** 0.39*** 0.20***

Ghadames 0.10- 0.33** 0.30*** 0.38** 0.28***

Ghariat -0.20- 0.10- 0.04- 0.38*** 0.08-

Hon -0.03- 0.28* 0.17* 0.43*** 0.19***

Jalo -0.11- 0.12- 0.22** 0.33** 0.14*

Kufra -0.04 - 0.13 - 0.15+ 0.35** 0.20*

Misurata 0.32** 0.56*** 0.43*** 0.59*** 0.52***

Nalut 0.11- 0.44** 0.32** 0.33*** 0.30***

Sebha -0.21- 0.02- 0.02- 0.17- -0.01-

Shahat -0.19+ 0.02- 0.12-- 0.19+ 0.00-

Sirt 0.04- 0.26* 0.30*** 0.41*** 0.25***

Tazerbo -0.33* -0.03- 0.02- 0.22* 0.00-

Tripoli airport 0.10- 0.38* 0.30** 0.41*** 0.29***

Tubruk 0.12- -0.53*** -0.61*** 0.07- -0.21**

Yefren -0.30* -0.05- -0.04- -0.10- -0.10-

Zuara 0.14- 0.33** 0.31** 0.38*** 0.32***

Average -0.02 0.14 0.13 0.28 0.14

S.D 0.18 0.24 0.23 0.17 0.18

Table (2.1) Trends of Maximum Temperature (oC/decade), by Mann- Kendall Sen's test (1961-2010)



There is a general tendency for the warming trend in annual and seasonal minimum

temperature. It is evident that the observed trends are either positive (as shown in the majority of

stations) or non-significant as presented in a few stations (i.e., Shahat, Sirt, Tazerbo and Tripoli

airport, in winter). Also, of particular concern is the fact that Misurata is the only station showing

a negative trend, especially during the spring season? It is also worth mentioning that Tubruk

station that located at the far eastern coast has recorded highest ascending significant trend in

autumn and in annual of time scales which is mainly attributed to the prevailing of the second

warmest trend observed over the north of Egypt during 1948-2003 (Baruch Ziv et al. 2005). No

differences are observed between the coastal and inland stations in response to both autumn and

annual minimum temperature variability. Nevertheless, warming trends in whole stations are less

much marked in winter except of Ghariat and Tubruk, and more coherent in summer and

autumn.

Annual trends of minimum temperature are evidently positive in most of the stations.

Overall, the minimum surface temperature has risen at an average rate of 0.46oC/decade, which

is almost three times as large as the rate of maximum temperature. Seasonal trend analyses

reveals that most of the warming is found in autumn and summer (0.59oC/decade) and

(0.54oC/decade) respectively. Regionally, the increase in the minimum temperature seems to be

more coherent over the whole Mediterranean area than the observed for maximum temperature

as has been demonstrated in several studies (e.g. C. Simolo et al. 2011; M. Brunet et al. 2007).

2.4 Trends of average temperature (1961-2010)

Average temperature for annual and seasons base of different periods is shown in

(Appendix-A). According to Table (2.3), positive significant trends (warming pattern) of the

mean annual temperature were observed at all studied stations. The trends ranged between 0.08

and 0.50°C/decade at Benina which is represented lowest trend and Hon respectively. In winter,

non-significant trends could be generally identified and solely Shahat station shows a downward

but non-significant trend. In spring and summer, a significant upward trend is observed in most

time series. In autumn, whole study stations show strong upward tendency trends. In particular,

most series experienced significant and stronger warming summer and autumn over the study

period in comparison to weak and non-significant winter and spring trends. Moreover, it is clear

that the mean annual temperature experienced high inter-seasonal variability




Agedabia 0.25** 0.41*** 0.76*** 0.67*** 0.58***

Benina 0.19* 0.26+ 0.39*** 0.26*** 0.00***

Derna 0.26*** 0.35*** 0.52*** 0.47*** 0.39***

Gagbub 0.15* 0.18* 0.42*** 0.50*** 0.28***

Ghadames 0.30** 0.71*** 0.81*** 0.73*** 0.69***

Ghariat 0.54*** 0.29+ 0.63*** 0.77*** 0.53***

Hon 0.57*** 0.64*** 0.80*** 0.95*** 0.77***

Jalo 0.27** 0.31*** 0.53*** 0.56*** 0.41***

Kufra 0.65*** 0.69*** 0.81*** 0.96*** 0.80***

Misurata -0.09- -0.11+ -0.04- 0.04- -0.05+

Nalut 0.28** 0.41*** 0.45*** 0.50*** 0.41***

Sebha 0.44*** 0.46*** 0.57*** 0.58*** 0.49***

Shahat 0.08- 0.16+ 0.37*** 0.30** 0.25***

Sirt 0.04- 0.12+ 0.32*** 0.40*** 0.22***

Tazerbo 0.17- 0.31* 0.33*** 0.46*** 0.36***

Tripoli airport 0.08- 0.21** 0.44*** 0.35*** 0.29***

Tubruk 0.94*** 0.79*** 0.85*** 1.27*** 0.97***

Yefren 0.48*** 0.59*** 0.73*** 0.63*** 0.62***

Zuara 0.53*** 0.53*** 0.65*** 0.81*** 0.66***

Average 0.32 0.38 0.54 0.59 0.46

S.D 0.25 0.23 0.23 0.29 0.26

Table (2.2) Trends of Minimum Temperature (oC/decade), by Mann- Kendall Sen's test (1961-2010)



Generally, mean annual temperature over study period has spatially average of

(0.31°C/decade) which is mainly controlled strongly by autumn and summer temperatures and

caused for such that high annual warming. The overall warming is also associated with higher

rates of change for minimum temperature than the maximum temperature (0.46 versus

0.14°C/decade). In brief, study period located under enhanced of greenhouse warming

conditions. According to the Climate Change Monitoring Reports (2004-2011), global average

temperatures have varied on different time scales ranging from a few years to several decades.

On a longer time scale, global average surface temperatures have risen at about 0.68°C since

1891 to 2010 (the earliest date for which instrumental temperature records are available), surface

temperatures over the northern hemisphere have risen at about 0.71°C during the same period.

This long-term trend can be attributed to global warming caused by increased concentration of

greenhouse gases such as CO2. Northern hemisphere annual temperature trend equal roughly 4

times of Libyan annual temperature trend and about 3 times of previous Libyan trend (El

Kenawy et al. 2008), which is resulted out of using different space and time scales between

these reports and study respectively as well as this study is involved in its investigation the

warmest decade (2001-2010) since last 100 years at least (WMO 2011). Conversely, it is

obvious that at regional scale the Libyan temperature trend seems lower than that found over

Spain by 55% for the period of 1973-2005 (M. Brunet et al. 2007).

Trend of Mediterranean temperature patterns not uniform over the region, for instance,

increases can be identified in the western Mediterranean area, whereas an opposite trend

becomes apparent in the eastern Mediterranean basin (E.Hertig et al., 2010). In brief, the recent

studies provide some degree of consistency between the Libyan trend and other regional and sub-

regional trends.

Inter-annual variability which is generally much very smaller than for rainfall as

temperature (Fig 2.2) is controlled more dominantly by the seasonal solar variations. The timing

of the warm and cool seasons is largely dominated by the general summer/winter cycle.

Nationally a very large seasonal cycle (typically inlands and highlands) and a very small

seasonal cycle (typically coastal places) variation of annual temperature characterize the general

behavior of the temperature parameter pattern. While minimum temperatures typically track the




Agedabia 0.22** 0.33*** 0.36*** 0.50*** 0.38***

Benina 0.03- 0.06- 0.07- 0.19- 0.08*

Derna 0.06- 0.17- 0.28*** 0.31*** 0.20***

Gagbub 0.13+ 0.17* 0.36*** 0.43*** 0.25***

Ghadames 0.20- 0.47*** 0.52*** 0.56*** 0.44***

Ghariat 0.26- 0.28* 0.41*** 0.64*** 0.44***

Hon 0.26** 0.48*** 0.47*** 0.67*** 0.50***

Jalo 0.03- 0.20* 0.35*** 0.40*** 0.26***

Kufra 0.29* 0.42*** 0.49*** 0.69*** 0.49***

Misurata 0.12- 0.21* 0.19** 0.30*** 0.23***

Nalut 0.14- 0.43*** 0.39*** 0.41*** 0.35***

Sebha 0.13- 0.26* 0.30*** 0.38** 0.23***

Shahat -0.06- 0.08- 0.28*** 0.25** 0.15**

Sirt 0.08- 0.22* 0.32*** 0.40*** 0.25***

Tazerbo 0.05- 0.25* 0.40*** 0.47*** 0.31***

Tripoli airport 0.09- 0.31** 0.36*** 0.37*** 0.29***

Tubruk 0.56*** 0.10+ 0.11- 0.71*** 0.38***

Yefren 0.08- 0.26* 0.34*** 0.27*** 0.24***

Zuara 0.37*** 0.44*** 0.48*** 0.58*** 0.49***

Average 0.16 0.27 0.34 0.45 0.31

S.D 0.14 0.13 0.12 0.16 0.12

Table (2.3) Trends of Average Temperature (°C/decade), by Mann- Kendall Sen's test (1961-2010)



Figure (2.1) Trends of Average Temperature (°C/decade) for period (1961-2010)

Climate Cha

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m linear tren

1987

1990

1993

1996

t"

1987

1990

1993

1996

ature from 1

each year,

nds

1996

1999

2002

2005

1996

1999

2002

2005

1961 to 201

the blue lin

2005

2008

2011

2005

2008

2011

June2012

10; for base

nes indicate

2

e

e

Climate Cha

Khalid Ibrah

Figure

line (19

five-yea

ange over Libya &

him Elfadli

(2.2) (conti

981 – 2010

ar means, an

‐2‐2‐1‐1‐0

01122

& Impacts on Agr

inued) Annu

) average. T

nd the red li

1960

1963

.5°C

.0°C

.5°C

.0°C

.5°C0°C.5°C.0°C.5°C.0°C.5°C

riculture

ual anomali

The bars in

ines indicat

1966

1969

1972

1975

31

ies in avera

ndicate anom

te long-term

1975

1978

1981

1984

"Misurat

age tempera

malies for

m linear tren

1987

1990

1993

ta"

ature from 1

each year,

nds

1996

1999

2002

2005

1961 to 201

the blue lin

2005

2008

2011

June2012

10; for base

nes indicate

2

e

e

Climate Cha

Khalid Ibrah

Figure

line (19

five-yea

ange over Libya &

him Elfadli

(2.2) (conti

981 – 2010

ar means, an

‐2

‐2

‐1

‐1

‐0

0

1

1

2

2

‐2

‐2

‐1

‐1

‐0

0

1

1

2

2

& Impacts on Agr

inued) Annu

) average. T

nd the red li

1960

1963

.5°C

.0°C

.5°C

.0°C

0.5°C

0°C

0.5°C

.0°C

.5°C

.0°C

.5°C

1960

1963

2.5°C

2.0°C

1.5°C

1.0°C

0.5°C

0°C

0.5°C

1.0°C

1.5°C

2.0°C

2.5°C

riculture

ual anomali

The bars in

ines indicat

1966

1969

1972

1975

1966

1969

1972

1975

32

ies in avera

ndicate anom

te long-term

1975

1978

1981

1984

"Shahat

1975

1978

1981

1984

"Sirt"

age tempera

malies for

m linear tren

1987

1990

1993

t"

1987

1990

1993

ature from 1

each year,

nds

1996

1999

2002

2005

1996

1999

2002

2005

1961 to 201

the blue lin

2005

2008

2011

2005

2008

2011

June2012

10; for base

nes indicate

2

e

e



Figure (2.2) (continued) Annual anomalies in average temperature from 1961 to 2010; for base

line (1981 – 2010) average. The bars indicate anomalies for each year, the blue lines indicate

five-year means, and the red lines indicate long-term linear trends

1960

1963

1966

1969

1972

1975

1978

1981

1984

1987

1990

1993

1996

1999

2002

2005

2008

2011

‐2.5°C

‐2.0°C

‐1.5°C

‐1.0°C

‐0.5°C

0°C

0.5°C

1.0°C

1.5°C

2.0°C

2.5°C"Tazerbo"

1960

1963

1966

1969

1972

1975

1978

1981

1984

1987

1990

1993

1996

1999

2002

2005

2008

2011

‐2.5°C

‐2.0°C

‐1.5°C

‐1.0°C

‐0.5°C

0°C

0.5°C

1.0°C

1.5°C

2.0°C

2.5°C"Tripoli airport"

1960

1963

1966

1969

1972

1975

1978

1981

1984

1987

1990

1993

1996

1999

2002

2005

2008

2011

‐2.5°C‐2.0°C

‐1.5°C

‐1.0°C

‐0.5°C0°C

0.5°C

1.0°C1.5°C

2.0°C

2.5°C"Tubruk"



Figure (2.2) (continued) Annual anomalies in average temperature from 1961 to 2010; for base

line (1981 – 2010) average. The bars indicate anomalies for each year, the blue lines indicate

five-year means, and the red lines indicate long-term linear trends

1960

1963

1966

1969

1972

1975

1978

1981

1984

1987

1990

1993

1996

1999

2002

2005

2008

2011

‐2.5°C

‐2.0°C‐1.5°C

‐1.0°C

‐0.5°C

0°C0.5°C

1.0°C

1.5°C

2.0°C2.5°C

"Yefren"

1960

1963

1966

1969

1972

1975

1978

1981

1984

1987

1990

1993

1996

1999

2002

2005

2008

2011

‐2.5°C

‐2.0°C

‐1.5°C

‐1.0°C

‐0.5°C

0°C

0.5°C

1.0°C

1.5°C

2.0°C

2.5°C"Zuara"



Tables (2.4) present a summary of the anomalies variability of annual temperature

average of (1961-2010) for different baseline periods of all stations, which is computed

according to the following formula;

.. .

, where

A.V = anomalies variability A.S.D = anomalies standard deviation AVG (baseline) = average of annual temperature for the identified period

There is a general agreement between all stations that baseline average period of (1981-

2010) can be considered as a lowest variability average and more steady than other baseline

periods in particular the timing of global warming has been started in the mid of the 1980’s

(WMO 2011), for that reason it seems that acceptable to be as idealism period presented in (Fig

2.1) for calculating the annual temperature anomalies for all stations in order of updating trends

of recent anomalies.

Anomalies variability ranging over Libya between 1.89 to 3.84% in Benina and

Ghadames respectively, this means spatially there is no remarkable significance for considering

of hottest or coldest years depending on the baseline period (1981-2010).

Trough Figure (2.1), it seems that 2010 has been the hottest year in Libya at least since

1961 with a value of 1.36°C± 0.56°C above of the period 1981-2010 annual average of 21.14°C ,

also obvious that in most stations the last 10 years (20001-2010) recorded higher average over

any decade during the study period.

These findings are supported by what have been observed globally where the year 2010

was especially notable in that global surface temperature reached record values at the same level

as in 1998 and 2005, consistent with the acceleration of the warming experienced over the last 50

years. 2010 ranks as the world’s second warmest year, with the difference between it and 2005

within the margin of uncertainty, and was estimated to be 0.53°C ± 0.09°C above the 1961–1990

annual average of 14°C.

The year also signaled the close of the warmest decade on record. Over this decade,

warming was markedly more pronounced in some regions, notably so in North Africa and the



Arabian Peninsula, South Asia and the Arctic. Recent warming has been especially strong in

Africa; temperatures averaged over Africa were 1.29°C above the long-term average (1961–

1990). Mediterranean region also had its warmest year on record equaling its previous warmest

year (WMO 2011). Further to that and based on the new baseline average (1981-2010) Libya has

been experiencing recent warming by the end of 1990’s with about a 10 years delay of global

warming starting which based on long-term average of (1961-1990).

2.5 Trends of diurnal temperature range (1961-2010)

Table (2.5) shows the sharp decrease in the DTR which is a spatially uniform pattern of

significant negative trends across the country at both seasonal and annual timescales. All the

stations show negative (except Misurata) and significant (except Gagbub, Sirt and Tripoli

airport) trends. The descending trend is more coherent in summer with a range between –

1.52°C/decade in Tubruk and 0.48°C/decade in Misurata.

A comparison of Tables 2-2 and 3-2 implies that the downward trend of TR is mainly

caused by the distinct upward trend in minimum temperature, whilst in Misurata the picture

completely opposite where strong upward trend of its maximum temperature is obviously caused

for up warming trend of TR. This result is broadly consistent with the findings of previous

studies over Libya of second half last century (e.g., El Kenawy et al. 2008; El Tantawi 2005) in

which minimum temperature increases faster than maximum temperature, causing a significant

decline in the diurnal range of temperature. At the national level, temperature range (TR) is

generally characterized by a sharp negative trend (0.33°C/decade), as a consequence of the faster

increase in the minimum temperature with respect to the role of maximum temperature.

However, for all seasons, the greatest decrease has occurred in both summer and spring (–

1.52°C/decade, –0.30°C/decade), respectively.



Stations (1961-1990) (1971-2000) (1981-2010)

Agedabia 3.20 3.16 3.09

Benina 1.90 1.90 1.89

Derna 2.50 2.49 2.45

Gagbub 2.45 2.45 2.41

Ghadames 4.02 3.93 3.84

Ghariat 3.82 3.73 3.64

Hon 3.95 3.89 3.78

Jalo 2.64 2.63 2.58

Kufra 3.66 3.61 3.52

Misurata 2.32 2.30 2.27

Nalut 3.72 3.67 3.58

Sebha 2.42 2.40 2.37

Shahat 2.93 2.90 2.87

Sirt 2.91 2.90 2.84

Tazerbo 3.21 3.19 3.13

Tripoli airport 2.84 2.82 2.76

Tubruk 3.73 3.70 3.59

Yefren 3.04 3.00 2.95

Zuara 4.08 4.00 3.88

Average 3.12 3.09 3.02

S.D 0.66 0.63 0.60

Table (2.4) Anomalies Variability (%) for Annual Temperature °C (1961-2010) in Different

Base Line Averages




Agedabia -0.15+ -0.27*** -0.67*** -0.34*** -0.38***

Benina -0.25** -0.36*** -0.44*** -0.40*** -0.42***

Derna -0.34*** -0.30*** -0.46*** -0.32*** -0.40***

Gagbub -0.08- 0.00- -0.16* -0.07- -0.08-

Ghadames -0.24** -0.33*** -0.51*** -0.37*** -0.35***

Ghariat -0.73** -0.17+ -0.58*** -0.39** -0.47***

Hon -0.68*** -0.40*** -0.68*** -0.50*** -0.54***

Jalo -0.37** -0.19* -0.27*** -0.21** -0.24***

Kufra -0.65*** -0.57*** -0.63*** -0.57*** -0.62***

Misurata 0.39*** 0.67*** 0.48*** 0.54*** 0.55***

Nalut -0.27* 0.04- -0.16** -0.10- -0.11*

Sebha -0.65*** -0.47*** -0.56*** -0.40*** -0.53***

Shahat -0.25** -0.22+ -0.29** -0.13+ -0.21*

Sirt -0.15- 0.13- -0.02- 0.07- 0.00-

Tazerbo -0.52*** -0.33*** -0.29*** -0.22* -0.35***

Tripoli airport 0.00- 0.12- -0.15- 0.07- 0.00-

Tubruk -0.83*** -1.30*** -1.52*** -1.12*** -1.14***

Yefren -0.73*** -0.59*** -0.75*** -0.71*** -0.70***

Zuara -0.44*** -0.20* -0.31*** -0.42*** -0.35***

Average -0.37 -0.25 -0.42 -0.30 -0.33

S.D 0.31 0.39 0.39 0.34 0.34

Table (2.5) Trends of Diurnal Temperature Range (°C/decade), by Mann- Kendall Sen's test

(1961-2010)



2.6 Trends of total rainfall (1961-2010)

Total rainfall (Appendix-B) is other important climate parameter in the present study

which has changed during the last 50 years as shown in Tables (2.6) and (2.7), and Figures (2.3)

and (2.4).

The statistical analyzing of reliable instrumental data was demonstrating marked changes

of rainfall at all stations under study over the long-term period 1961-2010.

In annual time scale, negative trends of total rainfall have been observed at most stations.

The highest ratio negative trend was -10.17 % (-27.68 mm) / decade at Tripoli airport (located

about 25 km south west of Tripoli city) with normal of 272.27 mm, while the lowest was -0.24%

(-0.62 mm) / decade at Derna with normal of 260.20 mm. Positive trends were observed at three

stations ranging between 19.22 and 0.79 %/decade. Temporally, the inter-annual rainfall totals

variability’s were very high in majority of stations except at Shahat, Tripoli airport and Tubruk

which shown a high significant trends, in addition, the southern places have much variability

than northern places. Spatially, most stations under study are located in the northern parts of

Libya and little data was available for the southern parts which must be taken into account to

elaborate the spatial changes of rainfall despite they have very low amount of rainfall normals.

Remarkably variations have been observed in distribution of annual rainfall total changes over

Libya.

The trends were negative over whole country except Ghadames, Tubruk and Yefren

where the rainfall has been observed increasingly and only Tubruk has significant trend.

Generally, rainfall decreased in Libya in average of -0.10 % per decade, where two patterns

nationally can be recognized spatially; northern pattern in decreasing average of -1.24% per

decade and southern pattern in increasing average of 1.47 % per decade.

Seasonally, rainfall in Libya occurs in winter, autumn and spring, while summer is dry.

The trends of seasonal rainfall at all stations were computed for winter (December, January,

February), spring (March, April, May) and autumn (September, October, November) over the

period study.

Trends of rainfall totals in winter showed positive trends at most stations ranging

between 19.51% (16.27 mm) and 0.33 % (0.06 mm) per decade. Negative trends prevailed at



only four stations most of them located at the coast zone. All trends were not linear explained by

high rainfall variability in winter. No significant trends were observed at all stations except three

stations (Ghadames, Tubruk and Yefren).

Overall, spatially rainfall distribution indicated that the increasing ratio of northern parts

more pronounced than at southern parts with an average of 3.18% and 1.77 % per decade

respectively. In spring, the pattern of rainfall distribution completely opposite to the winter

season where most of stations experienced downward trends except four stations that indicated

upward trends. Decreasing trends ranged between (-0.68%/decade) and (-11.28%/decade), and

increasing trends ranging between 4.69%/decade and 0.21%/decade.

High inter-annual rainfall variability and not linear trend noticed at all stations except at

two stations (Naught and Tripoli airport) which have enough negative significant spring trends.

In general, spatial rainfall distribution indicated that the negative pattern predominated widely

over the country especially at the northern places with general average of (-1.99%/decade).

In autumn, negative trends of rainfall have been clearly observed at most of stations

expressing decreasing ratio between -2.20 % (-0.88 mm)/decade at Nalut in the northwestern

high lands of Libya and -14.10 % (-12.03 mm)/decade at Tripoli airport in near coast of

northwestern Libya. While positive of ratio trends were recorded at five of stations ranging

between 7.86 % (1.95 mm)/decade at Tubrukt in the far of eastern coast and 0.51 % (0.15

mm)/decade at Aegdabia in middle coast of Libya. Trends of rainfall total in autumn were linear

and significant at four stations which are all located at the coast zone. Since the trend is absent in

most places of the south hence the behavior trend of the north places prevailed as a general

pattern of autumn season with a highest average of -2.15 %/decade among rest of seasons which

in turn controlled completely on annual rainfall pattern time scale over Libya.

Typically the inter-annual variability of rainfall totals is high which are very sensitive to

the regional synoptic systems. Variability can be expressed by anomaly statistical measure in

order to investigate the behavior of annual rainfall totals during the past 50 years in terms of

knowing the general temporal and spatial rainfall pattern each year and its relation to the annual

temperature anomaly depending on predefined baseline period. For this purpose Tables (2.8)

prepared to elaborate some findings for better understanding of observed changes attributes.

From the table it is clear that the anomalies variability], which are computed according to the



below statistical formula, for different overlapping 3 baseline times [(1961-1990), (1971-2000)

and (1981-2010) have no same values and ranking.

.. .

, where

A.V = anomalies variability A.S.D = anomalies standard deviation AVG (baseline) = average of annual rainfall totals for the indentified period

Consequently can also notice that;

Most of stations have higher anomaly variability of last 30 years than another baseline times.

Derna and Tubruk in the eastern coast, Agedabia in the middle coast and Ghadames in North West inland have lower values of anomaly variability of last 30 years than another baseline times.

Highest anomaly variability values prevailed at south parts of Libya.

Highest anomalies presented since beginning of 1980’s in majority stations.

It is also worth mentioning that the anomaly variability of annual rainfall totals is

inversely proportional with annual temperature anomaly variability in particularly during last 30

years (1981-2010).

Rainfall trend analyses, on different spatial and temporal scales, has been of great

concern during the past century because of the attention given to global climate change from the

scientific community: they indicate a small positive global trend, even though large areas are

instead characterized by negative trends (Climate change monitoring report 2010; IPCC,

2007). Over Mediterranean basin countries negative trends of annual rainfall are more

pronounced (e.g. A. DUNKELOH et al. 2003; A. Longobardi et al. 2009; F. LAGET et al.

2008; Brunet M. et al. 2007; Brunetti M. et al. 2004) and Libya is among these countries

which has been observed such that downward trend especially since 1976 (El Tantawi 2005).

The annual precipitation amount (for land areas only) in 2010 was 57 mm above normal

(i.e., the 1971 – 2000 average (Climate change monitoring report 2010). The conversely

situation has been observed over Libya in 2010 (Fig. 2.4), where all amounts of stations were

below normal (except Kufra, Tazerbo and Tubruk) but for the 1981 – 2010 average.




Agedabia 0.86- -0.17- 0.00- 0.15- -1.50-

Benina -6.00- -1.33- 0.00- 1.29- -9.62-

Derna 7.60- -0.24- 0.00- -5.52- -0.62-

Gagbub 0.00- 0.00- 0.00- 0.00- -0.76-

Ghadames 1.48* 0.11- 0.00- 0.44- 4.50*

Ghariat 0.06- -1.22- 0.00- 0.78- -2.00-

Hon -0.02- 0.00- 0.00- 0.00+ -0.20-

Jalo 0.11- 0.00- 0.00- 0.00+ -0.10-

Kufra 0.00- 0.00- 0.00- 0.00- -0.00-

Misurata -3.21- 1.45- 0.00- -1.66- -5.42-

Nalut 2.76- -6.18+ 0.00- -0.88- -12.29-

Sebha 0.00- 0.00* 0.00- 0.00- 0.69-

Shahat -10.52- -2.15- 0.00+ -11.00* -21.53*

Sirt 6.71- 1.09- 0.00- -5.67+ -2.27-

Tazerbo 0.00- 0.00- 0.00- 0.00- 0.00-

Tripoli airport -8.46- -5.22+ 0.00- -12.03* -27.68**

Tubruk 16.27** 0.04- 0.00- 1.95- 24.72***

Yefren 12.67+ -3.83- 0.00- -4.09- 2.05-

Zuara 5.90- -1.07- 0.00- -9.05+ -11.50-

Average 1.38 -0.99 0.00 -2.38 -3.34

S.D 6.52 2.03 0.00 4.27 10.73

Table (2.6) Trends of Total Rainfall (mm/decade), by Mann- Kendall Sen's test (1961-2010)




Agedabia 0.80- -1.02- 0.00- 0.51- -0.98-

Benina -3.48- -3.66- 0.00- 2.31- -3.64-

Derna 4.77- -0.68- 0.00- -8.69- -0.24-

Gagbub 0.00- 0.00- 0.00- 0.00- -4.74-

Ghadames 11.93* 1.19- 0.00- 5.95- 15.95*

Ghariat 0.33- -9.64- 0.00- 4.06- -3.95-

Hon -0.20- 0.00- 0.00- 0.00+ -2.11-

Jalo 2.09- 0.00- 0.00- 0.00+ -1.07-

Kufra 0.00- 0.00- 0.00- 0.00- -0.00-

Misurata -2.28- 3.93- 0.00- -1.69- -1.95-

Nalut 3.07- -11.28+ 0.00- -2.20- -6.60-

Sebha 0.00- 0.00* 0.00- 0.00- 7.66-

Shahat -3.23- -2.20- 0.00+ -8.68* -3.88*

Sirt 6.27- 4.69- 0.00- -9.26+ -1.18-

Tazerbo 0.00- 0.00- 0.00- 0.00- 0.00-

Tripoli airport -6. 10- -10.60+ 0.00- -14.10* -10.17**

Tubruk 19.51** 0.21- 0.00- 7.86- 19.22***

Yefren 10.08+ -5.73- 0.00- -6.62- 0.79-

Zuara 5.61- -3.06- 0.00- -10.26+ -5.01-

Average 2.59 -1.99 0.00 -2.15 -0.10

S.D 6.10 4.46 0.00 5.89 7.20

Table (2.7) Trends of Total Rainfall Ratio (“%”/decade), by Mann- Kendall Sen's test (1961-

2010)



Figure (2.3) Trends of Rainfall Total (mm/decade) for period 1961-2010



Figure (2.4) Annual anomalies in total rainfall ratios from 1961 to 2010, for base line (1981 –

2010) average. The bars indicate anomalies ratios for each year, the blue lines indicate five-year

means, and the red lines indicate long-term linear trends.

1960

1963

1966

1969

1972

1975

1978

1981

1984

1987

1990

1993

1996

1999

2002

2005

2008

2011

‐100

‐50

0

50

100

150

(%)

"Agedabia"

1960

1963

1966

1969

1972

1975

1978

1981

1984

1987

1990

1993

1996

1999

2002

2005

2008

2011

‐100

‐50

0

50

100

150

(%)

"Benina"

1960

1963

1966

1969

1972

1975

1978

1981

1984

1987

1990

1993

1996

1999

2002

2005

2008

2011

‐100

‐50

0

50

100

150

(%)

"Derna"



Figure (2.4) (continued) Annual anomalies in total rainfall ratios from 1961 to 2010, for base line

(1981 – 2010) average. The bars indicate anomalies ratios for each year, the blue lines indicate a

five-year means, and the red lines indicate long-term linear trends.

1960

1963

1966

1969

1972

1975

1978

1981

1984

1987

1990

1993

1996

1999

2002

2005

2008

2011

‐100

0

100

200

300

400

500

(%)

"Gagbub"

1960

1963

1966

1969

1972

1975

1978

1981

1984

1987

1990

1993

1996

1999

2002

2005

2008

2011

‐100

0

100

200

300

400

500

(%)

"Ghadames"

1960

1963

1966

1969

1972

1975

1978

1981

1984

1987

1990

1993

1996

1999

2002

2005

2008

2011

‐100

0

100

200

300

400

500

(%)

"Ghariat"




(1981 – 2010) average. The bars indicate anomalies ratios for each year, the blue lines indicate


1960

1963

1966

1969

1972

1975

1978

1981

1984

1987

1990

1993

1996

1999

2002

2005

2008

2011

‐100

0

100

200

300

400

500

(%)

"Hon"

1960

1963

1966

1969

1972

1975

1978

1981

1984

1987

1990

1993

1996

1999

2002

2005

2008

2011

‐100

0

100

200

300

400

500

(%)

"Jalo"

1960

1963

1966

1969

1972

1975

1978

1981

1984

1987

1990

1993

1996

1999

2002

2005

2008

2011

‐1000

100200300400500600700800900

(%)

"Kufra"




(1981 – 2010) average. The bars indicate anomalies ratios for each year, the blue lines indicate


1960

1963

1966

1969

1972

1975

1978

1981

1984

1987

1990

1993

1996

1999

2002

2005

2008

2011

‐100

‐50

0

50

100

150

(%)

"Misurata"

1960

1963

1966

1969

1972

1975

1978

1981

1984

1987

1990

1993

1996

1999

2002

2005

2008

2011

‐100

0

100

200

300

400

500

(%)

"Nalut"

1960

1963

1966

1969

1972

1975

1978

1981

1984

1987

1990

1993

1996

1999

2002

2005

2008

2011

‐100

0

100

200

300

400

500

600

(%)

"Sebha"



Figure (2.4) (continued) Annual anomalies in total rainfall ratios from 1961 to 2010, for base line (1981 – 2010) average. The bars indicate anomalies ratios for each year, the blue lines indicate five-year means, and the red lines indicate long-term linear trends.

1960

1963

1966

1969

1972

1975

1978

1981

1984

1987

1990

1993

1996

1999

2002

2005

2008

2011

‐100

‐50

0

50

100

150

(%)

"Shahat"

1960

1963

1966

1969

1972

1975

1978

1981

1984

1987

1990

1993

1996

1999

2002

2005

2008

2011

‐100

‐50

0

50

100

150

(%)

"Sirt"

1960

1963

1966

1969

1972

1975

1978

1981

1984

1987

1990

1993

1996

1999

2002

2005

2008

2011

‐100

0

100

200

300

400

500

600

700

(%)

"Tazerbo"



Figure (2.4) (continued) Annual anomalies in total rainfall ratios from 1961 to 2010, for base line (1981 – 2010) average. The bars indicate anomalies ratios for each year, the blue lines indicate five-year means, and the red lines indicate long-term linear trends.

1960

1963

1966

1969

1972

1975

1978

1981

1984

1987

1990

1993

1996

1999

2002

2005

2008

2011

‐100

‐50

0

50

100

150

(%)

"Tripoli airport"

1960

1963

1966

1969

1972

1975

1978

1981

1984

1987

1990

1993

1996

1999

2002

2005

2008

2011

‐100

‐50

0

50

100

150

(%)

"Tubruk"

1960

1963

1966

1969

1972

1975

1978

1981

1984

1987

1990

1993

1996

1999

2002

2005

2008

2011

‐100

‐50

0

50

100

150

(%)

"Yefren"

Climate Cha

Khalid Ibrah

Figure ((1981 –five-yea

Table (2Base Li

ange over Libya &

him Elfadli

(2.4) (contin– 2010) avear means, an

2.8) Anomaine Average

& Impacts on Agr

nued) Annuerage. The bnd the red li

Stations AgedabiaBenina Derna

Gagbub Ghadame

Ghariat Hon Jalo

Kufra Misurata

Nalut Sebha Shahat

Sirt Tazerbo

Tripoli airpTubruk Yefren Zuara

Average

S.D

alies Variabes

riculture

ual anomaliebars indicatines indicat

(196

a 4229

es 87

11219

a 35

1223

16

port 3643

e 75

bility (%) fo

51

es in total rate anomaliete long-term

61-1990) (43.54 28.72 26.11 95.55 86.37 73.31 19.62 21.16 93.71 30.33 50.08 21.68 20.78 39.32 66.78 34.12 63.60 45.99 35.60

73.49 50.48

or Annual R

ainfall ratios ratios for

m linear tren

(1971-200040.25 28.14 25.45 83.23 86.41 76.28

111.74 110.28 155.16 30.19 46.54

123.61 22.04 36.34

138.48 35.60 49.50 43.99 35.68

67.31 42.28

Rainfall Tota

os from 196each year,

nds.

) (1981-238.929.525.291.966.981.8

113.111.220.30.655.3

125.22.139.9

166.42.640.749.041.1

73.353.5

als mm (196

1 to 2010, fthe blue lin

2010) 92 52 28 99 94 85 14 64 66 60 31 54 16 91 96 69 79 07 10

37 56

61-2010) in

June2012

for base linenes indicate

Different

2

e e



2.7 Trends of rainy days (1961-2010)


Agedabia -0.43- -0.30- 0.00- -0.45- -1.11-

Benina 0.40- 0.00- 0.00- 0.77- 0.59-

Derna 0.42- -0.86+ 0.00- 0.00- -0.77-

Gagbub 0.00- 0.00- 0.00- 0.00- 0.00-

Ghadames 0.33- 0.00- 0.00- 0.33+ 1.18**

Ghariat 0.00- 0.00- 0.00- 0.00- -0.98+

Hon 0.50+ 0.00- 0.00- 0.00- 0.42-

Jalo 0.00- 0.00- 0.00- 0.00+ -0.27-

Kufra 0.00- 0.00- 0.00- 0.00- -0.00-

Misurata 0.77- 0.00- 0.00- 0.00- 0.00-

Nalut 0.45- -0.29- 0.00- 0.00- 0.00-

Sebha 0.00- 0.00* 0.00- 0.00- 0.48*

Shahat -1.36- -0.91* 0.00- -1.00+ -3.57**

Sirt 1.67* 0.54- 0.00- 0.00- 2.00*

Tazerbo 0.00- 0.00- 0.00- 0.00- 0.00-

Tripoli airport 0.56- -0.45- 0.00- -0.27- -0.50-

Tubruk 0.00- -0.56- 0.00- -0.29- -1.50-

Yefren 0.00- -0.83+ 0.00- -2.22* -4.32**

Zuara 0.79- 0.00- 0.00- -0.45- -0.31-

Average 0.22 -0.19 0.00 -0.19 -0.46

S.D 0.59 0.38 0.00 0.60 1.47

Table (2.9) Trends of Rainy Days (=>0.1 mm/decade), by Mann- Kendall Sen's test (1961-2010)



The number of rain days in a month, or season or year is a useful indication of the nature

of daily rainfall in a particular month, or season or year and is an important parameter for sectors

that are sensitive to the frequency of rainfall events rather than the accumulated total rainfall

over a period. Typically, as would be expected, the number of rain days in a month, or season or

year is proportional to the total rainfall in the month, or the season or the year. However this is

not always the case. In some locations a drier month, or season or year could have the same

number of rain days as a wetter month, or season or year.

Table (2.9) shows the trends of rainy day which is defined locally as each day would has

0.1 mm of rainfall or more, also at annually scale shows negative trends at nine stations, the

highest and lowest trends were -0.27 and -4.34 day/decade in Jalo and Yefren respectively.

Positive trends were shown at five stations; the lowest trend was 0.42 day/decade in Hon while

the highest was 2.0 day/decade recorded in Sirt. Generally, annual negative pattern trend

dominated spatially over all the country whilst negative pattern more explicitly at northwestern

parts in contrast another parts.

The trends were significant at six stations, which are; Ghadames, Ghariat, Sebha, Shahat, Sirt and Yefren. Seasonally, negative trends dominated spatially in spring and autumn with less variability in spring meanwhile in winter positive pattern was prevailed. It seems that trend patterns of spring and winter seasons controlled directly on annual attributes trend. Briefly, rainy days patterns distribution trend consistence symmetrically with total rainfall patterns.



Chapter three

Impact of climate change

on wheat crop



3.1 Impact of climate change on agricultural

Predicting the impact of climate change on agricultural sector is perhaps one of the most

crucial issues. The impact of climate change affects not only producer of food and fiber product,

but also the supporting industries including seed, fertilizer and pesticide industries and their

management and services. Schemes should be developed to reduce the vulnerability of climate

on these activities. Agricultural productions are well adjusted to mean climatic conditions, but

sensitive to variability, extremes and changes in the mean state. They are sensitive to the direct

effects of climate upon temperature, rainfall, humidity, atmospheric composition (especially

regarding CO2 levels) and extreme weather events. They are also sensitive to the indirect effects

of climate on soil quality, the incidence of plant diseases and on weed and insects (including

pests) populations. In particular, irrigated agriculture would be affected by changes in the rate of

replenishment of freshwater aquifers. Assessments made by different scientists of the impact of

climate change upon agricultural productivity therefore vary and are beset by great uncertainty

(McMichael et al., 1996).

3.2 Simulation of the impact of climate change on wheat crop

3.2.1 The Decision Support System for Agro technology Transfer

The Decision Support System for Agro technology Transfer (DSSAT) system developed

as part of the International Benchmark Sites Network for Agro technology Transfer (IBSNAT)

project. The IBSNAT project has established a network of research sites around the world where

various agricultural technologies can be field tested as part of the process of transferring them to

other farming systems. The DSSAT system is a set of crop models, utility programs, and data

sets for facilitating technology testing through simulation. The DSSAT system contains many of

the elementary components. The first component is the DSSAT Shell that provides a common

user interface to the whole system. This allows all features to be readily accessed while reducing

learning time as new models and capabilities are added to the package. The system also includes

a specialized data base management systems (DBMS), which, unfortunately, was custom built as

opposed to being grounded in some accepted commercial system. This shortcoming is

overshadowed by the fact that it embodies an agreed to method of recording several types of data



crucial to agricultural crop modeling. The DBMS includes data entry routines for crop field trial

experimental data, weather data, soil data, plus graphical and tabular reporting programs for what

are called minimum data sets.

The minimum data set concept is a critical element of standardization in the system. By

definition a minimum data set provides enough information about a site and a season to run any

crop model in the package. A given model may not use all the data in a minimal data set but it

will not require more. The acceptance of this concept by the DSSAT community has major

implications. First it says we agree that a certain particular body of information adequately

defines a specific field scenario with respect our current level of understanding of crop

development. Secondly, it implies a method of recording field data that guarantees results at

different sites or times can be compared. Thirdly, and of least significance but still important, it

insures that all models can be run against all data sets permitting intercrop comparisons.

A final component of DSSAT is a set of "agro technology transfer applications" which

relate to or utilize other elements of the system in miscellaneous ways. One of these is a weather

data generator. This is a stochastic model that uses the probability distributions to generate time

series of artificial but "realistic" daily weather data. These time series are often one to two

centuries long and are used to drive the hundreds of model runs necessary to generate probability

distributions of model outputs.

This process is called Monte Carlo simulation after the famous European gambling

resort. One might ask why long runs of real weather are not used. The answer is that weather

patterns change slightly but detectably on a scale of decades. This is why the WMO issues new

tabulations of climate "norms" every ten years. The majority of these changes are certainly

arbitrary fluctuations; it is a subject of intense debate as to whether there may be an embedded

directional component such as global warming. Of course the desired results from a Monte Carlo

simulation are the probabilities of various outcomes in "times like these". Data produced by

weather generators using static distributions permits this whereas real weather data does not. A

second application is a strategy evaluation program that allows particular decision strategies to

be tested in the context of a large number of possible runs.



A final program allows the estimation of genetic coefficients for new varieties of those

crops modeled in the system. This is a classic parameter estimation problem and the program

implements an iterative method similar to those we have studied.

3.2.2 CERES-Wheat Model

CERES-Wheat model (Godwin et al., 1989) has been designed to simulate crop growth

and development within the framework of the DSSAT v3.5 Decision Support System for Agro

technology Transfer (Hoogenboom et al., 1994).

It simulates the effects of cultivar, planting density, weather, soil water and nitrogen on crop

growth, development and yield. Therefore it can be used to predict potential alternative

management strategies and tactics that affect yield and intermediate steps in the yield formation

process.

Most input data are daily weather data (radiation, precipitation, maximum and minimum

air temperatures) and soil data (drainage and runoff coefficients, evaporation and radiation

reflection coefficients, soil water holding capacity amounts, and rooting preference coefficients

at several depth increments). The model also requires saturated soil water content and initial soil

water content for the first day of the simulation. Crop genetic inputs are coefficients related to

photoperiod sensitivity, duration of grain filling, conversion of mass to grain number, grain

filling rates, vernalisation requirements, stem size, and cold hardiness. Management input

information includes plant population, planting depth, and date of planting.

If irrigation is used, the date of application and amount is required. Latitude is also

required for calculation of day length calculation. The CERES-wheat model is capable of using

different weather, soils, genetic and management information within a growing season or for

different seasons in a single model execution. It simulates the phonological development,

biomass accumulation and partitioning, leaf area index, root, stem, leaf and grain growth, soil

water and nitrogen balance, the crop’s utilization of water and nitrogen, water and nitrogen stress

(Godwin and Singh, 1998; Ritchie, 1998; Ritchie et al., 1998).



Phonological development and growth in the CERES models are specified by cultivar-

specific genetic coefficients depending on the photoperiod, thermal time, temperature response

and dry matter partitioning. CERES models calculate dry matter accumulation as a linear

function based on intercepted photosynthetically active radiation. Potential dry matter

accumulation depends on the amount of biomass already produced and the actual leaf area index.

This is then corrected for actual daily biomass by applying factors for water and/or nitrogen

stress and non-optimal temperature. Rooting depth is assumed to increase each day as a function

of the crop development rate and is thus predicted as the product of a constant (set for each crop)

and the thermal time for the day plus the rooting depth from the previous day. Another parameter

taken into account is the root weighting factor (ranging from 0 to 1), which characterizes the

potential suitability of each soil layer for root growth. It is multiplied by a soil water factor to

obtain the actual root distribution. In this way vertical distribution of soil water content is taken

into account and root growth is restricted to zones with higher soil water availability.

The CERES-Wheat model includes the capability to simulate the direct physiological

effects of increased atmospheric CO2 concentrations on plant photosynthesis and water use,

based on experimental results. Higher levels of atmospheric CO2 concentrations have been found

to increase photosynthesis and stomatal resistance, resulting in increases in yield and water use

efficiency.

DSSAT software allows users to (I) input, organize and store data on crops, soils and

weather, (II) retrieve, analyze and display data, (III) validate and calibrate crop growth models

and (IV) evaluate different management practices (Jones, 1993).

3.3 Wheat, (Triticum aestivum L.)

Wheat is a widely adapted crop it is grown from temperate irrigated to dry and high

rainfall areas, and from warm humid to dry cold environments. Undoubtedly this wide adaptation

has been possible due to the complex nature of its genome, which provides a fantastic plasticity

to the crop. Wheat is a C3 plant and as such it thrives in cool environments. Wheat may be

classified in several ways, which may differ from country to country according to cultivar,

Protein content , Hardness ,where it’s grown, grain color (red or white) and when it was sown



(spring or winter). Organ differentiation defines the various wheat stages of development. These

stages can be classified to three stages these are germination to emergence; growth stage one

from emergence to double ridges; growth stage two from double ridges to anthesis and growth

stage three to include the grain filling period, from anthesis to maturity. Physiological maturity is

usually taken as the time when the flag leaf and spikes turn yellow (Hanft and Wych, 1982).

Physiological maturity, defined as maximum kernel dry weight, determines the readiness

of the crop for harvest. This stage of crop development is reached prior to the crop being ready

for combining as the kernel is relatively soft (easily dented with finger nail) and kernel moisture

percentage is relatively high. Swathing or glyphosate applications can occur at physiological

maturity without yield loss. Moisture percentage is not a good predictor of physiological

maturity as it varies with varieties and environments.

Moisture percentage is relatively high until about 95% of the maximum dry weight has

accumulated. Then it generally decreases rapidly with rate of water loss determined

predominantly by prevailing environmental conditions. At physiological maturity, moisture

percentage ranges from about 20 to 40%.Although physiological maturity is defined relative to

dry weight accumulation; there are visual indicators that accurately predict the attainment of this

important stage. The most accurate indicator known is the appearance of a dark layer of cells.

The specific location of these dark cells depends on the crop.

For example, it is seen at the base of the corn kernels (black layer) and along the crease

of the wheat kernel (pigment strand). Although this pigment strand is the most accurate visual

indicator of physiological maturity, it is not always the most useful as kernels in the same spike

and especially different spikes reach physiological maturity at different times. Another visual

indicator of physiological maturity is loss of green color from the preduncle, kernel (including

crease) and glumes. Generally, the glumes at the bottom of the spike are the last of these

structures to lose green color. Thus, some green color on the lower glumes of some spikes is a

good indicator of physiological maturity because little, if any, dry weight will accumulate after

this time. The loss of green color from glumes is a good predictor of physiological maturity that

is easily observed across the field.



a) Germination to emergence

The minimum water content required in the grain for wheat germination is 35 to 45% by

weight (Evans et al., 1975). Germination may occur between 4 and 37°C being optimal from 12

to 25°C. Seed size does not alter germination but affects growth, development and yield. Bigger

seeds have several advantages when compared to smaller seeds, such as faster seedling growth,

higher number of fertile tillers per plant and higher grain yield (Spilde, 1989). The advantage of

bigger seeds is shown when the crop is grown under environmental stresses, particularly drought

(Mian and Nafziger, 1994). At the time that crop emergence occurs the seed embryo has three

to four leaves primordial and almost half of the leaf primordial already initiated (Baker and

Gallagher, 1983a, 1983b; Hay and Kirby, 1991). During germination the seminal roots are the

first to grow, followed by the coleoptile which protects the emergence of the first leaf. The

length of the coleoptile limits sowing depth and its length changes with genotype increasing only

lightly when seeds are sown deeper (Kirby, 1993). Semi dwarf wheat has shorter coleoptiles

than tall wheat.

b) Emergence to double ridges

Wheat tillers grow from the axils of the main shoot leaves. The potential number of tillers

varies with genotype, particularly among flowering types, winter types having a bigger number.

Semi dwarf wheat usually has a high number of tillers. Bud differentiation into tillers and tiller

appearance generally ends just before stem elongation starts (Baker and Gallagher, 1983b).

Tillering does not end at any specific wheat development stage but rather that it is controlled by

a number of genetic and environmental factors (Longneker et al., 1993). Not all tillers produce

spikes in wheat, many tillers abort before anthesis (Gallagher and Biscoe, 1978).

The phyllochron (the interval between similar growth stages of two successive leaves in

the same culm) is strongly dependent on temperature (Rickman and Klepper, 1991), but severe

water deficits (Cutforth et al., 1992) and strong nitrogen deficiency (Longnecker et al., 1993)

retard the leaf emergence rate in spring wheat. Genetic variation is observed in the phyllochron

of genotypes of bread wheat and durum wheat (Frank and Bauer 1995).



c) Double ridges to anthesis

Wheat plants have from 4 to 8 leaves in the main shoot when the growing apex changes

from the vegetative to the reproductive stage. The length of the apex at this time is

approximately 0.5 mm. The glume and lemma primordium stages follow. The floret primordia

are found in the axil of each lemma. Temperatures above 30°C during floret formation cause

complete sterility (Owen, 1971; Saini and Aspinal, 1982). Each spikelet has from 8 to 12 floret

primordia in the central part of the spike. The basal and distal spikelets have from 6 to 8 florets.

Less than half of these florets complete anthesis. The rest abort or are insufficiently developed

before anthesis to be fertilized (Kirby, 1988; Kirby and Appleyard, 1987; Hay and Kirby,

1991).

d) Anthesis to physiological maturity

The wheat spike contains only one spikelet per rachis node. Each spikelet has between

three and six potentially fertile florets (Kirby and Appleyard, 1984), which are auto pollinated

in 96% of the cases (Martin et al., 1976). Anthesis begins in the central part of the spike and

continues towards the basal and apical parts during a 3 to 5 day period (Peterson, 1965). The

proximal florets of the central spikelet are fertilized 2 to 4 days earlier than the distal florets.

These grains usually have a greater weight (Simmons and Crookston, 1979).

Recently, biotechnological methods have been applied to improve wheat and create new

varieties. These techniques often compress the time needed for development of a new variety.

For example, if testing has shown that a gene has an improving effect; it can be incorporated into

the target wheat plant and monitored through growth generations to ensure that it is retained.

This does not eliminate laboratory testing using traditional procedures, but it does eliminate

some traditional selection processes required to ensure that desirable traits are maintained. Of

course, varieties developed with these techniques are subjected to field tests and must also meet

other requirements.

3.3.1 Grain Production of Wheat in Libya



Many climatic and land constraints limit Libya's grain production to just two cereal

crops: wheat and barley. These crops are restricted to just a narrow, rain-brushed ribbon of land

(and its adjacent highlands) along the coast, and a few irrigated areas on isolated oases.

Cultivation of autumn-sown wheat is made possible because there are two main water sources.

First, there are large reserves of shallow groundwater in Tripolitania, along Libya's northwest

coast. This source permits significant irrigation. Second, the scant coastal precipitation that does

occur fortuitously falls during the winter grain growing season (November through April).

While wheat is the preferred food grain, barley is more adaptable in the marginal climate

and soils, so it is a popular choice for the Libyan farmer located in the drier hinterland. Fall

planting typically begins in October, after the first fall rains arrive, and can last into December.

Harvest begins in May for wheat, wrapping up in June. The critical flowering period for wheat

occurs in late March and early April. During this time period, the crop is most sensitive to high

temperatures and low precipitation.

3.3.2 Two Small Agricultural Areas

Only two areas in Libya have average annual rainfall levels exceeding the minimum

threshold (250-300 mm) considered necessary to sustain rainfed agriculture figure (3.2). The

climate in both of these growing regions is Mediterranean, with almost all precipitation falling

during the winter and late fall months. Libya and North Africa's weather are generally influenced

by just two sources: the Mediterranean Sea to the north, and the expanse of desert to its south.

When cooler air masses from the north meet up with hot desert air, lifting occurs, which can

create rainfall. Besides chronically low precipitation totals, another major impediment for

reliable crop production is the aridity of the country's climate.

Desert winds or dry "Ghibli" can greatly reduce relative humidity and substantially

reduce or destroy a crop. "Ghiblis can last five days, but seldom persist for more than one. If the

transpiration requirement is extraordinarily high for just a few days and the soil moisture

available to the plant roots falls considerably short of meeting this demand, plants will suffer

serious damage. A two or three day hot, dry Ghibli coming after a 30-day rainless period will

ruin a promising grain crop." While soils are mostly fertile, production is still constrained by



water scarcity. Fluctuations in both the timing and location of rainfall prevent reliable yearly

harvests. Farther south, beyond the narrow band along the fertile coast, rainfall is less than 100

millimeters (4 inches) per year, far below the limit for sustaining crop-growth, with rainfall

missing large areas of the desert for consecutive years.

a) Cyrenaica

Cyrenaica is a large geographic region in northeast Libya. It’s inhabited, northern areas,

just east of the Gulf of Sidra; include Banghazi and many neighboring coastal cities. Cyrenaica's

most agriculturally conducive areas are concentrated on the Barce Plain on its immediate coast.

Slightly beyond, Jabal al Akhdar, a coastal plateau rising 900 meters between Banghazi to the

southwest and Darnah to the northeast is the other major producing area. Its subtle rise produces

an orographic effect on weather systems, wringing out scant moisture from the region's dry air

masses. Its rain-inducing tendency effectively allows low yielding rain-fed crops (wheat and

barley) to be grown on the plateau. The highest land on the plain receives between 400 and 600

millimeters of annual rainfall, (the most in Libya) while the immediately adjacent, north-facing

areas receive 200 to 400 millimeters. Although Libya's highest rainfall totals occur on the high

ground of Al Jabal Al Akhdar in northern Cyrenaica, underground water aquifers are extremely

deep, and therefore mostly cost-prohibitive for irrigation. Most of Cyrenaica's agriculture is

dryland or rainfed crops.

b) Tripolitania

Tripolitania is situated in the northwest corner of the country. It contains Libya's greatest

population density, and is clustered with many large cities, including the capital. The area

includes Jabal Nafusah and the Jifarah Plain, areas that receives between 200-400 millimeters of

rain annually. While this is minimal for dry land agriculture, an aquifer underlies the Jifarah

Plain, allowing fairly intensive well-driven irrigation to occur. The combined rainfall and

irrigation supports low yielding wheat, barley and pasture land. An old report from 1966, but

likely still accurate, lists two-thirds of the nations cultivated acreage to be located in Tripolitania.

The same report also states that Tripolitania has twice as much arable land as Cyrenaica.



Figure (3.1) Wheat production areas in Libya (Source FAO database)

3.4 Effect of climate change on wheat production

To better understand the impact of global change on agricultural production, it is

important to concentrate research efforts in areas where uncertainties can be reduced (Schneider,

1989), and this will require close cooperation and communication among scientists from multiple

disciplines (Krupa and Kickert, 1989). Projections of global impacts often account for the

beneficial physiological effect of atmospheric CO2 enrichment on plant growth without

considering the air quality factor (Mulchi et al., 1992; Barnes and Pfirrmann, 1992). These

projections are also based on results from plants grown under experimental conditions.

Refinement of crop growth models for predictive purposes cannot be made before interactive

studies of elevated CO2 and other important environmental factors such as water availability,

temperature, and air quality are better understood (Allen, 1990; Barnes and Pfirrmann, 1992).

Therefore, published projections of global climate change impact on agriculture (e.g.,

Adams et al., 1990; Stockle et al., 1992) may be overestimated. When elevated CO2 makes



plants grow faster and bigger, and that growth enhancement is combined with decreased stomatal

conductance brought on by higher CO2 (thereby reducing water loss), plants become

substantially more water-use efficient and far more resistant to drought. Wheat is widely

recognized as one of the most responsive to CO2 fertilization of all cereal crops as a result of

enhanced photosynthesis and improved water use efficiency.

Much of the positive effect of CO2 fertilization on wheat yields is due to the improved

resistance to drought conditions (Wittwer 1995). Partial stomatal closure leads to reduced

transpiration per unit leaf area and, combined with enhanced photosynthesis, often improves

water-use efficiency, therefore crop yield increases by using a larger total amount of water over a

longer period (Allen et al., 1996).

An increase in atmospheric concentration of CO2 increases yields of wheat, by increasing

photosynthetic rates and decreasing transpiration. In addition, the effect of CO2 fertilization is

higher at increased temperatures (Wheeler et al. 1996). It is reported that elevated CO2 increases

the dry matter production during the vegetative growth stage (Mitchell et al. 1999; Wall et al.

2000) and yield in wheat. Rainfed crops were more sensitive to increased CO2 than irrigated

crops while low nitrogen applications depressed the ability of the wheat crop to respond to

increased CO2 (Tubiello et al. 1995). Amthor et al. (2001) examined fifty different studies on

how wheat growth and yields are impacted by varying levels of atmospheric CO2.

He divided the studies into those that used laboratory chambers, and those using

glasshouses, closed-top field chambers, open-top chambers, and free-air field CO2 enrichment

systems. He found that yields increased with elevated levels of CO2 "with a maximum effect

(+37%) at about 890 ppm CO2 ". He also reports, "On average, doubling CO2 from 350 to 700

ppm increased yield about 31%." On top of that he found that elevated CO2 stimulated the yield

of water-stressed wheat. Enhanced wheat yields of approximately 20 percent could result from

increasing CO2, but wheat production is likely to be limited by competition for land from other

crops, including soybeans (Southworth et al. 2002b).

Higher temperature might be expected to increase plant development because variables

such as duration of growth, average growth rates and amount of assimilate partitioned to the



economic yield. By increasing temperature, evapotranspiration and water-use during the growing

season, the likelihood of water stress and consequent growth reduction during the cereal grain-

filling period increases. Increases in temperature accelerated phonological development for

wheat crop, shortened time to maturity, lowered yield and decreased water use efficiency

(Brown and Rosenberg 1997).

A shorter growing period therefore means less total water demand and fewer episodes of

water stress (Peiris et al., 1996; Wang and Connor, 1996). Moot et al. (1996) and Batts et al.

(1998), however, reported that high temperature shortens the growth period and decreases the

yield. The crop response to higher temperatures clearly depends on the character of temperature

increase as well as on the developmental stage of the crop (Porter and Gawith, 1999).

Wheat yields are set to increase throughout Europe for all climate change scenarios but

not increase for all other crops used in the study (Harrison and Butterfield 1996, Butterfield et

al. 2000). Simulation studies for wheat yield in Britain report that temperature rise generally

results in lower yields; yet an increase in precipitation and atmospheric CO2 concentration

tended to increase yields (Semenov et al., 1996; Wolf et al., 1996). Increases in temperature

would be expected to lengthen the potential growing season of winter wheat, resulting in a shift

of thermal limits of agriculture (Alexandrov, 1997). Rosenzweig and Iglesias (1998) reviewed

findings from eighteen countries, reporting that climatic change would require alteration to

agricultural systems.

Adaptation strategies to reduce adverse impacts of climatic change involve alterations in

current management practices such as timing of operations, planting date, plant density, crop

type or variety, irrigation, fertilizer (especially nitrogen application) and planting date. Change of

temperature variability by more than 25% leads to statistically significant changes in/ wheat

yield distribution and the effect of temperature variability decreases with increased values of

mean temperature Trnka et al. (2004).



3.5. Data and methodology

The experiment data (that used for validation purpose) was published by (Shreidi, 2009),

“Agricultural and Animal Research Center in Libya”. The experiment was carried out at the

western coast high lands of Libya during the season 1999/2000 (Nov. – May) to study the

Productivity Efficiency of bread wheat crop (Triticum aestivum L.) for twelve Cultivars which

are so-called (Acsad 813, Acsad 849, Acsad 851, Acsad 853, Acsad 857, Acsad 881, Acsad 881,

Acsad 885, Gaminia, Behos 208, Behos 116 and Mukthar), under conditions of rain-fed

agriculture. The climate parameters maximum temperature (oC), minimum temperature (oC),

solar radiation (MJ/m2) and rainfall (mm/day) for the western coast high lands were obtained

from “Libyan National Meteorological Center” for the season 1999/2000 and the climate change

data for the same parameters for the period of 2010-2020 is extracted by GCM of (HadCM3) by

applying A1B emission scenario of IPCC. Figure (3.2) shows the minimum and maximum

temperature, and rainfall for the season 1999/2000 and 2010-2020 average, from this figure we

can deduce that the climate parameters of this site, the maximum and minimum temperature at

season 1999/2000 are higher than average of period 2010-2020 (for emission scenario A1B),

while rainfall has negative difference.

The climate change scenarios for location were assessed according to future conditions

derived from MAGICC/SCENGEN software of the university of East angle (UK). In this the

study the GCM model (HadCM3) A1B scenario data were used. HadCM3 (abbreviation

for Hadley Centre Coupled Model, version 3) is a coupled atmosphere-ocean general circulation

model (AOGCM) developed at the Hadley Centre in the United Kingdom. It was one of the

major models used in the IPCC Third Assessment Report in 2001. Because projections of climate

change depend heavily upon future human activity, climate models are run against scenarios.

There are 40 different scenarios, each making different assumptions for future greenhouse gas

pollution, land-use and other driving forces. Assumptions about future technological

development as well as the future economic development are thus made for each scenario. Most

include an increase in the consumption of fossil fuels. These emissions scenarios are organized

into families, which contain scenarios that are similar to each other in some respects. IPCC

assessment report projections for the future are often made in the context of a specific scenario

family. A1B scenario belongs to the A1 scenarios family which is of a more integrated world.



The A1 family of scenarios is characterized by rapid economic growth, a global population that

reaches 9 billion in 2050 and then gradually declines, the quick spread of new and efficient

technologies and a convergent world - income and way of life converge between regions and

extensive social and cultural interactions worldwide. The A1B subset scenario based on

technological depends of balanced emphasis on all energy sources (fossil and non-fossil-fuels);

[source: http://en.wikipedia.org/wiki/Special_Report_on_Emissions_Scenarios#A1].

The principal of MAGICC/SCENGEN is allowing the user to explore the consequences of

a medium range of future emissions scenarios. The user selects two such scenarios from library

of possibilities. The reason for two scenarios is, to able to compare a no action scenario with an

action or policy scenario.

Thus, in MAGICC/SCENGEN the two emissions scenarios are referred to as a reference

scenario and policy scenario (Wigley et al. 2000).

DSSAT 3.5 (CERES-Wheat) model methodologies’ and procedures’ are used to study the

simulation of the impacts of climate change on different mentioned cultivars of wheat crop.

3.6 Validation of CERES-Wheat model

For validation purpose of the CERES-Wheat model the flowering date, physiological

maturity date and Grain yield parameters are selected for presenting in this investigation (for the

season 1999/2000) and at the same site.

3.6.1 Flowering date

Table (3.1) shows the comparison between measured and predicted flowering date

(number of the day mostly after a week of sowing date), and percentage differences for 12 wheat

cultivars at studied site. The cultivar Acsad 853 has the maximum measured flowering date and

Behos 116 has the minimum one. The predicted values of flowering date are smaller than

measured one for seven cultivars (Acsad 849, Acsad 851, Acsad 853, Acsad 883, Acsad 885,

Behos 116 and Mukthar) and the vice verse in the cultivars of (Acsad 813, Acsad 857, Acsad

881and Behos 208), but the predicted and measured flowering date is equally for the cultivar



Figure (3.2) Comparison between observed (season 1999/2000) and predicted (2010-2020)

minimum and maximum temperature, and rainfall parameters



Gaminia. The percentage differences of flowering date are negative for wheat cultivars of

(Acsad 813, Acsad 857, Acsad 881and Behos 208), but are found positive for other studied

cultivars where the highest negative difference observed in Acsad 813 while found zero for

Gaminia cultivar. The average of all studied cultivars, for both measured and predicted flowering

date, is 110.6 days while percentage difference is zero. Otherwise the RMSE (Root-Mean-

Square-Error which is a frequently used measure of the differences between values predicted by

a model or an estimator and the values actually observed) is 2.17 days with 1.66 % of

normalization. So the model successes to predict the flowering date with very small error within

an acceptable range.

Cultivar Measured Predicted Difference %

Acsad 813 107 109 -1.87

Acsad 849 112 111 0.89

Acsad 851 109 108 0.92

Acsad 853 114 113 0.88

Acsad 857 112 114 -1.79

Acsad 881 111 113 -1.80

Acsad 883 112 110 1.79

Acsad 885 110 109 0.91

Gaminia 109 109 0.00

Behos 208 113 115 -1.77

Behos 116 106 105 0.94

Mukthar 112 111 0.89

Average 110.6 110.6 0.00

Table (3.1) Comparison between measured and predicted flowering date (in day unit) for

different wheat cultivars, (for the season 1999/2000)



3.6.2 Physiological maturity

Table (3.2) shows the comparison between measured and predicted physiological maturity

date (number of the day mostly after a week of sowing date), and percentage differences for 12

wheat cultivars at studied site. From there we can notice that the cultivars Acsad 849 and Acsad

853 have the maximum measured values, and Acsad 851 and Behos 116 have the minimum

values of measured physiological maturity date. The predicted values of (Acsad 813, Acsad 849,

Acsad 853, Acsad 857, Acsad 885 and Gaminia) are higher than the measured, whereas (Acsad

851, Acsad 883, Behos 208, Behos 116 and Mukthar) are lower than the measured, while Acsad

881has same value. Hence the percentage differences for (Acsad 813, Acsad 849, Acsad 853,

Acsad 857, Acsad 885 and Gaminia) appear as negative values and as a positive for (Acsad 851,

Acsad 883, Behos 208, Behos 116 and Mukthar) while is zero for Acsad 881. The average

measured and predicted physiological maturity dates for all studied wheat cultivars are 140.8 and

141.6 days respectively with -0.53 % of percentage difference. RMSE and its normalization

percentage are 3.98 days and 2.82 % respectively for the all wheat cultivars. Hence the model

successes to predict the physiological maturity dates for wheat cultivars under studying and the

performance is found very high with very small of error margin.

3.6.3 Grain yield

Table (3.3) shows the comparison between measured and predicted grain yield

(Kilograms per Hectare), and percentage differences for 12 wheat cultivars at studied site. It is

noticed that the Acsad 885 cultivar has the maximum measured grain yield compared to other

cultivars in this experiment and Acsad 853 has the minimum amount. The grain yield predicted

values of (Acsad 813, Acsad 881, Acsad 885, Gaminia and Behos 208) are higher than the

measured, and the values of (Acsad 849, Acsad 851, Acsad 853, Acsad 857, Acsad 883, Behos

116 and Mukthar) are lower than the measured. The percentage differences of studied wheat

cultivars are negative for (Acsad 813, Acsad 881, Acsad 885, Gaminia and Behos 208) and

found positive for the others. The average measured and predicted grain yield amounts are

1307.5 and 1306.3 kg/ha respectively with average percentage difference of 0.13 % for all

studied wheat cultivars. The RMSE and its normalization percentage are 185.58 kg/ha and 14.19

% respectively for the wheat cultivars. In general it is clear that the model success with very



high degree in prediction of grain yield like that reported in flowering date and physiological

maturity of the all wheat cultivars.


Acsad 813 141 143 -1.42

Acsad 849 144 147 -2.08

Acsad 851 139 137 1.44

Acsad 853 144 146 -1.60

Acsad 857 143 146 -2.31

Acsad 881 140 140 0.00

Acsad 883 140 139 0.50

Acsad 885 140 141 -0.50

Gaminia 140 143 -2.36

Behos 208 140 139 0.50

Behos 116 139 138 0.50

Mukthar 141 140 0.92

Average 140.8 141.6 -0.53

Table (3.2) Comparison between measured and predicted physiological maturity date (in day

unit) for different wheat cultivars, (for the season 1999/2000)




Acsad 813 1280 1296 -1.25

Acsad 849 1510 1499 0.73

Acsad 851 1250 1236 1.12

Acsad 853 1140 1125 1.32

Acsad 857 1300 1291 0.69

Acsad 881 1330 1347 -1.28

Acsad 883 1190 1174 1.34

Acsad 885 1550 1563 -0.84

Gaminia 1370 1385 -1.09

Behos 208 1210 1222 -0.99

Behos 116 1260 1249 0.87

Mukthar 1300 1288 0.92

Average 1307.50 1306.3 0.13

Table (3.3) Comparison between measured and predicted grain yield (kg/ha) for different wheat

cultivars, (for the season 1999/2000)



3.7 Simulation the impact of climate change on wheat crop

Obtained reliable validation output led to this simulation to be made. Simulation of the

climate change impacts on wheat crop (the subject of this study) for the average period (2010-

2020) will be studied through flowering date (days sum), physiological maturity date (days sum)

and grain yield (kg/ha) as well for the same studied site and cultivars.

Average of decadal period (2010-2020) is considered to avoid the inter-annual variability effect

of individual years, consequently climate change impacts would be more apparent.

3.7.1 Flowering date

Table (3.3) shows the predicted flowering date (in day unit) by using SRES A1B

(referred to Special Report on Emission Scenarios published by IPCC)) emission scenario of

HadCM3 as an average of period (2010 – 2020). It is noticed that; the predicted values by the

scenario A1B for the cultivar Behos 116 is the minimum (100 days) and Acsad 853 isthe

maximum (108 days) among of other cultivars. The average predicted flowering dates are 103

days. With comparing of table (3.1) we can find that the predicted average values (2010 – 2020)

by the scenario are smaller than the measured season (1999/2000) for all cultivars, this means

that the flowering date will decrease for SRES A1B emission scenario during the climate change

period (2010 -2020).

3.7.2 Physiological Maturity

Table (3.4) shows the predicted physiological maturity date (in day unit) by using SRES

A1B emission scenario of HadCM3 as an average of period (2010 – 2020). It is noticed that; the

cultivar Behos 116 has the minimum value (130 days) while Acsad 849 has the maximum date

(137 days) to compare of other cultivars. The average predicted physiological maturity dates are

(135 days). With comparing of table (3.2) we can find that the predicted average values (2010 –

2020) by the scenario are smaller than the measured season (1999/2000) for all cultivars, this

means the physiological maturity dates will decrease for SRES A1B emission scenario during

the climate change period (2010 -2020).



Cultivar Predicted values (2010 - 2020)

Acsad 813 102

Acsad 849 104

Acsad 851 102

Acsad 853 108

Acsad 857 104

Acsad 881 103

Acsad 883 104

Acsad 885 102

Gaminia 103

Behos 208 106

Behos 116 100

Mukthar 104

Average 103

Table (3.4) Average predicted flowering date of period (2010-2020) by using SRES A1B

emission scenario for different wheat cultivars

3.7.3 Grain yield

Table (3.5) shows the predicted grain yield (kg/ha) by using SRES A1B emission

scenario of HadCM3 as an average of period (2010 – 2020). It is noticed that; the predicted

average values by the scenario A1B for the cultivar Acsad 853 is the minimum (718 kg/ha) and

Acsad 885 is the maximum (1019 kg/ha) among of other cultivars, while the average predicted

grain yield is (820 kg/ha). With comparing of table (3.3) we can find that the predicted average

values (2010 – 2020) by the scenario are smaller than the measured season (1999/2000) for all

cultivars, this means the grain yield will decrease for SRES A1B emission scenario during the

period (2010 -2020).




Acsad 813 131

Acsad 849 137

Acsad 851 132

Acsad 853 134

Acsad 857 133

Acsad 881 134

Acsad 883 133

Acsad 885 131

Gaminia 134

Behos 208 133

Behos 116 130

Mukthar 132

Average 135

Table (3.5) Average predicted physiological maturity date of period (2010-2020) by using SRES

A1B emission scenario for different wheat cultivars.




Acsad 813 767

Acsad 849 992

Acsad 851 775

Acsad 853 718

Acsad 857 790

Acsad 881 829

Acsad 883 741

Acsad 885 1019

Gaminia 903

Behos 208 742

Behos 116 786

Mukthar 786

Average 820

Table (3.6) Average predicted grain yield (kg/ha) of period (2010-2020) by using SRES A1B

emission scenario for different wheat cultivars



Conclusions



4. CONC LUSIONS

1. In general, trends of Libyan annual mean, maximum and minimum temperature

are 0.31, 0.14 and 0.46 oC/decade respectively, which are mainly all derived by

autumn mean (0.45oC/decade), maximum (0.28oC/decade) and minimum

(0.59oC/decade) temperature trends. On the other hand annual warming is

controlled by minimum rather than maximum temperature.

2. All stations have experienced in winter, non-significant maximum temperature

trends except at 6 stations which reported significant trend where 3 of them

indicated positive trend (most of them at the coast ) and another indicated

negative one (at the north mountains and the south east desert places). Generally

winter spatially has low variability trend (-0.02 oC/decade) than another seasons

which is being accepted regionally.

3. There is a general tendency for the warming trend in annual and seasonal

minimum temperature where the highest are 0.59 and 0.54oC/decade for autumn and

summer seasons respectively. It is evident that the observed trends are either

positive (as shown in the majority of stations) or non-significant as presented in

a few stations (i.e., Shahat, Sirt, Tazerbo and Tripoli airport) in winter.

4. Positive high significant trends (warming pattern) of the mean annual

temperature were observed at all studied stations (which controlled strongly by

autumn pattern) to be less varied over most places of the south, ranged between

0.49 to 0.50oC/decade at Hon and Kufra respectively, versus high variation over the

eastern coast region that extended from Agedabia (0.38oC/decade) to Benina

(0.08oC/decade) to Derna (0.20oC/decade).

5. Annual rainfall trends were negative over whole country (-3.34 mm/decade),

which is agreed with Mediterranean trend, except Ghadames, Tubruk and

Yefren where they have been observed increasingly and only Tubruk has



significant trend (24.72 mm/decade) that could be more reliable than another

positive trends in terms of geographical location.

6. Annual rainfall decreased in Libya in average of -0.10 % per decade, where two

patterns nationally can be recognized spatially; northern pattern in decreasing an

average of -1.24% per decade, that agreeable with south Mediterranean coasts

observations, and southern pattern in increasing average of 1.47 % per decade

which is considered less significant due to its very low annual rainfall rate

(<15.0 mm).

7. The rainfall trends in annual scale were significant at four stations, which are;

Ghadames, Shahat, Tripoli airport and Tubruk. Seasonally, negative trends

dominated spatially in spring (-1.99% per decade) and autumn (-2.15% per

decade) with less variability in the south of country (-1.1 and 1.3% per decade in

spring and autumn respectively) than in the north of country (-2.7 and -4.6% per

decade in spring and autumn respectively as well). However in winter, positive

pattern (2.6% per decade) was prevailed where the north side experienced more

variability distribution (3.2% per decade) to the south part (1.8% per decade). In

general annual, inter-annual, seasonal and inter-seasonal variability is higher

over the coastal places.

8. The CERES-wheat model succeeded with very high accuracy trough validation

processing in predicting of grain yield, flowering date and physiological

maturity of studied wheat cultivars where their RMSE are 185.58 kg/ha, 2.17

and 3.98 days respectively. .

9. The flowering and physiological maturity dates under climate change studied

projection (A1B) of period (2010-2020) predicted a shorter number of days than

measured at season of 1999/2000 climate by 8 and 6 days respectively, and

grain yield will decrease by 488 kg/ha as an average over all studied cultivars..



10. The all cultivars indicate expected reduction in productivity of wheat yield

during the study period of (2010-2020) of A1B scenario with comparing that

measured at season of 1999/2000 (range between 422 to 531 kg/ha) which

might indicate the need of use other cultivars more resist to climate change

impacts.



Appendixes



5. Appendix-A

Average Temperature (oC) Normals

Station 1961-2010 1961-1990

Winter Spring Summer Autumn Annual Winter Spring Summer Autumn Annual

Agedabia 23.83 20.21 25.99 22.61 20.86 13.53 19.94 26.40 22.04 20.47

Benina 13.50 19.04 26.28 22.19 20.25 13.50 19.14 26.22 21.91 20.18

Derna 14.92 18.13 25.29 22.57 20.23 14.83 17.97 24.95 22.22 19.99

Gagbub 13.09 21.25 29.06 22.72 21.46 12.89 21.10 28.65 22.26 21.22

Ghadames 11.93 21.96 31.67 23.00 22.12 11.72 21.53 31.15 22.28 21.65

Ghariat 11.78 20.04 28.60 21.76 20.57 11.56 19.69 28.20 20.89 20.00

Hon 12.50 21.19 28.79 22.55 21.26 12.23 20.87 28.35 21.82 20.79

Jalo 14.25 22.40 29.89 23.62 22.54 14.12 22.24 29.43 23.15 22.22

Kufra 14.50 24.56 31.14 24.18 23.59 14.12 24.14 30.54 23.48 23.07

Misurata 14.61 18.96 26.51 23.29 20.84 14.52 18.78 26.26 22.91 20.61

Nalut 10.77 18.19 27.55 20.71 19.31 10.64 17.77 27.20 20.22 18.96

Sebha 13.61 24.09 31.55 24.56 23.45 13.47 23.93 31.29 24.11 23.21

Shahat 10.12 15.06 22.89 18.41 16.62 10.14 15.00 22.54 18.08 16.44

Sirt 14.59 19.14 25.73 23.07 20.64 14.49 19.01 25.36 22.57 20.35

Tazerbo 13.57 23.51 29.98 23.46 22.65 13.43 23.34 29.60 22.98 22.36

Tripoli airport 12.99 19.29 27.88 22.60 20.69 12.93 19.03 27.53 22.14 20.40

Tubruk 13.29 18.22 25.31 21.56 19.64 12.69 18.14 25.20 20.83 19.26

Yefren 10.69 17.89 27.34 20.75 19.18 10.64 17.60 26.96 20.33 18.90

Zuara 13.71 18.43 26.07 22.58 20.21 13.37 17.97 25.46 21.90 19.68



5. Appendix-A(continued)

Average Temperature (oC) Normals

tation 1971-2000 1981-2010


Agedabia 13.59 20.06 26.80 22.43 20.72 30.68 20.43 25.85 23.05 21.19

Benina 13.39 19.03 26.28 22.15 20.21 13.46 19.04 26.37 22.42 20.32

Derna 17.91 25.09 22.38 20.03 0.00 14.91 18.24 25.56 22.81 20.39

Gagbub 12.77 21.03 28.83 22.55 21.28 13.15 21.33 29.39 23.06 21.64

Ghadames 11.86 21.95 31.87 22.93 22.16 12.04 22.52 32.27 23.84 22.67

Ghariat 11.62 19.76 28.62 21.64 20.44 11.90 20.27 28.97 22.41 20.94

Hon 12.25 21.12 28.80 22.45 21.15 12.66 21.60 29.29 23.24 21.74

Jalo 13.96 22.34 29.74 23.39 22.36 14.19 22.52 30.25 23.92 22.75

Kufra 14.21 24.54 30.90 23.92 23.40 14.70 24.95 31.65 24.72 24.01

Misurata 14.58 18.88 26.44 23.26 20.79 14.71 19.15 26.72 23.67 21.07

Nalut 10.70 18.05 27.53 20.61 19.22 10.82 18.67 27.91 21.29 19.68

Sebha 13.47 24.11 31.59 24.47 23.39 13.59 24.31 31.88 24.94 23.67

Shahat 10.11 15.07 22.90 18.46 16.64 10.03 15.15 23.21 18.66 16.78

Sirt 14.44 18.95 25.54 22.92 20.47 14.63 19.29 26.07 23.52 20.89

Tazerbo 13.27 23.53 29.95 23.27 22.49 13.56 23.69 30.37 23.80 22.87

Tripoli airport 12.89 19.09 27.77 22.49 20.56 13.01 19.57 28.26 23.14 21.00

Tubruk 13.01 17.95 25.06 21.34 19.42 13.85 18.23 25.37 22.21 20.00

Yefren 10.58 17.71 27.37 20.72 19.12 10.76 18.15 27.70 21.12 19.44

Zuara 13.63 18.21 25.95 22.48 20.08 13.99 18.85 26.59 23.31 20.70



5. Appendix-B

Total Rainfall (mm) Normals

Station 1961-2010 1961-1990


Agedabia 106.99 16.36 0.12 30.22 153.56 100.43 16.57 0.05 28.51 145.46

Benina 172.65 36.41 0.62 55.89 264.33 175.75 38.47 0.52 57.05 269.31

Derna 159.47 35.05 3.23 63.50 260.20 151.49 34.04 4.55 68.28 258.09

Gagbub 10.35 4.25 0.07 1.78 15.98 10.36 4.68 0.10 2.32 16.70

Ghadames 12.38 9.35 0.78 7.35 28.22 8.42 8.73 0.75 6.84 23.70

Ghariat 17.20 12.62 1.79 19.24 50.55 18.71 13.87 1.78 18.87 52.66

Hon 9.76 2.68 0.01 2.07 9.57 8.69 2.44 0.01 3.20 8.86

Jalo 5.12 2.62 0.01 2.02 9.31 3.76 2.36 0.01 3.08 8.45

Kufra 0.79 0.90 0.00 0.08 1.67 1.05 0.51 0.00 0.05 1.61

Misurata 140.79 36.99 2.01 98.21 277.53 141.81 34.16 1.52 105.53 281.83

Nalut 89.84 54.77 2.55 40.12 186.15 90.69 64.30 2.36 42.07 198.23

Sebha 3.26 2.39 0.50 2.95 9.04 3.49 1.63 0.51 3.26 8.79

Shahat 325.80 97.78 3.17 126.68 554.30 331.50 101.05 4.32 133.37 569.28

Sirt 107.13 23.19 1.04 61.16 191.97 97.77 22.09 0.83 71.59 191.44

Tazerbo 1.37 1.15 0.00 0.22 2.87 0.81 1.47 0.00 0.33 3.08

Tripoli airport 138.73 49.19 1.29 85.30 272.27 149.66 55.69 1.54 102.87 305.84

Tubruk 83.37 18.62 0.20 24.77 128.61 64.60 17.63 0.33 19.33 102.14

Yefren 125.63 66.86 3.84 61.84 259.26 111.51 71.52 3.01 72.36 256.48

Zuara 105.31 35.13 1.56 88.21 229.41 104.51 37.67 0.98 108.76 250.30



5. Appendix-B(continued)

Total Rainfall (mm) Normals

Station 1971-2000 1981-2010


Agedabia 100.43 16.57 0.05 28.51 145.46 102.71 20.42 0.12 33.09 157.35

Benina 175.75 38.47 0.52 57.05 269.31 171.51 38.62 0.42 62.32 274.80

Derna 151.49 34.04 4.55 68.28 258.09 160.66 38.37 1.06 64.59 264.85

Gagbub 10.36 4.68 0.10 2.32 16.70 11.94 5.97 0.03 1.24 19.18

Ghadames 8.42 8.73 0.75 6.84 23.70 9.82 8.15 0.61 5.41 23.69

Ghariat 18.71 13.87 1.78 18.87 52.66 19.67 13.75 1.81 16.16 50.61

Hon 8.69 2.44 0.01 3.20 8.86 9.29 2.99 0.01 1.97 9.48

Jalo 3.76 2.36 0.01 3.08 8.45 4.49 2.89 0.01 1.90 9.28

Kufra 1.05 0.51 0.00 0.05 1.61 0.91 0.98 0.00 0.13 2.01

Misurata 141.81 34.16 1.52 105.53 281.83 146.13 34.41 2.00 100.22 283.16

Nalut 90.69 64.30 2.36 42.07 198.23 97.97 66.42 2.65 45.90 213.34

Sebha 3.49 1.63 0.51 3.26 8.79 4.30 2.67 0.23 1.45 8.65

Shahat 331.50 101.05 4.32 133.37 569.28 306.60 99.53 2.14 127.24 536.68

Sirt 97.77 22.09 0.83 71.59 191.44 110.59 25.37 0.60 70.61 207.12

Tazerbo 0.81 1.47 0.00 0.33 3.08 2.29 1.33 0.00 0.09 3.71

Tripoli airport 149.66 55.69 1.54 102.87 305.84 142.10 57.70 0.75 92.69 293.11

Tubruk 64.60 17.63 0.33 19.33 102.14 82.97 20.14 0.20 27.45 131.22

Yefren 111.51 71.52 3.01 72.36 256.48 121.38 77.62 4.73 64.38 268.14

Zuara 104.51 37.67 0.98 108.76 250.30 115.96 38.87 1.15 94.10 249.78



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Arabic summary



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تعزى الزيادة السنوية لمعدالت درجة الحرارة بشكل عام الى ارتفاع معدل درجة

دار ل 0.46( الحرارة الصغري عن درجة الحرارة العظمي السنوي نمق مقاب

.على التوالي) سنوات 10/ درجة مئوية 0.14

اطق معظم ىالمطار عل بان جميع االتجاهات المناخية لوضحت الدراسة وأ المن

رن وغدامس اطق يف ا آانت سالبة ، من جهة اخرى شهدت من الساحلية لليبي

.وطبرق اتجاهات موجبة

رار الزراعي " النموذج الرياضيأحرز اذ الق ؤ " دعم واتخ ة في التنب ة عالي دق

ات يولوجي وآمي ج الفس ار والنض ل اإلزه ح ومراح ول القم ة محص بإنتاجي

.لليبيا) مرتفعات الساحل الغربي (الحبوب المنتجة بمنطقة الدراسة

تخدام م اس ي ت وذج الرياض ي " النم رار الزراع اذ الق م واتخ ؤ " دع ي التنب ف

ة محصول ا رة بانتاجي ) 2020ـ 2010( لقمح لألصناف المدروسة خالل الفت

ي اعتمدت آفترة مستقبلية تحت ظروف احد سيناريوهات التغيرات المناخية الت

التغير ا ) A1B(خالل هذه الدراسة اخي المن الخاص بالهيئة الحكونية المعنية ب

)IPCC( ي م الزراع ة للموس ة الواقعي روف التجرب ا بظ ومقارنته

النتائج بان الدورة الزراعية لمحصول القمح آانت وضحت، وأ )1999/2000(

اض يولوجي ، وانخف ار والنضج الفس ام مراحل اإلزه دد أي ث ع ن حي اطول م

.شديد في آميات الحبوب المنتجة

Climate change over Libya and impacts on agriculture

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Transcript of Climate change over Libya and impacts on agriculture