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
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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.
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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.
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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.
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Table of Contents
Acknowledgements ................................................................................................................................ iii
Abstract................................................................................................................................................... iv
Table of Contents ................................................................................................................................... vi
Index of Figures ..................................................................................................................................... vii
Index of Tables ..................................................................................................................................... viii
Index of Equations ................................................................................................................................ viii
Climatic Series Results………………………………………………………………………………………… 5
Underpinning the Problem / Defining Research Scope .......................................................................... 7
Study Area .............................................................................................................................................. 8
Data ........................................................................................................................................................ 9
Method and Results .............................................................................................................................. 10
Discussion and Conclusions ................................................................................................................ 25
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
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.
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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.
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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)
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
Armas, R., Caetano, M. , Carrão, H., Soares, A., Pereira, M.J., Gutierrez, A., Rocha, A., Pace, G., Zucca, C.,del Barrio, G. and Paganini, M., 2010. Earth Observation From Space To Support The UNCCD: The DesertWatch Extension Project. European Space Agency Living Planet Symposium 2010, Bergen, Norway. Armas, R., Dinis, J., Pereira, M.J., Rocha, A., Design Justification File / System Trade-off Analysis version 3 of the DesertWatch Extension to Portuguese Partners, CSW-DESERTW-2010-TNR-04091 Bai, Z.G., Dent, D.L., Olsson, L. and Schaepman, M.E., 2008. Global Assessment of Land Degradation and Improvement. 1. Identification by remote sensing. Report 2008/01, ISRIC – World Soil Information, Wageningen,
Correia, P., 2010. Modelação e Estimação - Uma introdução à geostatistica, numist
Costa, A, Durão, R., Pereira, M.J., Soares, A., 2008. Using stochastic space-time models to map extreme precipitation in southern Portugal. Natural Hazards and Earth System Sciences, July 2008. DFID, 2004. Report by the United Kingdom of Great Britain and Northern Ireland on measures taken to support the implementation of the United Nations Convention to Combat Desertification with a particular focus on affected developing country parties in Africa, Department for International Development, October 2004. Durão, R.M., Pereira, M.J., Costa, A.C., Delgado, J., del Barrio, G. and Soares, A., 2009. Spatial-temporal dynamics of precipitation extremes in southern Portugal: a geostatistical assessment study. International Journal of Climatology
DW-E, 2010. DesertWatch Extension to non Annex IV Countries, Newsletter Issue nº 1, June 2010, www.desertwatch.info
DW-E, 2011. DesertWatch Extension to non Annex IV Countries, Newsletter Issue nº 2, January 2011, www.desertwatch.info
ESA, 2009. DesertWatch Final Report 2009, European Spatial Agency, www.desertwatch.info
Geist, H. J., Lambin, E.F., 2004. Dynamic Causal Patterns of Desertification, September 2004 / Vol. 54 No. 9, BioScience 817
GEO-4, 2007. Global Environment Outlook GEO-4: Environment for Development, United Nations Environment Program, 2007, www.unep.org/geo
Gisladottir, G., Stocking, M., 2005. Land Degradation Control and Its Global Environmental Benefits, Land Degradation & Development 16: 99–112 Liniger, H., van Lynden, G., Nachtergaele, F. and Schwilch, G., 2008, A Questionnaire for Mapping Land Degradation and Sustainable Land Management, CDE/WOCAT, FAO/LADA, ISRIC - World Soil Information, Wageningen. Pereira, M.J., Roque, D., Benevides, P., Nunes, P., Armas, R. and Soares, A., 2011. Desertification Indicatos Derived From Earth Observation Data: Application to Portugal and Brazil, European Geosciences Union, General Assembly 2011.
Rosário, L., 2004. Indicadores de Desertificação para Portugal Continental, Direcção-Geral dos Recursos Florestais, Maio 2004
Soares, A., 2006. Geostatística para as Ciências da Terra e do Ambiente, Portugal: IST Press.
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UN, 1994. Final Text of the United Nations Convention to Combat Desertification in Those Countries Experiencing Serious Drought and/or Desertification, Particularly in Africa, General Assembly, September 1994. UNCCD, 2000a. United Republic of Tanzania Proposed National Action Programme To Combat Desertification, United Nations Convention to Combat Desertification. UNCCD, 2000b. The National Action Programme (NAP) In the Context of the UNCCD in Zimbabwe, The National Taskforce on the NAP Process in Zimbabwe. UNCCD, 2001. National Action Programme for Malawi for the United Nations Convention to Combat Desertification, www.unccd.int UNCCD, 2002a. Zambia National Action Programme, Ministry of Tourism, Environment and Natural Resources UNCCD, 2002b. Plano Nacional de Acção de Combate á Seca e à Desertificação, Ministério para a Coordenação da Acção Ambiental, 2002. UNCCD, 2004. National Action Programme for South Africa - Combating Land Degradation, Repulblic of South Africa Environmental Affairs and Tourism Department. UNCCD, 2006. Botswana National Action Programme to Combat Desertification, Department of Environmental Affairs Ministry of Environment Wildlife & Tourism, October 2006. UNEP, 2006. Africa Environment Outlook 2 - Our Environment, Our Wealth, United Nations Environment Program www.unep.org/dewa/africa
UNEP, 2007. United Nations Environment Program 2007 Annual Report , www.unep.pt.
UNEP, 2008. Africa: Atlas of Our Changing Environment, United Nations Environment Program www.unep.org UNU, 2007. Re-thinking Policies to Cope with Desertification - A Policy Brief based on The 2006 Joint International Conference: “Desertification and the International Policy Imperative”, The United Nations University, 2007, www.inweh.unu.edu.