Desertification Extreme Climatic Indices Analysis

Case Study – Southeast Africa

Natasha Louise de Lemos van Doorn

Masters Dissertation in

Environmental Engineering

Advisor:

Prof. Amílcar de Oliveira Soares

CERENA-Centre for Natural Resources and the Environment

April 2011

ii

“Desertification contributes to food insecurity, famine and poverty and can give rise to social, economic and political tensions that can cause conflicts, further poverty and land degradation.”

Kofi Annan, former UN Secretary-General, 17th June 20041

“Climate variability and change can, and does, exacerbate land degradation.”

Warren Evans, former World Bank Director of Environment, 17th June 20072

1 (DFID, 2004)

2 Press Statement In advance of Desertification Day

iii

Acknowledgements

I would like to thank CERENA Centre for Natural Resources and the Environment for all the

support given throughout the months of execution of this report.

My special thanks to the following: my advisor, Amílcar Soares, for his guidance, never ending

patience and unshakable optimism; to Maria Joao Pereira, for essential documentation and for

providing the “bread and butter” at the department; to Pedro Correia, for the valuable hours saved by

his fantastic programming skills; to Dora Roque, for the GIS “SOS” interventions and last, but not at

all least, to Pedro Nunes for explaining every task from scratch with unwavering serenity.

A heartfelt thanks to friends and family who endured my “bumpy” journey: Mimá, Peter Pan, M&M, Jo

and Becca.

iv

Abstract

The motivation for this research study arisen from DesertWatch Extension results, applied to three

distinct areas: Portugal, Brazil and Mozambique. Springing from unexpected results reached when

analyzing the particular case of Mozambique, due to insufficient prior evidence to sustain results and

unattained satisfying spatial pattern for desertification dynamics, this study set to characterize

desertification susceptibility, restrained to climate components, on a broad study area located on the

Southeast Africa. Being climate factors the source of unexpected results biophysical characterization

was not considered in this research study.

The climatic analysis of desertification followed DesertWatch Extension methodology and

benefited from the projects major assumptions to develop desertification susceptibility indicators. The

overall methodology, and particularly the characterization of desertification by extreme precipitation

indices, was successfully validated as rendering a valuable insight on the dynamics of such a

complex phenomena as land degradation.

The total number of days per year with precipitation below 1mm, known as RL1 index, was

statistically analysed to render a dynamic and static desertification indicator by evaluating its annual

mean and the decadal variance trend respectively. The spatial distribution of such components and

the final combining desertification susceptibility indicator substantiated the previous results of the

DesertWatch, project pin pointing a central area concerning high susceptibility to desertification in

Zimbabwe that spread to Mozambique (prior results showed only these fringes from which no

continuous pattern could be assessed).

The main findings of this report not only validates DesertWatch methodology and results but also

underlines the need to address desertification phenomena, and its impacts, at a proper scale to fully

understand it.

Key Words: Desertification and Land Degradation, DesertWatch-Extension, Southeastern Africa,

Extreme Climate Indices, Susceptibility to Desertification.

v

Resumo

A motivação para a presente tese de mestrado advém dos resultados obtidos no projecto

DesertWatch-Extension, aplicado a três áreas piloto: Portugal, Brasil e Moçambique. Resultados

pouco claros no caso particular de Moçambique, nomeadamente a inexistência do padrão espacial

contínuo expectável para o fenómeno de desertificação (de acordo com o, ainda que escasso,

conhecimento pericial da região) definiram o objectivo deste estudo como a caracterização da

susceptibilidade à desertificação limitada ao factor clima numa área específica do centro sul-africano.

Importa salientar que a restrição à análise climática, ignorando assim a componente biofísica

(vegetação e solo), se deveu ao facto de ser esta a componente responsável pelos resultados que

motivaram este estudo.

A análise climática da desertificação seguiu a metodologia DesertWatch e beneficiou dos

conhecimentos adquiridos neste projecto para desenvolver um indicador de susceptibilidade. O

índice extremo de precipitação RL1 - número total de dias com registo de precipitação abaixo de

1mm - foi seleccionado como o parâmetro determinante na caracterização do fenómeno de

desertificação. Através da análise da média do parâmetro RL1 ao longo do tempo e da derivada

temporal da variância do mesmo estipularam-se, respectivamente, as componentes estática e

dinâmica do factor clima e, a partir destas, determinou-se o indicador final de susceptibilidade.

A distribuição espacial do indicador e de cada uma das suas componentes demonstra o padrão

contínuo esperado, revelando uma área central de grande susceptibilidade localizada no Zimbabué,

que se estende para Moçambique.

Os principais resultados deste trabalho não só validam a metodologia DesertWatch como

demonstram a importância e a necessidade de análise do fenómeno de desertificação a uma escala

adequada à sua dinâmica, mormente a uma escala regional.

Palavras-Chave: Desertificação, DesertWatch-Extension, Centro Sul Africano, Índices Climáticos

Extremos, Susceptibilidade à Desertificação.

vi

Table of Contents

Acknowledgements ................................................................................................................................ iii

Abstract................................................................................................................................................... iv

Resumo ...................................................................................................................................................v

Table of Contents ................................................................................................................................... vi

Index of Figures ..................................................................................................................................... vii

Index of Tables ..................................................................................................................................... viii

Index of Equations ................................................................................................................................ viii

Introduction ............................................................................................................................................. 1

DesertWatch Extension to Portuguese Partners .................................................................................... 3

Study Area………………………………………………………………………………………………………. 3

Methodology…………………………………………………………………………………………………….. 3

Data………………………………………………………………………………………………………………. 5

Climatic Series Results………………………………………………………………………………………… 5

Underpinning the Problem / Defining Research Scope .......................................................................... 7

Study Area .............................................................................................................................................. 8

Data ........................................................................................................................................................ 9

Method and Results .............................................................................................................................. 10

Discussion and Conclusions ................................................................................................................ 25

Acronyms & Glossary ........................................................................................................................... 30

References ........................................................................................................................................... 32

vii

Index of Figures

Figure 1 – Indicator Processing Step of Simplified Logical Model (Pereira et al., 2011) ................................... 3

Figure 2 – Static and Dynamic Climate Component for Portugal ...................................................................... 5

Figure 3 - Static and Dynamic Climate Component for Brazil ........................................................................... 6

Figure 4 - Static and Dynamic Climate Component for Mozambique ................................................................ 6

Figure 5 – Study Area ....................................................................................................................................... 8

Figure 6 – Study area showing present and past research grids ...................................................................... 8

Figure 7 – Basic procedures followed by present research ............................................................................ 10

Figure 8 – Mean of Annual Maximum Temperature ........................................................................................ 10

Figure 9 - Variance of Annual Maximum Temperature Variance .................................................................... 11

Figure 10 - Mean of Annual Average Temperature ......................................................................................... 11

Figure 11 - Variance of Annual Average Temperature .................................................................................... 11

Figure 12 – Mean of Annual Minimum Temperature ....................................................................................... 12

Figure 13 - Variance of Annual Minimum Temperature .................................................................................. 12

Figure 14 - Mean of Annual Maximum Temperature ....................................................................................... 12

Figure 15 - Variance of Annual Maximum Temperature Variance .................................................................. 13

Figure 16 - Mean of Annual Average Temperature ......................................................................................... 13

Figure 17 - Variance of Annual Average Temperature .................................................................................... 13

Figure 18 - Mean of Annual Minimum Temperature ........................................................................................ 14

Figure 19 - Variance of Annual Minimum Temperature .................................................................................. 14

Figure 20 – Total Precipitation Mean for Study Area ...................................................................................... 15

Figure 21 – Total Precipitation Variance for Study Area ................................................................................. 15

Figure 22 – RL1 Mean for Study Area ............................................................................................................ 16

Figure 23 – RL1 Variance for Study Area ....................................................................................................... 16

Figure 24 - Total Precipitation Mean for Extended Study Area ....................................................................... 16

Figure 25 - Total Precipitation Variance for Extended Study Area .................................................................. 17

Figure 26 - RL1 Mean for Extended Study Area ............................................................................................. 17

Figure 27 - RL1 Variance for Extended Study Area ........................................................................................ 17

Figure 28 - PCC of Total Precipitation Decadal Mean for Study Area ............................................................. 18

Figure 29 – TP Decadal Mean Scheme of Pixels selected for validation by Linear Regression ..................... 18

Figure 30 – Linear Regressions for TP Decadal Mean ................................................................................... 18

Figure 31 - PCC of Total Precipitation Decadal Variance for Study Area ....................................................... 19

Figure 32 - TP Decadal Variance Scheme of Pixels selected for validation by Linear Regression ................. 19

Figure 33 - Linear Regressions for TP Decadal Variance ............................................................................... 19

Figure 34 - PCC of RL1 Decadal Mean for Study Area ................................................................................... 20

Figure 35 - RL1 Decadal Mean Scheme of Pixels selected for validation by Linear Regression .................... 20

Figure 36 - Linear Regressions for RL1 Decadal Mean .................................................................................. 20

Figure 37 - PCC of RL1 Decadal Variance for Study Area ............................................................................. 21

Figure 38 - RL1 Decadal Variance Scheme of Pixels selected for validation by Linear Regression ............... 21

Figure 39 - Linear Regressions for RL1 Decadal Variance ............................................................................. 21

viii

Figure 40 – Dynamic Climate Component ...................................................................................................... 23

Figure 41 – Kriged Dynamic Climate Component ........................................................................................... 23

Figure 42 – Static Climate Component ........................................................................................................... 23

Figure 43 – Kriged Static Climate Component ................................................................................................ 24

Figure 44 – Final Climate Component ............................................................................................................ 24

Figure 45 - Kriged Final Climate Component .................................................................................................. 24

Figure 46 - Total Precipitation Mean for Extended Study Area ....................................................................... 25

Figure 47 - RL1 Mean for Extended Study Area ............................................................................................. 25

Figure 48 - Static Climate Component // RL1 mean over time ........................................................................ 26

Figure 49 - RL1 Variance for Extended Study Area ........................................................................................ 27

Figure 50 - Dynamic Climate Component // Slope of RL1 Decadal Variance ................................................. 27

Figure 51 - Climate Component // Desertification Susceptibility Indicator ....................................................... 28

Figure 52 – Google Earth Snapshot of a possible Forest area in Zimbabwe .................................................. 29

Figure 53 – Google Earth Snapshot of a possible Agricultural area in Zimbabwe .......................................... 29

Index of Tables

Table 1 – ECMWF Data Server Options ........................................................................................................... 9

Index of Equations

Equation 1 - Compiling a Desertification Susceptibility Indicator (www.desertwatch.info) ................................ 4

Equation 2 – Static and Dynamic Component of a Susceptibility Indicator ....................................................... 4

Equation 3 – Final Susceptibility Indicator ........................................................................................................ 4

Equation 4 – Static Climate Component, CS(x) ............................................................................................... 22

Equation 5 – Dynamic Climate Component, CD(x) .......................................................................................... 22

Equation 6 – Climate Desertification Indicator ................................................................................................ 22

1

Introduction

Background and Aim

The United Nations Convention to Combat Desertification (UNCCD) defines desertification as

“land degradation in arid, semi-arid and dry sub-humid areas resulting from various factors, including

climatic variations and human activities” (UN, 1994), like social, economic and biophysical factors,

operating at local, national and regional scales (Geist et al., 2004). These factors, furthermore, are

linked and often self-reinforcing, leading to the cross of thresholds beyond which the underlying

ecosystem cannot restore itself but requires ever-greater external inputs for recovery (Liniger et al.,

2008; GEO-4, 2007).

Along with climate change and biodiversity loss, land degradation is ranked by the Millennium

Ecosystem Assessment (MA) and The Global Environment Outlook (GEO-4) as one of the world’s

greatest environmental challenges, threatening environmental and food security and increasing

poverty. Its symptoms include soil erosion, nutrient depletion, salinity, water scarcity, pollution and

loss of biodiversity (Bai et al., 2008). Estimates depict that over 250 million people are directly

affected by desertification (UNEP, 2007).

As a global environment and development issue, an adequate structured approach, based on

quantitative, up-to-date data, and governed by appropriate international strategies and agreements is

needed to support policy and action for food and water security, economic development,

environmental integrity and resource conservation (Liniger et al., 2008; Bai et al., 2008).

Unfortunately, countries where the impact of desertification and droughts are felt the most are also

countries where available data, technological knowledge and tools are usually scarce. Therefore, to

ensure the long-term productivity of inhabited drylands it was recognized by the United Nations (UN)

the need for an internationally coordinated approach and thus the origin of the UNCCD (ESA, 2009).

The Convention is the main global agreement to address land degradation and the problems that it

brings for poor developing countries with a special emphasis on Africa (Gisladottir et al., 2005). Areas

of concern include tropical Africa, south of the equator and southeast Africa (GEO-4, 2007).

To help convention parties reporting to the UNCCD and also to aid policy making at local level, the

European Space Agency (ESA) launched, in 2004, the DesertWatch project (DW-O). This project

aimed to demonstrate the capability of developing a user-oriented information system (DW IS), mainly

based on Earth Observation (EO) data in combination with some ancillary data, to support

desertification monitoring at various scales (ESA, 2009). The DW-O project focused on Northern

Mediterranean countries (Annex IV countries) with study areas monitored in Italy, Portugal and

Turkey.

Following the success of DW-O, user support and explicit public request by country delegations of

Brazil and Mozambique (as reported in events such as COP7/8 and the GMES for Africa in Lisbon)

ESA decided to fund a project extension, the DesertWatch Extension (DW-E).

2

The main scope of the extension project is to adapt the DW-O to a global context, so to respond

to the needs of non-Annex IV countries and, in particular, of those emerging from developing

countries mostly affected by land degradation phenomena and where access to field measurements

is scarce and highly dispersed (DW-E, 2011; ESA, 2009). Therefore the challenge for DW-E, as part

of ESA Data User Element (DUE), is to develop an EO based tool, easy to use by national and local

authorities, automatic and as standardised as possible and suitable for multiscale monitoring (ESA,

2009).

The DW-E focuses on three different UNCCD regional areas, respectively: Mozambique for the

Annex I countries (Africa); Brazil for the Annex III countries (Latin America and the Caribbean) and

Portugal for the Annex IV countries (Northern Mediterranean). As stated, the main objective of the

project is to define and test a standard methodology and a processing chain based on EO technology,

in order to produce Land Use and Land Cover (LULC) maps and Desertification Indicators (DW-E,

2010). As such the analysis of Desertification Indicators is based on a combination of LULC maps

with additional bio-physical (vegetation and soil) and climatic factors to produce Desertification

Susceptibility information. The LULC analysis is conducted at three different scales: Regional

(1:1.000.000); National (1:50.000) and Local (1:25.000) (DW N2, 2011).

The end-product intended is a user oriented geo-information system, the DesertWatch Information

System (DW-E IS), which is to be provided to all Convention Parties allowing to monitor

desertification, droughts, land degradation and its trends over time (ESA, 2009; Armas et al., 2010).

The purpose of the present research study follows main objective of DW-E, to contribute to the

development of tools and aid management policies in less developed countries with lack of resources

and technological means, and the pursuit of recent results and obstacles experienced while

developing the DesertWatch Extension to Portuguese Partners, more specifically when applied to

Mozambique. Contrary to rendered in the case of Portugal and Brazil, the applied methodology in

Mozambique didn’t return the spatial patterns for the desertification phenomena as expected by prior

knowledge, with no continuity pattern from areas known to be more susceptible to areas less

susceptible. The main reasons pointed to support these results were the lack of surveyed data, that

would validate findings, and the geomorphology of Mozambique, pinning the discontinuities observed

probably due to the influence of the Indian Ocean throughout the eastern coast of the country.

To fully understand the results encountered and to validate the DW-E methodology, the previous

research area was broaden allowing the analysis of the phenomena at a regional scale. Since fully

testing the DW-E methodology to such a scale would imply the analysis of data (biophysical

components, vegetation and soil, by processing satellite imagery series) impossible to comply with

the time span of this report, plus being climate analysis the source of the unexpected results, this

research focused only on the climatic component for southeastern African countries

Before addressing the actual research subject it is necessary to be familiar with the methodology

addressed and to visualize the results that motivated this study, as such, a summarized DW-E

introduction and results display will follow.

3

DesertWatch Extension to Portuguese Partners

Study Area

Taking into account user requirements and the availability of satellite images, the three UNCCD

regional areas focused were defined: Portugal, Brazil and Mozambique, at a national, regional and

local scale3.

Methodology

Focusing on the Indicators Processing step of the overall model of the DW-E Information System4

it is clear that the main objective of the service chain is to allow to estimate desertification

susceptibility of a desired location, by spatial inference of the desertification indicators, based on

knowledge of values for climate, vegetation, soil and LULC classes at the same location (Pereira et

al., 2011):

Figure 1 – Indicator Processing Step of Simplified Logical Model (Pereira et al., 2011)

3 Appendix B shows Scales, Area of Work and Final Study Areas for the three UNCCD regional areas.

4 Appendix A shows Simplified Logical Model of the DW-E Information System

4

The methodology to derive a Desertification Susceptibility Indicator based on EO comprised the

following steps (Pereira et al., 2011):

1. Characterization of extreme climate dynamics (precipitation) and biophysical factors (vegetation

and soil) over time conditioned to land use/land cover;

2. Spatial characterization of a susceptibility index to desertification conditioned to the climate and

biophysical conditions.

The final indicator combines climate, C(x), Vegetation, V(i), and Soil, S(i), components

conditioned to LULC cartography as depicted in:

Equation 1 - Compiling a Desertification Susceptibility Indicator (www.desertwatch.info)

The variable I(x0) Є [0, 1], takes values at each location x0, defining the maximum value of

susceptibility to desertification at I(x0) = 1 and the lowest susceptibility to desertification at I(x0) =0.

The indicator is sub-divided into a static, IS, and a dynamic component, ID, each one of them

derived from the correspondent climate grid and combining the three main components (climate,

vegetation and soil) by the linear estimation with co-located cokriging displayed beneath, where C

stands for Climate and B for Biophysical factors (vegetation and soil):

Equation 2 – Static and Dynamic Component of a Susceptibility Indicator

Bearing in mind that both components were given the same weight, the final susceptibility indicator

is given by averaging both components (Pereira et al., 2011; www.desertwatch.info):

2

)()()(

xIxIxI DS

Equation 3 – Final Susceptibility Indicator

Biophysical characterization converged to the analysis of radiometric indicators, such as

parameter NDVI for Vegetation (Normalized Difference Vegetation Index) and Soil Brightness

(Tasselled Cap Transformation per Land Cover class) by LULC class (www.desertwatch.info),

bearing that “areas more susceptible to desertification tend to present lower levels of NDVI and

higher levels of Brightness when compared with areas less susceptible” (Pereira et al., 2011).

5

Climate characterization converged from the analysis of precipitation and temperature over time to

considering only total precipitation (TP) and extreme precipitation dynamics, conveyed by the study of

parameter RL1 (total number of days per year with precipitation below 1 millimetre) as a draught

index representing extreme dry conditions. Since there was no clear relationship between

temperature patterns over time and the desertification process in the three studied areas this

component was discarded (Pereira et al., 2011; www.desertwatch.info).

A static and dynamic component were derived from the normalized mean of the RL1 values over a

period up to 30 years, and the normalized slope of the temporal trend of RL1 variance over time

calculated on a moving window of 10 years, respectively (Pereira et al., 2011).

The LULC maps produced with biophysical and climatic factors are spatially validated by previous

knowledge gathered over study areas.

Data

Data used to pursue the above stated was derived from NASA Landsat images time series set

over 20 years and ERA-Interim reanalysis data set, from the European Centre for Medium-Range

Weather Forecasts (ECWF) (Pereira et al., 2011).

Climatic Series Results

Climatic series analysed allowed to categorize the areas with high and low susceptibility to

desertification, based on the mean and trend (slope) of the decadal variance of RL1 for each country.

Spatial distributions of parameters studied are displayed below5 .

5 Being the scope of the present research the study of the climate component, results regarding findings related to the

biophysical and end-results are not demonstrated.

Figure 2 – Static and Dynamic Climate Component for Portugal

6

Figure 3 - Static and Dynamic Climate Component for Brazil

Figure 4 - Static and Dynamic Climate Component for Mozambique

7

Underpinning the Problem / Defining Research Scope

The previously described climate series analysis rendered distinct results for Portugal, Brazil and

the focus area that motivated this research, Mozambique. Clearly, on the spatial distribution of static

and dynamic climate component, no particular pattern can be concluded in the case of Mozambique,

whilst in Portugal and Brazil a clear spatial behavior is identifiable, with a continuous pattern from

more to less susceptible areas (see figures above).

The results rendered by DW-E methodology were validated for Portugal and Brazil, by prior

evidence on the phenomena (for example, the Desertification Information System to support National

Action Programs, DISMED, produced a Susceptibility to Desertification map for Portugal that

sustained DW-E results6) but in Mozambique, due to lack of prior data, the validation step was

unattainable. Size, disposition and geographical location of the country can also contribute for the

results that emerged from DW-E, Mozambique is a relatively odd shaped country, a strip of land

located in southeastern Africa, bordered by the Indian Ocean, Tanzania to the north, Malawi, Zambia,

and Zimbabwe to the west, and South Africa and Swaziland to the south7. The influence of the Indian

Ocean throughout the eastern coast of the country was also pointed out as a probable cause for

results obtained.

The absence of a spatial continuous pattern infers that the results probably accounted the fringes

of the phenomena, leading to suspect that “hot spots” were located in nearby countries. As such, the

scope of this research was defined, by dislocating the study area further inland, hoping to reveal the

source of the results obtained by analysing the phenomena at a regional scale.

As stated before, the aim of this research study was strictly set to climate component analysis,

which was the underlining factor that caused the unexpected results. A full application of the DW-E

methodology to the new study area would be of interest but impossible considering the nature and

span of this research and the time required studying satellite image series for biophysical indicators.

6 Appendix F presents final indicator map produced for DISMED

7Appendix C and D show a summarized profile of each country neighbouring Mozambique and maps characterizing the main

features of their natural environment

8

Study Area

The present research focused on the study area defined below, based on previous DW-E grid of

about 150 x 150 km, as illustrated in Figure 5. Main countries enclosed in new study area are Zambia,

Zimbabwe, Botswana and South Africa.

Figure 5 – Study Area8

Figure 6 – Study area showing present and past research grids

8 Terrain Maps from GoogleMaps.com

9

Data

Dataset was extracted from the European Centre for Medium-Range Weather Forecasts9

(ECMWF) interim re-analysis data server for a period of 20 years, from 1990 to 2010, for selected

geographical area (data grid retrieved with pixel size of 1.5x1.5º).

The evolution of precipitation and temperature over time was analysed by downloading daily fields,

for hydrological years (1st of October to 30

th of September) defined with the following options

10:

Options Precipitation Temperature

Data Hydrological year (1st October to 30th September)

Time 00:00:00 and 12:00:00 All

Step 12 0

Parameter Total precipitation 2 metre temperature

Geographical Area North (-15º) South (-27º) East (30º) West (19,5º)

Table 1 – ECMWF Data Server Options

The ECMWF output product is a GRIB file that was decoded to ASCII text by the ECMWF Parser

tool, available at CERENA department11

.

9 ECMWF, www.ecmwf.int

10 Appendix G shows options panel of ECMWF data server

11 http://cerena.ist.utl.pt/

10

Method and Results

Considering the DW-E methodology outlined previously (Figure 1) a similar chain of events can be

assembled to summarize and simplify the basic procedures applied:

Figure 7 – Basic procedures followed by present research

Temperature

Temperature data retrieved from ECMWF enabled the determination of the following annual

indices12

:

Annual average temperature (ºC)

Annual minimum temperature (ºC)

Annual maximum temperature (ºC)

Basic statistics study of the annual indices discriminated above, focused on analysing means and

variances per year and grid node for each parameter. All spatial distribution of analysed parameters

was plotted using GIS software13

, as presented below.

Figure 8 – Mean of Annual Maximum Temperature

12

Appendix H shows samples of data compiled by pixel (1.5x1.5º) 13

ArcGIS 9/ArcMap version 9.3

Climate

Data

ECMWF Server

Susceptibility to

Desertification

Indicator

Climate

Components

Basic Statistics

Climate

Analysis

Set of

Indices

Static and

Dynamic Climate

Indices

Indices Discarded

TP

RL1

11

Figure 9 - Variance of Annual Maximum Temperature Variance

Figure 10 - Mean of Annual Average Temperature

Figure 11 - Variance of Annual Average Temperature

12

Figure 12 – Mean of Annual Minimum Temperature

Figure 13 - Variance of Annual Minimum Temperature

Merging research results with previous DW-E results, the same parameters displayed before now

in extended study area are as follows:

Figure 14 - Mean of Annual Maximum Temperature

13

Figure 15 - Variance of Annual Maximum Temperature Variance

Figure 16 - Mean of Annual Average Temperature

Figure 17 - Variance of Annual Average Temperature

14

Figure 18 - Mean of Annual Minimum Temperature

Figure 19 - Variance of Annual Minimum Temperature

Temperature information is important for climate analysis, but not necessarily for assessing land

degradation phenomena. Spatial characterization of temperature rendered no particular pattern

indicating, as presumed by DW-E project (Pereira et al, 2010), that there is no clear relationship

between temperature and desertification, resulting in the parameter to be discarded from further

climate analysis.

Precipitation

From total precipitation daily data retrieved, a set of indexes was determined:

RL1, number of days per year with precipitation below 1mm (representing extreme dry conditions)

RL10, number of days per year with precipitation below 10 mm (representing dry conditions)

R30, number of days per year exceeding a fixed threshold of 30 mm (representing wet conditions)

TP, total annual precipitation, referring to cumulative precipitation over the year (mm)

15

During analysis of this set of annual indices, RL10 and R30 were discarded since data was very

small or even null for most of the years studied14

.

The basic statistics study of the annual indices, for TP and RL1, focused on analysing means and

variances per year and pixel, of each parameter. All spatial distribution of analysed parameters was

plotted using GIS software15

, as presented below.

Figure 20 – Total Precipitation Mean for Study Area

Figure 21 – Total Precipitation Variance for Study Area

14

Appendix H shows samples of data compiled by pixel (1.5x1.5º) and sustains the discarding of RL10 and R30 15

ArcGIS 9/ArcMap version 9.3

16

Figure 22 – RL1 Mean for Study Area

Figure 23 – RL1 Variance for Study Area

Merging results with previous DW-E research, the same parameters displayed before now in

extended study area are as follows:

Figure 24 - Total Precipitation Mean for Extended Study Area

17

Figure 25 - Total Precipitation Variance for Extended Study Area

Figure 26 - RL1 Mean for Extended Study Area

Figure 27 - RL1 Variance for Extended Study Area

In order to assess main temporal trends of the precipitation indices chosen, TP and RL1 were

subjected to a statistic analysis of parameter average and variance through a 10 year moving window

over time. The Pearson Coefficient Correlation (PCC) was also determined for each parameter, and

linear regressions for each different tendency presented in order to validate the coefficient.

18

Figure 28 - PCC of Total Precipitation Decadal Mean for Study Area

Figure 29 –Scheme of Pixels selected for validation by Linear Regression

y = 0,006x - 11,507R² = 0,6275

0,150

0,250

0,350

0,450

0,550

1989 1991 1993 1995 1997 1999 2001

(Med Pixel 53)

y = -0,0002x + 0,8454R² = 0,0003

0,150

0,250

0,350

0,450

0,550

1989 1991 1993 1995 1997 1999 2001

(Min Pixel 48)

y = 0,0179x - 35,395R² = 0,9629

0,150

0,250

0,350

0,450

0,550

1989 1991 1993 1995 1997 1999 2001

(Max Pixel 49)

Figure 30 – Linear Regressions for TP Decadal Mean

19

Figure 31 - PCC of Total Precipitation Decadal Variance for Study Area

Figure 32 - Scheme of Pixels selected for validation by Linear Regression

y = 0,0011x - 2,2023R² = 0,7964

0

0,005

0,01

0,015

0,02

0,025

0,03

0,035

0,04

1989 1991 1993 1995 1997 1999 2001

(Max Pixel 66)

y = -0,0002x + 0,371R² = 0,039

0

0,005

0,01

0,015

0,02

0,025

0,03

0,035

0,04

1989 1991 1993 1995 1997 1999 2001

(Med Pixel 52 )

y = -0,0027x + 5,3642R² = 0,9126

0

0,005

0,01

0,015

0,02

0,025

0,03

0,035

0,04

1989 1991 1993 1995 1997 1999 2001

(Min Pixel 24)

Figure 33 - Linear Regressions for TP Decadal Variance

20

Figure 34 - PCC of RL1 Decadal Mean for Study Area

Figure 35 - Scheme of Pixels selected for validation by Linear Regression

y = 0,1245x + 6,6424R² = 0,036

250255260265270275280285290295

1989199119931995199719992001

(Max Pixel 64)

y = -0,7339x + 1746,9R² = 0,5618

250255260265270275280285290295

1989 1991 1993 1995 1997 1999 2001

(Med Pixel 53 )

y = -1,9094x + 4072,7R² = 0,954

250255260265270275280285290295

1989 1991 1993 1995 1997 1999 2001

(Min Pixel 26 )

Figure 36 - Linear Regressions for RL1 Decadal Mean

21

Figure 37 - PCC of RL1 Decadal Variance for Study Area

Figure 38 - Scheme of Pixels selected for validation by Linear Regression

y = 14,684x - 29150R² = 0,9056

0

100

200

300

1989 1994 1999

(Max Pixel 65)

y = -2,4836x + 5211,6R² = 0,0209

0

100

200

300

400

1989 1994 1999

Med (Pixel 5)

y = -41,926x + 84071R² = 0,8725

0

200

400

600

800

1989 1994 1999

(Min Pixel 56)

Figure 39 - Linear Regressions for RL1 Decadal Variance

22

Analysing Desertification through the Climatic Components

Recalling the DW-E methodology described before, and bearing the purpose of this research to

focus on climate components only, biophysical factors (soil and vegetation) were not evaluated and

as such the indicator for desertification analysis weighs only the climate factor.

Although total precipitation indices were considered from this point on only extreme indices, RL1,

will be accounted since throughout the DW-E experience this revealed to be the most significant

indicator of land degradation phenomena (Pereira et al, 2010).

The climate component, C(x), combines a static, CS(x), and a dynamic component, CD(x).

The static climate component, Cs (x), results of subjecting the previous displayed statistic RL1

mean (Figure 22 and 26) to a transformation so that high values of RL1 corresponds to values of

CS(x) close to 1, and low values and values of RL1 corresponds to values of CS(x) close to null, as

follows:

averageRLMinaverageRLMax

averageRLaverageRLMaxxCS

1 1

1 1)(

Equation 4 – Static Climate Component, CS(x)

The dynamic climate component, CD(x), is determined by transforming the slope of the temporal

trend of RL1 variance over time (displayed in figure 37) calculated on a moving window of 10 years,

so that negative slopes assume values greater than 0.5 and positive slopes assume values smaller

than 0.5.

otherwise

slope minx2

slope 1RL|slope min|

|slope min|slope1RL if0

)x(CD

Equation 5 – Dynamic Climate Component, CD(x)

As stated before and due to the fact that the climate analysis was the main objective intended by

this research, the compiling of the final indicator derives only from the climate components previously

described. Therefore, the final indicator was determined by averaging both static and dynamic climate

components (following methodology described in equations 1 to 3):

2

)()()(

xCxCxC DS

Equation 6 – Climate Desertification Indicator

23

Spatial distribution of components was plotted by using GIS software16

and the Kriging

geostatistical estimator tool rendered the final spatial patterns presented

Figure 40 – Dynamic Climate Component

Figure 41 – Kriged Dynamic Climate Component

Figure 42 – Static Climate Component

16

ArcGIS 9/ArcMap version 9.3

24

Figure 43 – Kriged Static Climate Component

Figure 44 – Final Climate Component

Figure 45 - Kriged Final Climate Component

25

Discussion and Conclusions

Considering the obtained results, the overall purpose of the research can be claimed successfully

reached. Referring to the end results already displayed, the main objective of sourcing the “hot spots”

of desertification and land degradation as expected from the DW-E project was clearly pinned down.

This finding is an important outcome for it clearly states that desertification and land degradation are

phenomena that need to be address at a proper scale to be fully understood.

Recalling results, the study of total precipitation and the extreme indicator RL1 rendered first

concurring information, being measures of the same event with opposite displays, showing first

patterns that were later underlined on the static component:

Figure 46 - Total Precipitation Mean for Extended Study Area

Figure 47 - RL1 Mean for Extended Study Area

26

The static climate component refers to the analysis of the mean of parameter RL1, over the total

period of time evaluated (hydrological years from 1990 to 2010).

Representing extreme dry conditions by evaluating the total number of days per year with

precipitation below 1 mm it gives a measure of occurring droughts over time. As such, the static

component, alias the transformation of RL1 mean, defined areas prone to desertification where it

takes values close to one.

As displayed below, it is clearly visible a pattern spreading from southwest towards the central

area, through Botswana and Zimbabwe, reaching Mozambique with the pattern observed but not fully

understood in previous DW-E project (Figure 4).

Figure 48 - Static Climate Component // RL1 mean over time

Recalling the geographical set for the study area, the static climate component findings overlap in

the southwest region with the desert eco-region or semi-desert biome (Appendix D) as defined by

UNEP in Africa Outlook Report and Africa Atlas (UNEP, 2006; UNEP, 2008) and specifically in

Mozambique, with the dry tropical climate classification (Appendix E) as seen in the National Action

Program of Mozambique for the UNCCD (UNCCD, 2002b).

Land degradation is a complex phenomenon that can be caused my multiple factors, with a distinct

particularity of displaying a dynamic behavior, not captured by temperature or mean values of

precipitation indicators. Variance, and particularly dynamic variance, has been proven to be the best

indicator for the land degradation behavior, regarding the way in which this phenomenon settles and

usually escalates with time.

Recalling results achieved, the study of the extreme indicator RL1 revealed an area of interest

showing high values of variance:

27

Figure 49 - RL1 Variance for Extended Study Area

The PCC RL1 Decadal Variance study (Figure 37 and 38) categorized main trends and showed a

negative slope for areas highlighted in the figure above. This negative trend of variance indicates

more homogeneous drought over time, thus revealing areas more susceptible to land degradation

and desertification.

The dynamic climate component refers precisely to the stated, and is of the uttermost importance,

for it will indicate if phenomena behavior tends to be more or less continuous in time, and therefore

indicates the homogeneity of land degradation process over time. This component refers to the

transformation of the slope of RL1 decadal variance, rendering areas prone to desertification where it

takes values close to one and less susceptible where it takes values close to null.

As displayed below, it is clearly visible a central area, in southeast Zimbabwe spreading to

Mozambique:

Figure 50 - Dynamic Climate Component // Slope of RL1 Decadal Variance

28

The combining climate final indicator identifies areas with high and low susceptibility to

desertification by averaging static and dynamic components. This weighting of both dynamic and

static climate components, pinpoints concerning areas where land degradation is not only possible

but is also probably escalating.

The final indicator categorizes areas with high susceptibility to desertification (indicator takes

values close to one) by having on average higher values and a trend towards greater homogeneity of

the temporal parameter RL1 (negative slopes) and areas less prone to desertification (indicator takes

values close to null) by having lower average values and a greater variability, which means that in

those areas, these extreme events are more frequent in recent times, thus increasing the temporal

variance.

As displayed below, a central area in Zimbabwe reaching Mozambique is highlighted as an area

with greater susceptibility:

Figure 51 - Climate Component // Desertification Susceptibility Indicator

Merely for illustration purposes, sample snapshots of a Google Earth overview of highlighted area

are presented. These particular snapshots were chosen for baring some similarity with LULC classes

discriminated by the biophysical factor analysis in DW-E to Mozambique (Appendix I). The end-

results of such analysis discriminated specific Land Use classes for each study area, being for

Mozambique the following: Forests and Shrubland for NVDI; Agricultural Areas and Natural

Grassland for soil.

29

Figure 52 – Google Earth Snapshot of a possible Forest area in Zimbabwe

Figure 53 – Google Earth Snapshot of a possible Agricultural area in Zimbabwe

Reaching a final susceptibility indicator, restrained to climate characterization, attests the

achieving of the main goal set for this study.

The overall DW-E methodology, and particularly the characterization of desertification by extreme

precipitation indices, was successfully validated as rendering a valuable insight on the dynamics of

such a complex phenomena as land degradation.

The spatial distribution of climate dynamic and static component, and the combining susceptibility

indicator to desertification, finally substantiated the previous results of the DesertWatch project and

more importantly, underlined the importance and need to address desertification phenomena, and its

impacts, at a proper scale in this case, a regional scale.

30

Acronyms & Glossary

ArcMap is the main component of Esri's ArcGIS suite of geospatial processing programs, and is used primarily to view,

edit, create, and analyze geospatial data. ArcMap allows the user to explore data within a data set, symbolize features accordingly, and create maps (Wikipedia, //en.wikipedia.org) Aridity index The long-term mean of the ratio of mean annual precipitation to mean annual potential

evapotranspiration in a given area ASCII American Standard Code for Information Interchange is a character-encoding scheme based on the ordering of

the English alphabet its' codes represent text (Wikipedia, //en.wikipedia.org) CERENA Centre for Natural Resources and the Environment (http://cerena.ist.utl.pt)

Climate change Any change in climate over time, whether due to natural variability or as a result of human activity.

(The UN Framework Convention on Climate Change defines climate change as “a change of climate which is attributed directly or indirectly to human activity that alters the composition of the global atmosphere and which is in addition to natural climate variability observed over comparable time periods.”) (GEO-4) Climate variability Variations in the mean state and other statistics (such as standard deviations and the occurrence

of extremes) of the climate on all temporal and spatial scales beyond that of individual weather events. Variability may be due to natural internal processes in the climate system (internal variability), or to variations in natural or anthropogenic external forcing (external variability). (GEO-4) CLIVAR Research Programme on Climate Variability and Predictability (www.clivar.org)

Desertification land degradation in arid, semi-arid and dry sub-humid areas resulting from various factors, including

climatic variations and human activities (UNCCD). This is land degradation in arid, semi-arid and dry sub-humid areas resulting from various factors, including climatic variations and human activities. It involves crossing thresholds beyond which the underpinning ecosystem cannot restore itself, but requires ever-greater external resources for recovery (GOE-4) DFID Department for International Development

DI Desertification Indicator

DISMED Desertification Information System to support National Action Programmes in the Mediterranean

(www.dismed.eionet.europa.eu/) Drough means the naturally occurring phenomenon that exists when precipitation has been significantly below normal

recorded levels, causing serious hydrological imbalances that adversely affect the land resource production systems (UNCCD) Drylands are all terrestrial regions where the production of crops, forage, wood and other ecosystem services are

limited by water (MA). Formally, the definition encompasses all lands where the climate is classified as dry subhumid, semiarid, arid or hyper-arid (based on Aridity Index values, UN).Areas characterized by lack of water, which constrain two major, interlinked ecosystem services: primary production and nutrient cycling. Four dryland sub-types are widely recognized: dry sub-humid, semi-arid, arid and hyper-arid, showing an increasing level of aridity or moisture deficit. Formally, this definition includes all land where the aridity index value is less than 0.65. See also Aridity index.(GEO-4) DUE Data User Element

DW DesertWatch

DW IS DesertWatch Information System

DW-E Extended DesertWatch

DW-E IS DesertWatch Extended Information System

DW-O Original DesertWatch

ECMWF European Centre for Medium-Range Weather Forecasts (www.ecmwf.int)

EO Earth Observation

31

ERA-Interim is the latest ECMWF global atmospheric reanalysis of the period 1989 to present. "Reanalyses are

produced using fixed, modern versions of the data assimilation systems developed for numerical weather prediction, they are more suitable than operational analyses, as those provided by LSA SAF, for use in studies of long-term variability in climate. ERA- Interim is a reanalysis of the global atmosphere covering the period since 1989, and continuing in real time. As ERA-Interim continues forward in time, updates of the archive will take place on a monthly basis." (DW-E) ESA European Space Agency www.esa.int

GEO-4 Global Environment Outlook 4 (www.unep.org/geo)

GIS Geographic hfformation System

GRIB a general purpose, bit-oriented data exchange format efficient for transmitting large volumes of gridded data

(WMO) Hydrological Year 1st October to 30th September

Land cover The physical coverage of land, usually expressed in terms of vegetation cover or lack of it. Influenced by

but not synonymous with land use Land degradation is a long-term loss of ecosystem function and services, caused by disturbances from which the

system cannot recover unaided (UNEP, GEO-4) The loss of biological or economic productivity and complexity in croplands, pastures and woodlands. It is due mainly to climate variability and unsustainable human activity.(GEO-4) Landsat The Landsat Program is a series of Earth-observing satellite missions jointly managed by NASA and the U.S.

Geological Survey. Landsat satellites have taken specialized digital photographs of Earth’s continents and surrounding coastal regions for over three decades, enabling people to study many aspects of our planet and to evaluate the dynamic changes caused by both natural processes and human practices (landsat.gsfc.nasa.gov) LULC Land Use Land Cover

MA Millennium Ecosystem Assessment

NAP National Action Programme

NVDI Normalized Difference Vegetation Index

PANCD Programa de Acção Nacional de Combate à Desertificação

PCC Pearson's Correlation Coefficient is defined as the covariance of the two variables divided by the product of their

standard deviations RL1 Number of days per year with precipitation below 1mm (representing extreme dry conditions)

RL10 Number of days per year with precipitation below 10 mm (representing dry conditions)

R30 Number of days per year exceeding a fixed threshold of 30 mm (representing wet conditions)

Susceptible drylands Susceptible drylands refer to arid, semi-arid and dry sub-humid areas. Hyper-arid areas (the

true deserts, with an aridity index of less than 0.05) are not considered to be susceptible to desertification because of their very low biological activity and limited opportunities for human activity. See also Drylands and Aridity index. TP Total annual precipitation, referring to cumulative precipitation over the year (mm)

UN United Nations

UNCCD United Nations Convention to Combat Desertification www.unccd.int

UNCED United Nations Conference on Environment and Development

UNEP United Nations Environment Program

UNU United Nations University (www.inweh.unu.edu)

WMO-CCL World Meteorological Organization–Commission for Climatology (www.wmo.int)

WOCAT World Overview of Conservation Approaches and Technologies

32

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