Social and Economic Impact of Climate Change in Rural Hungary: Analysis and Monitoring Szerkesztő Dr. Kulcsár László University of West Hungary Press Sopron SOCIAL AND ECONOMIC IMPACT OF CLIMATE CHANGE IN RURAL HUNGARY: ANALYSIS AND MONITORING Edited by Dr. László Kulcsár
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
Soci
al a
nd E
cono
mic
Impa
ct o
f Clim
ate
Chan
ge in
Rur
al H
unga
ry: A
naly
sis
and
Mon
itor
ing
Szerkesztő Dr. Kulcsár LászlóUniversity of West Hungary Press
Sopron
SOCIAL AND ECONOMIC IMPACT OF CLIMATE CHANGE IN RURAL HUNGARY:ANALYSIS AND MONITORINGEdited by Dr. László Kulcsár
Social and Economic Impact of Climate Change in Rural Hungary:Analysis and Monitoring
Edited byDr. László Kulcsár
Agroclimate: Impact Analysis of the
Projected Climate Change
and Possible Adaptation in the Forestry
and Agriculture Sector
TÁMOP-4.2.2.A-11/1/KONV-2012-0013
Project Leader
Prof. Dr. Csaba Mátyás, Member of the
Hungarian Academy of Sciences
University of West Hungary
Faculty of Economics
Sopron
2014
Book Reviewers:
Viktória Szirmai
András Ruff
Márton Bruder
ISBN: 978-963-334-210-7
Foto: László Kulcsár
“We are not going to be talking about polar bears and butterflies,
we are going to be talking about how this issue of climate impacts
people in their backyards, in their states, in their communities.”
Chris Lehane, Politologist
Los Angeles Times, May 21. 2014
Acknowledgments
We are grateful to everyone who contributed to this publication: primarily to academi-
cian Csaba Mátyás, for his advice and evaluation during the project, to the reviewers for
their detailed and thorough opinions, to the students of the University of West Hungary,
Faculty of Economics, for their field work and their assistance in data processing.
We thank all the families of Zala county engaged in agricultural activities for their
contribution and for sharing with us their views and feelings about the climate change
and its impacts.
We thank for the valuable assistance of Mária Csete and Tamás Czira from the
National Adaptation Centre of the Geological and Geophysical Institute of Hungary.
Contents
Acknowledgments 4
Foreword 7
Why Does Socio-Economic Impact of Climate Change Matter? 8
László Kulcsár, Csaba Székely
Vulnerability of Society to Climate Change:
Development of the Methodology of Vulnerability Studies
from the Beginning to the ‘Climate Vulnerability Index’ 14
Judit Vancsó Ms Papp, Csilla Obádovics, Mónika Hoschek
Vulnerability of Society to Climate Change: Complex Review of
Social-Economic Vulnerability in Micro Regions of Zala county 25
Csilla Obádovics, Mónika Hoschek, Judit Vancsó Ms Papp
Vulnerability of Society to Climate Change: Analysis of
and adaptation. In: Economics and Management of Climate Change. Risks, Mitigation and
Adaptation. Springer
Kovács, András Donát (2007): A környezettudatosság fogalma és vizsgálatának hazai gyakor-
lata [The Concept of Environmental Awareness and its Practical Analysis in Hungary]. In:
Residential environment conference, University of Debrecen.
Malcomb, Dylan W, Elizabeth A. Weaver, Amy Richmond Krakowka (2014): Vulnerability mod-
eling for sub-Saharan Africa: An operationalized approach in Malawi. Applied Geography
48. 17-30.
McCarthy, J.J. – Canziani. O.F. – Leary, N.A. – Dokken, D.J. – White, K.S. 2001: Climate Change 2001: Working Group II.: Impacts Adaptation and Vulnerability.
14
McDowell, Graham, James D. Ford (2014) The Socio-ecological Dimensions of Hydrocarbon
Development in the Disko Bay Region of Greenland: Opportunities, Risks, and Tradeoffs
Applied Geography 46 98-110.
Parrotta, John A., Ronald L. Trosper (eds) (2012): Traditional Forest-Related Knowledge. sustaining
Communities, Ecosystems and Biocultural Diversity. Springer.
Ruiz-Mallén, Isabel, Esteve Corbera (2013): Community-Based Conservation and Traditional
Ecological Knowledge: Implications for Social-Ecological Resilience. Ecology and Society 18
(4):12
Trosper, Ronald L, John A. Parrotta (2012): Introduction: The Growing Importance of Traditional
Forest-Related Knowledge. In: Parrotta, John A., Ronald L. Trosper (2012): Traditional Forest-
Related Knowledge. sustaining Communities, Ecosystems and Biocultural Diversity. Springer.
Wolf, Johanna (2011): Climate Change Adaptation as Social Process. In: Ford, James D., Lea
Berrang-Ford (2011): Introduction. In: James D. Ford, Lea Berrang-Ford (eds): Climate Change
Adaptation in Developed Nations. Springer.
15
A Vulnerability of Society to Climate Change:Development of the Methodology of Vulnerability Studies from the Beginning to the ‘Climate Vulnerability Index’
Judit Vancsó Ms Papp, Csilla Obádovics , Mónika Hoschek
ABSTRACT: Apart from sustainable development, vulnerability is perhaps the other most popular definition, used in a large number of scientific research studies. In this study we review the devel-opment of vulnerability as a concept and the evolution of the vulnerability test methodology from the beginning to the current days by relying on the available international and Hungarian literature, focusing primarily on the vulnerability of society to the impacts of climate change. In our work we try to reveal the inadequacies that need to be eliminated in the future and that currently have a negative effect on the efficient use of the methodology
Keywords: climate change, vulnerability, adaptation, Climate Vulnerability Index
Development of the definition of vulnerability
Vulnerability as a concept has been known in science for a long time: in the past it was
used mostly by medical and biological sciences for a long time (e.g., Traquair, H.M. 1925;
Scharrer, E. 1940; Lewis, W.M. – Helms D.R. 1964), and became an interdisciplinary
concept from the 1980s. These days vulnerability analyses have a key role in environ-
mental risk assumptions, disaster prevention, studies dedicated to public health and
economic development and, especially in research focusing on the correlation between
climate change and adaptation (Füssel, H.M. 2005). Peter Timmerman (1981) was the
first to put the definition into the focus of studies dedicated to climate change as a result
of the then prevailing objectives of the World Meteorology Organization (WMO). WMO
conducted a key research for identifying the factors that make society at different level of
development vulnerable or adaptable to climate fluctuation and change. Timmermann’s
(p. 21.) definition: “vulnerability refers to the degree to what extent a system fails to re-
spond to risky and unfavorable events” has occurred in numerous versions to date, which
shows that the concept is as variable and hard to define as the concepts of sustainable
development and sustainability. In a study, published in 2009 Schroeder, D. – Gefenas,
E. reviewed the majority the previously used definitions (5 versions) and came up with
the following definition (p. 117): “to face the probability of occurrence of a pre-definable
effect without the availability of basic ability or knowledge, required for defence”. In the
end, the negative consequence of the impact and the inability of the system are included
in the latter definition, the same way as in Timmermann’s definition, only in a slightly
more sophisticated way. Consequently, when authors define vulnerability, they always
16
take into account a negative stress effect, which is known and may occur, and a system
that is unable to respond effectively to the impact.
The first report of the second task force of IPCC already used the concept of vulner-
1990), but then the phenomenon was limited more to mapping the effects of climate
change. From the third report besides impacts and adaptations the concept of vulnera-
bility has become an issue of key importance (McCarthy, J.J. – Canziani. O.F. – Leary,
N.A. – Dokken, D.J. – White, K.S. 2001). According to the task force in terms of climate
change a vulnerable system response sensitively also to slight changes occurring in the
climate (harmful effects appear) and the ability to adapt is severely restricted. In con-
trast, a flexible system and society is not sensitive to climate fluctuation or change and
is capable of adaptation.
Review of major experiments to measure vulnerability
Vulnerability is measured with a vulnerability index. The basis of the method was de-
veloped by Lino Briguglio (Briguglio, L. 1993) for establishing the vulnerability of small
developing island states. Briguglio’s index consisted of three components: exposure to
external economy environment, the “island” status and distance, and inclination for nat-
ural disasters. To define exposure to the external economic environment, he developed a
composite index of three elementary indicators (number of population, GDP, size of land),
based on the idea that vulnerability to the external economic environment primarily
depends on population density and the conditions of the economy. In the case of island
status and distance, the share of goods transportation in export revenues was included
in the index, while in relation to the inclination for natural disasters he used the figures
of damages caused by natural disasters as a ratio of GDP, prepared by the UN. Later
Briguglio modified and developed the indicator on several occasions (Briguglio, L. 1995,
1997; Briguglio, L.-Galea, W. 2003). Then the vulnerability indicator began to develop in
several directions and, apart from social, economic vulnerability analyses, the indicator
required for environmental vulnerability analyses, i.e. the Environmental Vulnerability
Index (EVI) was also developed in several projects between 1998 and 2004 (Kaly, U.L. et
al 2004). To define environmental vulnerability, the authors listed fifty indicators from
the areas of weather-climate, geology, geography, resources and services, and human
population. Vulnerability was approached from three aspects - risks, resistance and dam-
ages - while the results were shown on a scale of five (resistant - extremely vulnerable).
With the environmental problems, the first obvious examples relate to the vulner-
ability analyses dedicated to climate change, involving the development of “Climate
Vulnerability Index” in 2002. Wu, S.I. and his colleagues analyzed the vulnerability of
the coasts of New Jersey state in view of floods, coastal storms and sea level variation.
In their work they also analyzed the vulnerability of society, for which they took into
account the age structure of society, its breakdown by nationality and gender, the income
17
figures and the living standards. Several scenarios were prepared for the future changes
of the sea level.
While Wu and his colleagues analyzed the vulnerability of the population of one
state to the variation of sea levels, in her study Katharina Vincent (2004) compared
the vulnerability to shortage of water of certain countries of Africa. In her opinion the
social-economic impact of climate change is a complex correlation of social, economic,
political, technological and institutional factors. She calculated her index from economic
welfare and stability, demographic structure, institutional stability, infrastructure supply,
globalization processes and supply of natural resources.
Sullivan, C. – Meigh. J. (2005) also analyzed the vulnerability of society in relation
to problem associated with water stocks as a result of the climate change and, apart
from a few exceptions, extended their study to all countries of the Earth. The authors
stressed that the CVI index was also suitable for performing regional analyses within
the countries. The components of the index were selected by the authors according to
the following criteria (Table 1).
Table 1. Potential variables for inclusion as sub-components of the CVI
CVI components Sub-components/variables
Resources Assessment of (surface) water (and groundwater) availabilityEvaluation of water storage capacity, and reliability of resourcesAssessment of water quality and dependence on imported/desalinated water
Access Access to clean water and sanitation Access to irrigation coverage adjusted by climate characteristics
Capacity Expenditure on consumer durables, or incomeGDP as a proportion of the GNP, and water investment as a % of total fixed capital investment Educational level of the population, and the under-five mortality rateExistence of disaster warning systems, and strength of municipal institutions Percentage of people living in informal housingAccess to a place of safety in the event of flooding or other disasters
Use Domestic water consumption rate related to national or other standards Agricultural and industrial water use related to their respective contributions to GDP
Environment Livestock and human population densityLoss of habitatsFlood frequency
Exposure Extent of land at risk from sea level rise, tidal waves, or land slips Degree of isolation from other water resources and/or food sourcesDeforestation, desertification and/or soil erosion ratesDegree of land conversion from natural vegetationDeglaciation and risk of glacial lake outburst
Source: Based on Sullivan, C. – Meigh. J. (2005), edited by authors
18
The above example shows that the researchers of the topic did not think in a single
framework and that the indicators were selected according to different criteria, depending
on individual problems. It is understandable and acceptable if one thinks about why a rising
sea level generates vulnerability for the economy and environment on the coasts of New
Jersey, and why it is not a problem in the Sahel zone. Reversing the correlation: it is clear
that due to the risk of the population in the Sahel zone is vulnerable and the population of
the coasts of New Jersey are not affected by the problem. Global modeling of vulnerability
to climate change is therefore a problem given the possibility of a multilateral approach
to the issue, and difficulties of comparison. The analyses can capture the problem mostly
according to topics (e.g., concentrating only on water issues or soil degradation, biodiversity
changes, etc.), and not in a complex manner. Another factor that makes the issue more
complicated is that the processes associated with the social-economic impacts of the climate
change and part of the indicators used for measuring them may also change as a result of
factors other than climatic effects.
Table CCIAV assessment
Impact Vulnerability Adaptation Integrated
Scientific objectives
Impacts and risks under
future climate
Processes affect-ing vulnerability
to climate change
Processes affect-ing adaptation and adaptive
capacity
Interactions and feed-backs between multiple
drivers and impacts
Practical aims
Actions to reduce risks
Actions to reduce vulnerability
Actions to im-prove adaptation
Global policy options and costs
Research methods
Drivers-pressure-state-impact-
response (DPSIR) methods
Vulnerability indicators, past and present climate risks, risk estimates,
review of the results of develop-ment/sustainability policy perfor-mance, relationship of adaptive
capacity to sustainable development
Integrated assessment modeling, cross-sectoral
interactions, integration of climate with other drivers,
stakeholder discussions linking models across types and scales, combining assess-
ment approaches/methodsSpatial
domainsTop-down
global→localBottom-up
local→regional(macro-economic approach-
es are top-down)
Linking scales (global/re-gional) often grid-based
Scenario types
Exploratory scenarios of cli-mate and other factors, norma-tive scenarios (stabilization)
Scenarios related to social-eco-
nomic conditions
Adaptation analogues
from history,
Exploratory scenarios: exogenous and often endog-enous (including feedbacks)
An overall concept, which also provides a framework to vulnerability analysis related
to climate is included in the 4th IPCC report in 2007 (Parry, M.L. et al 2007). Although
the CCIAV climate change impact adaptation and vulnerability (summarized in Table 2)
does not provide any solution to the above problems, it points out that the analyses, which
previously concentrated only on impacts and vulnerability, should also take into account
potential responses and the adaptation capacity of the respective society. Consequently,
the CCIAV table intends to provide a complex framework for the analysis of the various
parameters (impact, vulnerability, adaptation) which are related to climate change and
were often managed separately and not in correlation before.
After the fourth IPCC report, more and more studies dealt also with the analysis of
the adaptation capacity (see e.g., Allison, E.H. et al 2009; Lioubimtseva, E. – Henebry.
G.M. 2009; Wongbusarakum, S. – Loper, C 2011), taking into account numerous related
factors, such as e.g., socio-cultural, economic and political conditions of a community
and related governance and institutional framework. According to the authors it is im-
portant to assess the status of the adaptation capacity because by improving adaptation,
exposure and sensitivity can be reduced.
Below, we shall review the Hungarian studies dedicated to the social and economic
impacts of climate change.
Review of the most important attempts to measure
vulnerability based on the Hungarian literature
The first Hungarian studies dedicated to the impacts on climate change on society
were conducted at the beginning of the new millennium (Budai Z. 2003, Szirmai V.
2004., 2005), but the VAHAVA report, which analyzed the estimated impacts of climate
change (Láng I. – Csete L. – Jolánkai M. 2007.) covered first the issue of adaptation
comprehensively. The team preparing the report was commissioned to assess the
impacts of climate change and vulnerability triggered by it, as well as the correlation
with the required responses. In the report the team presented in detail the potential
impacts of climate change and, underlying the importance of adaptation, made rec-
ommendations to elaborate adaptation strategies in the main documents of the sectors
of the national economy.
After the VAHAVA report, the studies focusing on the social-economic impacts
of climate change reflected traces of research in an increasingly diversified approach.
Apart from the analyses focusing on health impacts (heat stress, air pollution, strong-
er UV-B radiation, increasing allergy symptoms) (Kishonti K. et al 2007. Páldy A.
Málnási T. 2009, Páldi A.-Bobvos J. 2011), analyses describing problems in tourism
(ski tourism) (Szécsi N.-Csete M. 2011), agricultural production (milk production,
variation of yields of cultural plants) (Reiczigel J. et al 2009,), and nature protection
(bird migration routes, changes of Danube phytoplankton,( Kiss A. et al 2009. Sipkay
Cs. et al 2009) also appeared. In 2011 the Sociology Institute of MTA (Hungarian
20
Academy of Science) published a volume of studies (Tamás P.-Bulla M. 2011) dedicated
to “Risk and vulnerability - Environmental dimensions - Social aspects”. The polit-
ical discussion paper (NCCS 2013) prepared in preparation for the Second National
Climate Change Strategy as a response to the questions and recommendation of the
VAHAVA report, which stressed the promotion of adaptation as opposed to the impacts
of climate change already referred to a National Adaptation Strategy. The document
presents in detail the impacts of climate change on natural resources, and on human
and social-economic consequences (human health, agriculture, built environment,
transport, waste management, energy infrastructure, tourism, disaster prevention)
and, then following the presentation of specific vulnerability studies, lays down the
objectives, the direction of actions and tasks related to adaptation. The precedents of
the vulnerability analyses included in the document are described in the studies by
Pálvölgyi T. et al 2010, and Pálvölgyi T. – Czira T. 2011 and Pálvölgyi T. et al 2011.
The vulnerability analyses described in the document (Second NCCS) are based on
the CCIAV assessment, recommended by IPCC and described above and were devel-
oped by an international project CLAVIER (Climate Change and Variability: Impact
in Central and Eastern Europe) concerning, among others, the analysis of the impacts
of climate change on the ecological and built environment. In the course of the study,
the authors conducted district level vulnerability analysis in relation to drought, forest
fires and heat waves in towns.
They applied a multiple approach: the expected impacts were derived from exposure
(e.g., drought, f lood) and sensitivity (e.g., response of the vegetation cover to changes
in temperature), then the adaptability to the impacts was identified (the main steps
of the study are summarized in the following table). The degree of sensitivity, expo-
sure and adaptability was illustrated in a map. Vulnerability was determined by the
correlation between the impacts and adaptability: accordingly, the system with a little
climate impact and strong adaptability may be considered robust and has the smallest
vulnerability. In contrast, a system with a strong impact and weak adaptability is the
most vulnerable. The systems with weak adaptability even despite a small impact form
a transition; they are at risk. Systems that have a great expected impact and strong
adaptation are fragile.
The authors noted that the study was a pilot study and that the indicators for the
indices were selected subjectively. The main purpose of this method is to present how
to conduct any territorial vulnerability analyses according to indicators, specifically
designed for a particular problem and to present the results illustratively. Consequently,
the calculation of the indices should be revised and extended within the framework of
the methodology covered by the discussion paper. Following the approach presented by
the authors, we also made an attempt to conduct a vulnerability analysis for drought
primarily by extending the definition of adaptation capacity (more details in the second
part of the study). We deemed it necessary because in the presented examples it was
unclear to us whether we managed to find the most suitable indicators to capture the
problem in the calculation of the adaptation index for drought. The authors prepared
21
the index based on the assumption that bearing and compensation, as well as elimina-
tion of damages depend primarily on the economy of the region. Thus, the index was
calculated from the indicator reflecting the income generating capacity of the sector
and the agricultural support granted for 2003-2008 on one hectare of agricultural
area i.e., in that structure the adaptation capacity for drought would depend only on
economic factors and the knowledge, understanding of the problem of society and
irrigation options, etc. would be disregarded.
Table 3: Main steps of applying the CIVAS model
Phase 1: Impact bearers, indicators and calculation methods
step Complex climate problems and impact bearing systems. Description of the problems and their role in the development of local climatic vulnerability.
step Sensitivity indicators for each complex problem based on literature and expert estimates.
step Exposure indicators in line with sensitivity indicators based on fine resolution regional climate model results in the form of regional territorial averages.
step Decision on the method of calculating the estimated impact. Mathematical representation of the joint consideration of the sensitivity and exposure indicators (straight line combination)
step Definition of indicators describing adaptability, separately for each complex problem; based on the typical social-economic responses to the problem and information of the literature.
step Vulnerability calculation method. Mathematical representation of the joint consideration of the estimated impact and adaptability indicators (straight line combination)
Phase 2: Calculation, evaluation, analysis
step Production of indicators defined in Phase 1. Building a database from the mathematical values of the indictors defined in Steps 2, 3 and 5.
step Vulnerability calculation. Building a database according to Steps 4 and 6 of Phase 1.
step Analysis and evaluation of regional vulnerability. Definition of most vulnerable regions.
Source: Second National Climate Change Strategy (discussion paper) 2013.
Following the review of the Hungarian studies dedicated to vulnerability to so-
cial-economic impacts of climate change, we can conclude that, following international
professional trends, they also appeared in the Hungarian literature taking into account
not only the impacts of climate change, but also the issue of adaptation. Considering that
a complex adaptation strategy may first be presented in the envisaged Second National
Climate Change Strategy and that so far there have been very few studies concerning the
adaptability of society, further work would be required to analyze the knowledge and
general attitude of society to the impacts of climate change and the ideas of individuals
concerning adaptation.
22
Summary
Climate change as an ecological stress is one of the compelling forces that the impact
bearing society must find a way to adapt to. The efficiency of adaptation is determined
by the stability of the respective communities. These days that stability is measured with
vulnerability indices. The initial diversity of vulnerability analyses have developed into
a consistent framework of impacts, adaptation and vulnerability. However, due to the
impacts of climate change that appear in variable phenomena the stability-vulnerability
problem cannot be captured in a complex manner, only by focusing on a specific parame-
ter (e.g., water level change, floods, drought, forest fires). If not globally, at least nationally
it would be important to elaborate composite and complex indices in the methodology of
vulnerability analyses that are capable of simultaneously measuring the instability and
vulnerability of society to climate change.
References
Allison, E.H. – Perry, A.L. – Badjeck, M.C. – Adger, W.N. Brown, – K. Conway, D. – Halls, A.S. – Pilling, G.M. – Reynolds, J.D. – Andrew, N.L. – Dulvy, N.K. 2009: The Livelihood Vulnerability Index: A pragmatic approach to assessing risks from climate variability and change—A case study in Mozambique. Global Environmental Change 19. 1. pp. 74-88.
Briguglio, L. 1993: The Economic Vulnerabilities of Small Island Developing States. Study com-missioned by CARICOM for the Regional Technical Meeting of the Global Conference on the Sustainable Development of Small Island Development States, Port of Spain, Trinidad and Tobago.
Briguglio, L. 1995: Small Island States and their Economic Vulnerabilities. World Development, 23, 1615-1632.
Briguglio, L. 1997: Alternative Economic Vulnerability Indices for Developing Countries. Report prepared for the Expert Group on Vulnerability Index, UN(DESA).
Briguglio, L. and Galea, W. 2003: Updating and Augmenting the Economic Vulnerability Index. Islands and Small States Institute, Malta.
Budai Z. (2003): A globális időjárás-változás lehetséges hatásai a turizmusra [Potential Impacts of Global Climate Change on Tourism]. Turizmus Bulletin, 2003/1. pp. 23-27.
23
Füssel H.M. 2005: Vulnerability in Climate Change Research: A Comprehensive Conceptual Framework. Breslauer Symposium. University of California International and Area Studies, UC Berkeley.
Kaly, U.L., Pratt, C.R. and Mitchell, J. 2004. The Demonstration Environmental Vulnerability Index (EVI) 2004. SOPAC Technical Report 384, 323 pp.
Kishonti K, Páldy A, Bobvos J. 2007: A hőhullámok egészségre gyakorolt káros hatásainak is-merete Magyarországon a városi lakosság körében [Understanding the Harmful Impacts of Heat Waves on Health in Hungary among the Hungarian urban population]. Climate-21 Leaflets. 50, 12-27.
Kiss A., Csörgő T., Harnos A., Kovács Sz., Nagy K. 2009: Changes in the Migration of the Wood Warbler (Phylloscopus sibilatrix) from the Aspects of Climate Change. Climate 21 Leaflets,. 56, 91–99.
Láng, I. – Csete, L. – Jolánkai, M. (ed., 2007): A globális klímaváltozás: hazai hatások és válaszok: a VAHAVA jelentés [A global climate change: Hungarian impacts and responses; VAHAVA report]. Szaktudás Kiadó Ház, Budapest, ISBN 978-963-9736-17-7
Lewis, W.M. – Helms D.R. 1964: Vulnerability of Forage Organisms to Largemouth Bass. Transactions of the American Fisheries Society 93.3. pp. pp. 315-318.
Lioubimtseva, E. – Henebry. G.M. 2009: Climate and environmental change in arid Central Asia: Impacts, vulnerability, and adaptations. Journal of Arid Environments 73. pp. 963-977.
McCarthy, J.J. – Canziani. O.F. – Leary, N.A. – Dokken, D.J. – White, K.S. 2001: Climate Change 2001: Working Group II.: Impacts Adaptation and Vulnerability.
McG. Tegart, G.W. Sheldon, G.W. and Griffiths, D.C. 1990: Climate Change – The IPCC Impacts Assessment. Australian Government Publishing Service, Canberra, Australia 294 p.
M.L. Parry, O.F. Canziani, J.P. Palutikof, P.J. van der Linden and C.E. Hanson (eds) 2007: Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, 2007.: Impacts Adaptation and Vulnerability. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.
Páldy, A. – Málnási, T. (2009): Magyarország lakossága egészségi állapotának környezetegészségügyi vonatkozásai [Environmental Health Aspects of the Health Conditions of the Hungarian Population]. National Institute of Environmental Health, Budapest.
24
Páldi A.-Bobvos J. 2011: A klímaváltozás egészségi hatásai. Sebezhetőség – alkalmazkod-óképesség [Impacts of Climate Change on Health]. In: Tamás P.-Bulla M. (ed.): Sebezhetőség és adaptáció - A reziliencia esélyei [Vulnerability and adaptability - Chances of Resilience]. MTA Research Institute of Sociology, 2011. pp. 97-115.
Pálvölgyi, T. – Czira, T. – Dobozi, E. – Rideg, A. – Schneller, K. (2010): A kistérségi szintű égha-jlat-változási sérülékenység vizsgálat módszere és eredményei [Micro Region Level Climate Change Vulnerability Analysis. Method and Results.]. Climate-21 Leaflets, 2010. No. 62. pp. 88-103.
Pálvölgyi T. – Czira T. 2011: Éghajlati sérülékenység a kistérségek szintjén [Climate Vulnerability in Micro Regions]. In: Tamás P.-Bulla M. (ed.): Sebezhetőség és adaptáció - A reziliencia esélyei [Vulnerability and adaptability - Chances of Resilience]. MTA Research Institute of Sociology, 2011. pp. 237-253.
Pálvölgyi T. – Czira T. – Bartholy J. – Pongrácz R. 2011: Éghajlati sérülékenység a hazai kistérségek szintjén [Climate Vulnerability in Hungarian Micro Regions]. In: Bartholy J. – Bozó L. – Haszpra L. (ed.): Klímaváltozás – 2011. Klímaszcenáriók a Kárpát-medence térségére [Climate Change - 2011. Climate Scenarios for the Carpathian Basin]. MTA and ELTE Department of Meteorology, Budapest. pp. 235-257.
Reiczigel J, Solymosi N , Könyves L, Maróti-Agóts A, Kern A , Bartyik J. 2009: A hőstressz okozta tejtermelés-kiesés vizsgálata hőmérséklet-páratartalom indexek alkalmazásával [Review of Milk Production Loss Caused by Heat Stress by Using Temperature-Moisture Content Indices], Magyar Állatorvosok Lapja [Hungarian Veterinary Magazine]. 131, 137-144.
Scharrer, E. 1940: VASCULARIZATION AND VULNERABILITY OF THE CORNU AMMONIS IN THE OPOSSUM. archives of Neurology nad Psychiatry 44. 3. pp. 483-506.
Schroeder, D. – Gefenas, E. (2009): Vulnerability: Too Vague and Too Broad? Cambridge Quarterly of Health Care Ethics 18. 2. pp. 113-121.
Sipkay Cs, Kiss KT, Drégelyi-Kiss Á, Farkas E, Hufnagel L. 2009: Klímaváltozási szcenáriók el-emzése a dunai fitoplankton szezonális dinamikájának modellezése alapján [Analysis of Climate Change Scenarios based on the Modelling of the Season Dynamism of the Danube Phytoplankton]. Hidrológiai Közlöny,. 89, 56-59.
Sullivan, C. – Meigh. J. 2005: Targeting attention on local vulnerabilities using an integrated index approach: the example of the Climate Vulnerability Index. Water Science and Technology 51. 5. pp. 69-79.
25
Szécsi, N. – Csete, M. (2011): A turizmus szereplőinek klímaváltozáshoz való alkalmazkodá-sa a Szentendrei kistérségben [Adaptation to Climate Change of the Actors of Tourism in Szentendre Micro Region]. Climate-21 leaflets No. 65.pp. 64-86.
Szirmai V. (2004): A globális klímaváltozás társadalmi összefüggései. Készült A globális klímavál-tozással összefüggő hazai hatások és az arra adandó válaszok című MTA-KvVM közös kutatási projekt keretében [Social Aspects of Global Climate Change Prepared within the Framework of the Joint MTA-KvVM Research Project, Hungarian Impacts Related to Global Climate Change and Responses], Budapest.
Szirmai V. (2005): Globális klímaváltozás és a társadalmi biztonság [Global Climate Change and Social Security]. – Magyar Tudomány [Hungarian Science], 166. 2005. 7. pp. 849–857
Tamás P.-Bulla M. (ed.): Sebezhetőség és adaptáció - A reziliencia esélyei [Vulnerability and ad-aptability - Chances of Resilience]. MTA Research Institute of Sociology, 2011.
Timmermann, P. 1981: Vulnerability, Resilience, and the Collapse of Society. Environmental monograph No 1. Institute for Environmental Studies, University of Toronto.
Traquair, H.M 1925: The special vulnerability of the macular fibres and „sparing of the macula”. British Journal of Ophthalmology 9. 2. pp. 53-57.
Wongbusarakum, S. – Loper, C. 2011: Indicators to assess community-level social vulnerability to climate change: An addendum to SocMon and SEM-Pasifika regional socioeconomic mon-itoring guidelines. april, 2011. FIRST DRAFT FOR PUBLIC CIRCULATION AND FIELD TESTING
Wu, S.Y. – Yarnal, B. – Fisher, A. 2002: Vulnerability of coastal communities to sea-level rise: a case study of Cape May County, New Jersey, USA. Climate Research 22. pp. 255-270.
Second National Climate Change Strategy 2014-2025, an outlook to 2050. Discussion paper. September 2013. Downloaded: 14 February 2014. http://www.kormany.hu/download/7/ac/01000/M%C3%A1sodik%20Nemzeti%20%C3%89ghajlatv%C3%A1ltoz%C3%A1si%20Strat%C3%A9gia%202014-2025%20kitekint%C3%A9ssel%202050-re%20-%20szakpoliti-kai%20vitaanyag.pdf
National Drought Strategy - discussion paperhttp://2010-2014.kormany.hu/download/7/0a/90000/Aszalystrategia.pdfDownloaded: 15 July 2014
26
Vulnerability of Society to Climate Change: Complex Review of Social-Economic Vulnerability in Micro Regions of Zala county
Csilla Obádovics, Mónika Hoschek, Judit Vancsó Ms Papp
ABSTRACT This study is dedicated to the possible measuring of the impacts of climate change on social and economic processes and the review of the indicators and ratios that can be used in the measurements. The impacts of climate change on society cannot be measured in any straightforward manner, because social and economic processes exist also independently from the climate change and are affected by accidental factors. It is practically impossible to conclude whether or not a change in society is attributable to the climate change or another process, independent from it. Another factor casting a further shade on the issue is that all this represents two-way interaction: in the long run social and economic processes also have an impact on climate change (IPCC 2012). During our study we focused on the factors that determine the general condition of society, assuming that a stable, well-organised society is able to respond to the challenges of climate change with flexible responses.
KEYWORDS: sensitivity of society, adaptability, exposure, vulnerability, factors affecting the vul-nerability of society
Introduction
The literature contains numerous publications on the economics and economic aspects
of climate change. There are several approaches to the aspects of climate change that
can be quantified, projected or modeled with various statistical and economic methods.
Given the complexity of climate change, the social-economic correlations may be
revealed only with diversified analyses.
The MTA- (Hungarian Academy of Sciences) Adaptation to Climate Change Research
Group, and Mária Csete in the VAHAVA project have already analyzed the social-eco-
nomic impacts of climate change. However, their final research report does not contain
answers to the objectives outlined in the research concept.
In their studies they approached the economic correlations of climate change from
two aspects. They looked at the frequency, intensity and damages caused by weather
conditions (global warming, drought, torrential rains, mud flood, early and late frosts,
hail storms, hurricane type phenomena, etc.) and also focused on the different sensitivity,
vulnerability, bearing and reconstruction capacity of the sectors of the national economy,
settlements, regions and social groups.
They proposed four analytic and evaluation methods for concluding the character-
istics and size of the various types of damages:
• damages that cannot be expressed in monetary terms (e.g., biodiversity decay);
• damages extending in time and occurring later (e.g., treatment of illnesses);
• indirect damages (e.g., loss of export and markets due to the decay of orchards);
• direct damages (e.g., damages caused by various weather conditions).
27
There is an important conclusion, according to which measures and activities aimed at
decelerating the negative impacts of climate change and growth and development could
be parallel and simultaneous processes. This is facilitated by new technology solutions
and changes taking place in the structure of the economy.
“Exposure and sensitivity to climate change and vulnerability bearing and recon-
struction capacity include elements with an uncertain outcome and a potential impact
on economic risks. The quantification of the migration processes associated with climate
change may also be a challenge to researchers. Further studies may also relate not only to
social but also economic consequences of an increase in the distance between the groups
of society, boosted by climate change. The responses to climate change by a town and
rural areas and the benefits and disadvantages inherent in them.” (Csete 2006)
Various models have been developed to analyze the social-economic correlations of
climate change:
Local action sample model of adaptation to climate change.
Assessment of the regional risks of climate change and security.
Development of economic indicators to monitor the “climate protection perfor-
mance” of the key development trends.
Adaptation of environmental assessment methods (e.g., evaluation of natural
capital) to assess the “climate print” of local development efforts.
Development of a methodology for questionnaire-based representative surveys.
The results are contained in the Climate-21 booklets.
Climate change has an effect on numerous areas of social-economic processes, and
therefore there are:
impacts on tourism
impacts on health
the impacts on social-economic processes.
These impacts were studied by various researchers in different ways (Budai 2003,
Csete 2006, Szécsi-Csete 2011, IPCC 2012)
Impacts of climate change on tourism and health
In defining the development trends of tourism, apart from political, social and de-
mographic tendencies the impact of climate change on tourism has an important role
(Budai, 2003). Climate is one of the resources of tourism, because it affects the evolving
tourist services.
In their study dedicated to the same subject Szécsi and Csete (Szécsi, Csete 2011) investi-
gated whether or not the actors of tourism felt the impacts of climate change and how they
adapted to it. In the context of tourism and climate change international research focused
primarily on the potential consequences of any increase in the sea levels and the impacts
of climate change on ski tourism. Sensitivity to climate change is affected by the type of
tourism and the destination of the tourist trips. Business or conference tourism, visits to
28
relatives and health tourism are less sensitive than leisure, vacation, beach or ski tourism.
The “Djerba Declaration on Tourism and Climate Change” is dedicated to that topic.
The literature deals most with the health damaging impact of urban heat waves
within the context of the public health studies of climate vulnerability in small regions
The Zalaegerszeg micro region, which includes the county seats, is not vulnerable, is
the least sensitive to the impacts of climate change and has adequate adaptation capacity.
It is followed by Nagykanizsa micro region, which also has a large town type Centre. The
category of moderate vulnerability includes Keszthely micro region, with its castle, univer-
sity and high tourism potential, forming part of Balaton region and Hévíz micro region,
where the tourism potential and the thermal bath, equally attractive in winter and summer,
compensates for the sensitivity that stems from the older age structure.
The Lenti and Letenye micro regions are the most vulnerable in the county and belong to
the categories of exposure or strong exposure in every aspect. The Lenti micro region falls in
the category of strong exposure both in terms of exposure and sensitivity, and its adaptation
capacity is also low. The Pacsa micro region was classified into the group at risk due to its
high exposure and lack of adaptation capacity, while Zalakaros micro region was classified
there primarily due to lack of its adaptation capacity, even though in terms of exposure is
belonged to the group with moderate risk. Zalaszentgrót micro region belongs to the vul-
nerable group due to its sensitivity and the low degree of adaptation capacity. (Table 13).
Summary
The indirect impacts on climate change on society, such e.g., more frequent heat waves,
extreme weather conditions, forest fires, drought can be described, but the responses to
challenges, the adaptation capacity, the sensitivity to society depend on factors that de-
termine the social-economic processes also independently from climate change. In the
course of the vulnerability analysis applied in the study in the context of exposure - sensi-
tivity - adaptation capacity, we tried to take a look at the indicators that help illustrate the
general condition of a particular society, assuming that a stable community can come up
with flexible responses to compelling conditions that may accompany climate change. On
the basis of the result of the analysis it may be concluded about the micro regions of Zala
county that the majority of them are vulnerable due to their weak adaptation capacity.
Identifying the correlation between the indicators of social-economic processes, which
may be analysed in the long term and the weather indicators, coordinated in time is a
serious challenge for an analyst. The National Meteorology Service established 4 regional
models to estimate the climate change in Hungary and the Carpathian Basin. The cor-
relation between the climate models and the social-economic processes will constitute
the basis of the subsequent research phase.
References
Budai, Z. (2003): A globális időjárás-változás lehetséges hatásai a turizmusra [Possible Impacts
of Global Climate Change on Tourism]. Tourism Bulletin, 2003/1. pp. 23-27.
45
Csete, Mária (2006): A klímaváltozás társadalmi-gazdasági hatásai [Social-economic impacts of
climate change]. MTA-TKI Adaptation to Climate Change Research Group.
Szécsi, Nóra – Csete, Mária (2011): A turizmus szereplőinek klímaváltozáshoz való alkalmaz-
kodása a Szentendrei kistérségben [Adaptation to Climate Change of the Actors of Tourism
in Szentendre Micro Region]. Climate-21 leaflets No. 65.pp. 64-86.
Katharine Vincent (2004): Creating an index of social vulnerability to climate change for
Africa. Tyndall Centre for Climate Change Research and School of Environmental Sciences,
University of East Anglia Norwich NR4 7TJ. Tyndall Centre Working Paper No. 56 August
2004
Supin Wongbusarakum And Christy Loper (2011): Indicators to assess community‐level social
vulnerability to climate change: An addendum to SocMon and SEM‐Pasifika regional soci-
oeconomic monitoring guidelines. April, 2011. First Draft For Public Circulation And Field
TestinG
Láng, I. – Csete, L. – Jolánkai, M. (ed., 2007): A globális klímaváltozás: hazai hatások és válaszok:
a VAHAVA jelentés [Global Climate Change: Hungarian Impacts and Responses; VAHAVA
report]. Szaktudás Kiadó Ház, Budapest, ISBN 978-963-9736-17-7
Estimated health impacts of climate change. http://meteoline.hu/?m=214
Páldy, A. – Málnási, T. (2009): Magyarország lakossága egészségi állapotának
környezetegészségügyi vonatkozásai [Environmental Health Aspects of the Health Conditions
of the Hungarian Population]. National Institute of Environmental Health, Budapest.
Pálvölgyi, Tamás – Czira, Tamás – Dobozi, Eszter – Rideg, Adrienn – Schneller, Krisztián (2010):
A kistérségi szintű éghajlat-változási sérülékenység vizsgálat módszere és eredményei [Micro
Region Level Climate Change Vulnerability Analysis. Method and results.]. Climate-21 leaf-
lets, 2010. No. 62. pp. 88-103.
Kapronczai István (2010): Klímaváltozás – jövedelem-instabilitás – kibontakozás [Climate
Change - Income Stability - Progress]. Climate-21 leaflets, No. 59. pp. 32-37
IPCC (2011): SREX Special Report on Managing the Risks of Extreme Events and Disasters to
Advance. Climate Change Adaptation [Field, C.B.,et al eds.) Cambridge Univ. Press. UK
SREX Hungarian version: Climate Change Inter-Governmental Panel Theme Report on the
risk and management of extreme climate events. Summary for decision makers. Budapest
December 2011, Ministry of National Development
46
Vulnerability of Society to Climate Change: Analysis of Vulnerability to Drought in Zala Micro Regions
Judit Vancsó Mrs. Papp , Mónika Hoschek, Csilla Obádovics
ABSTRACT: In the first half of our study we reviewed the evolution of the vulnerability analysis method-ology from the beginning to the assessment of the potential social impact of climate change based on both foreign and Hungarian literature. This study presents the analysis of vulnerability, in the context of exposure, sensitivity and adaptation, to drought of the population living in the rural areas of Zala and connected to agriculture either in part or in full. Focusing on comparability and looking at the regional differences, we made our calculations at the level of micro regions.
Climate change as an ecological stress is one of the compelling forces that the impact
bearing society must find a way to adapt to. As participants of the TÁMOP-4.2.2.A-11/1/
KONV-2012-0013 “Agroclimate”3 project, our responsibility was to assess the potential
social impacts of the projected climate change in Zala county by using the previously ap-
plied vulnerability analyses. On the basis of the results of this questionnaire-based survey
conducted in the county to assess the impacts of the climate change on agricultural soci-
ety, and the indicative national documents describing the estimated impacts of the pro-
jected climate change (Láng I. – Csete L. – Jolánkai M. 2007; Nemzeti Éghajlatváltozási
Stratégia (National Climate Change Strategy - NCCS) and the second planned NCCS)
and the publications (e.g., Pongrácz et al 2009; Sábitz J. et al 2013; Gálos 2014)) we think
that the local population will have to face two significant problems in the future: less an
more unevenly distributed precipitation and more frequent years with drought, caused
by the global warming, as well as increasingly occurring flash floods, also caused by the
uneven distribution of precipitation. This study is dedicated to the review of the problems
caused by increasing droughts affecting the agricultural population.
Exposure and sensitivity
To define vulnerability to drought we relied on the methodology applied in the previ-
ous vulnerability studies described in the first half of this publication (Pálvölgyi et al
2010, Pálvölgyi T. – Czira T. 2011; Pálvölgyi et al 2011; NCCS 2) to which we made some
3 “Agroclimate: Impact Analysis of the Projected Climate Change and Possible Adaptation in the
Forestry and Agriculture Sector”
47
modifications, primarily in the calculation of the adaptation capacity of the society living
in the rural areas of Zala county. The vulnerability of the Zala agricultural population
to drought in the context of exposure - sensitivity - adaptation was defined by using the
following summarized parameters (Table 1).
Table 1. Indicators used in the calculation of sensitivity to drought
Impact Adaptation
Exposure Sensitivity
PaDI
– certain physical and water management features of soils:field capacity, dead water content, use-ful water stock, water absorption capac-ity and hydraulic conductivity of the soil, stratification of the soil section, features causing the special water balance and water retention capacity of the soil
– knowledge and information concerning adaptive agriculture (technology and change of species)
– accessibility of water, available for irrigation – direct and indirect agricultural support by
farm – HDI– Indicator calculated from the above indices
Source: Based on the data published by Pálvölgyi T. et al 2011;
CARPATCLIM and ENSEMBLES EU-FP6, edited by the author
49
The national effect resulting from exposure and sensitivity is neither week, nor ex-
tremely strong. On national scale the increase in drought indices fall in the three weakest
categories on a scale of five, the sensitivity to drought of the soils varies between the two
extreme categories (see e.g., Pálvölgyi T. et al 2011). Consequently, a slight increase in
drought affects strongly and occasionally slightly sensitive soils in the micro regions of
the country. That means that, compared to the national average, local farmers will have
to face occasionally stronger and occasionally weaker impacts in terms of drought. All
in all, the impact on the whole county will be moderately strong.
Calculation of adaptation capacity components
We tried to apply a different method to calculate the adaptation capacity of the agri-
cultural population of Zala county to the one described in the previous studies. In the
studies referred to above, the authors defined the adaptation capacity to drought based
on the assumption that the impacts summarized above cause damage and coping with
them, compensation for them and the elimination thereof depend on the economic
conditions of the region. They used a complex index as an indicator that contains the
agricultural gross added value (GAD) calculated with an expert estimate for the users
of the agricultural area of the district (individual farms, business associations) as an
index illustrating the income generating capacity of the sector and the total agricultural
support granted for one hectare of agricultural area between 2003 and 2008. Obviously,
the factors indicated above are also required for adaptation but, in our opinion, adap-
tation does not depend primarily on the subsequent management of problems (damage
elimination) but on prevention, which is not necessarily cost intensive.
We used the results of the questionnaire-based survey and interviews conducted by
our project among the residents of the rural areas of Zala county to calculate our own
indicator. The survey focused on the sensitivity to problems related to climate change and
adaptation capacity of the local society. In summary, it may be concluded that almost 90
percent of the respondents have experienced size of the climate change, reflected mainly
in summers with drought, more uneven distribution of alteration of the seasons. They
found that these changes had a negative impact on their economic activities. Only 30 %
of the respondents think that they would be able to adapt somehow to the consequences
of climate change. They identify adaptation as an environment aware lifestyle (approx-
imately 20%), irrigation (approximately 60%) and in the alteration of the currently used
business habits5, i.e. in adaptive agriculture (i.e. 17% of all respondents are aware of and
indicated that latter response).
5 Changes in the sowing structure, the sowing time and the cultivated plants, foil tent against
frost, plant coverage, net against hail, more effective spraying, collection of rainwater, covering the soil
with plants instead of ploughings that promotes drying, drainage gutters. Increasing the ratio of forests,
building water reservoirs for irrigation purposes.
50
On the basis of the responses, first we tried to analyze more the irrigation issue, which
is a complex problem both in Zala county and nationwide. Based on what is stated in the
National Drought Strategy discussion paper, we can declare that due to the likely decline
in the volume of precipitation in future, the volume of surface and sub-surface waters
will decrease. Hence the obvious solution in terms of adaptation would be prevention,
in the form of increasing the water storage capacity of soils, reducing evaporation with
adequate agricultural methods and cultivating plants, capable of adapting to different
volume of precipitation. As the availability of water for irrigation will decrease in future,
its effective, multi-purpose6 forms should be used only where it is absolutely necessary.
As the water retention capacity of hilly areas is much lower than that in flat areas, ad-
aptation through the change of species and technology would be especially important
in Zala county. In total, we can state about irrigation that compared to the solutions of
adaptation to drought listed above, irrigation would be a less ideals solution. However,
the options in that regard have not yet been used widely in Zala county.
On the basis of the NYUDUVIZIG7 data supply, it is clear that irrigation is not a
typical agricultural activity in Zala county. According to the Directorate, the size of ir-
rigated area is only approximately 300 hectares in the whole county, and only 53 farmers
are registered who extract water from surface or sub-surface sources for irrigation pur-
poses, but they actually use only approximately 1/3 of their reserves. Generally, farmers
cultivating more water intensive plants use irrigation (orchards and nurseries growing
decorative plants). In theory, therefore, there are still significant reserves in Zala county
for irrigation. As the simplest way of irrigation is to extract water from surface sources,
those agricultural areas are in an advantageous situation, which are situated close to
surface waters (river, lake, irrigation canal). Water may be extracted from surface sources
with less energy consumption compared to water extracted from sub-surface sources pro-
viding that water is not far from the area to be irrigated. In areas that are far away from
surface waters, sub-surface water extraction and building water reservoirs on hills could
be a solution if there are no irrigation canals. In relation to sub-surface water extraction,
it needs to be noted that when the volume of water extracted exceeds 500 m3/year, the
wells require a licence and aquifer water may be used for irrigation only in justified
cases (Act LVII of 1995 (Vgt.) Article 28 (1) and Decree of the Minister of Environment,
Telecommunications and Water Management No. 18/1996. (VI. 13.) KHVM). It is clear
that irrigation is feasible near surface waters. For the time being water reservoirs estab-
lished in hills for irrigation purposes are used for flood protection purposes.
Based on our questionnaire, it seems that although there would be irrigation demand
in Zala county, the low ratio of currently irrigated areas and parties involved in irriga-
tion suggests that a large number of those interested in irrigation are unable to use the
6 Irrigation is not simple water replacement but it is also ideal for nutrition dosage, as an anti-de-flation measure, and is also effective for making seeds grow, protecting against frost, delaying flowering, coloring, refreshing, for noble rot and plant protection purposes.
7 West Transdanubia Water Management Directorate
51
theoretically available options. In our opinion, there are two reasons for that: years with
droughts, when irrigation as a demand occurs only periodically. The other reason is
that irrigation as an investment, as well as the water charges payable by the farmer cause
additional expenses to the business. As for the time being, droughts occur in Zala only
periodically, it is understandable that for the time being farmers do not undertake the
additional expenses of irrigation. Hungary itself falls in the conditional irrigation zone,
where most plan species can be grown without irrigation. These days, the importance
of irrigation is the reduction of the fluctuation of yield and increasing the volume, value
and in most cases, the quality of the product (as stated in the drought strategy). Irrigation
is not a traditional or typical agricultural activity in Zala county. That supports even
more our conclusion that instead of irrigation, primarily adaptation with the change of
species and technology should be encouraged in Zala county. Although very few of the
respondents are aware and use those techniques, those few farmers reported positive
results, higher average yield and successful efforts to overcome any variation in yield
caused by uneven distribution of precipitation.
Based on the above, the most important indicator reflecting the adaptation capacity
of society is the status of knowledge about adaptive agriculture (change of technology
and species). Our survey was the expert estimate in that regard. During the calculation
of the indicator we identified the ideal situation when all farmers are all aware of the
outlined options of adaptation as 100%. However, as only 17% of the respondents are
aware of that, the indicator was low, and 17% at county and micro region level. As it is
the most important indicator, the largest weight is assigned to it during the calculation
of adaptation capacity. It may be modified, but cannot be changed significantly by the
other indicators.
The theoretical feasibility of irrigation is likely to be higher than its current utili-
sation, considering that irrigation in the county is negligible. The size of the currently
irrigated areas may probably be increased not as much by boring wells as by extracting
more water from surface sources. However, due to the reasons outlined above, the
theoretical opportunity cannot be included in the calculation, considering that there
are better solutions than irrigation in the county and the technical conditions are not
in place. Contrary to the areas of the Great Plain, this region did not build irrigation
canals apart from the facilities designed for irrigation on the previously drained ag-
ricultural areas of Kis-Balaton (Small Balaton) (Small Zala irrigation system) and the
Hévíz main irrigation canal, which can no longer be used either. Apart from that,
according to certain calculations, the increase in temperature, which will also affect
evaporation, coupled with a decrease in precipitation, may radically modify the water
yield of rivers: in an extreme case, 3°C average temperature increase and 5-10% de-
crease in precipitation can reduce the volume of rivers on agricultural areas by 50%
(Csáki P. 2013). Based on the above, it also arises as an issue whether or not irrigation
is relevant in Zala county. If we take into account that droughts and the frequency of
drought periods are likely to increase in the future, and that the water intensive cultures
(orchards) are equally present in the Zala hills as certain water intensive field crops
52
(hybrid corn, sweet corn, Zala county regional development concept, TEIR database)
in our opinion there may be more persistent demand for irrigation in future, assuming
that certain plants cannot be replaced by species, more tolerant to drought. As irriga-
tion is primarily associated with surface waters, we calculated the indicator based on
the ratio of surface waters (rivers, canals and stagnant water), and non-irrigated plough
land in the particular micro region. We used the CORINE surface cover data of the
TEIR database in our calculation. The 2.1 ratio of the Nagykanizsa micro region e.g.,
reflects the scope of surface waters calculated as a percentage of the total not irrigated
plough land within the territory of the micro region. Naturally, the indicator is not
perfect because the scope of waters cannot be used as the basis to conclude the volume
of water in them, but it shows well the accessibility and limitation of accessibility to
surface waters. Even now only assumptions can be made in relation to water reserves,
available for irrigation purposes, and therefore we did not endeavor to calculate quanti-
tative limits for the present, and especially for the future. As at present rather technical,
especially financial limitations apply to irrigation rather than the limited availability of
resources, we also looked at the additional income the particular micro regions could
earn with agricultural support. It may be assumed that micro regions able to apply for
support are more competent than the less successful ones. Most applications related
to non-direct standard area-based support, in relation to which readiness to act may
also be assumed, because access to indirect support is often a great challenge due to
the implementation and complexity of the application procedure. The calculation was
made for 2010, because data are available not only for calls, but also for the number of
farmers in that year (our database was also built from TEIR database).
The number of applications by farmer is rather low (Table 3). The county average
is 23%, i.e., only 23% of the total registered farmers receive some agricultural support.
It is probably due to the large number of farmers operating on small farms, than the
lack of successful applications (almost twenty thousand farmers are registered in Zala
county). The distribution around the average is not significant: the ratio is the lowest in
Nagykanizsa micro region (18%), and the highest in Lenti micro region (35%).
The support granted t o one farmer (HUF 540,000 as a county average) varies more
between micro regions, even if the differences are not significant. Accordingly, Pacsa
micro region is in the most advantageous situation (HUF 1,000,000), while Hévíz micro
region is in the least favorable situation (HUF 230,000).
The ratio of direct area-based and indirect agricultural support in the county is
48%-52%, dominated by direct support. The amount of granted support is shared by the
two types of calls similarly. In most micro regions, the direct area-based support ratio
is higher than the ratio of indirect support. The ratio of indirect support is greater than
50% only in the Zalaegerszeg (50.7%), Nagykanizsa (56%) and Lenti (66%) micro regions
i.e., in our approach these three micro regions are considered to be performing better.
In all three cases the indicators were calculated by comparing the performance of
the micro regions to the best performing micro region, which was considered as 100%.
53
Table 3. Support applied by farmer, winner applications and
ratio of indirect support in the Zala micro regions
Micro region Support per farm-er (HUF)
Ratio of farmers re-ceiving support among
the total farmers (%)
Ratio of indirect support within total support (%)
Hévíz 228 149 20 45
Keszthely 578 083 27 49
Lenti 719 633 3-5 66
Letenye 378 965 15 38
Nagykanizsa 498 170 18 56
Pacsa 997 122 21 45
Zalaegerszeg 484 626 25 51
Zalakaros 404 692 21 45
Zalaszentgrót 561 744 24 42
Source: Based on MVH data, edited by the authors
As shown above, it seems that adaptation depends primarily on human factor, knowledge
and preparations; we also took into account those factors in our index by using the Human
Development Index (HDI). HDI an aggregated index, whose components are life expectancy
at birth, school qualifications and per capita GDP. As the latter component cannot be gen-
erated at micro region level, it was replaced by the per capita income (Obádovics, Kulcsár
2003). In our calculations HDI varies between 0 and 100. The HDI value of Zala micro re-
gions vary between 24 (Zalakaros) and 64 (Keszthely), the average of the nine micro regions
is 48, which is much lower than the ideal maximum value. Similarly to the calculation of
support, we used the value of the micro region with the best HDI index as 100% (Table 4).
Adaptation capacity calculation
During the adaptation capacity calculation, we calculated the weight average of the above
indicators, where 60% weight was assigned to the indicator reflecting the status of adaptive
agriculture, 20% weight was assigned to the indicator reflecting accessibility to surface waters
and 5-5 % weight was assigned to the other four indicators (support related indicators and
HDI). In our opinion the first indicator is fundamental in terms of adaptation. Irrigation
may be an increasing demand in future, but traditionally and at present it is not an agricul-
tural activity, typical in Zala county and as droughts increase, the usable water stocks may
reduce, therefore average weight was assigned to it. The support-related indicators are not
dominant factors in this respect, yet they reflect the competence and flexibility of farmers,
which are important characteristics in adaptation and therefore it cannot be ignored. The
54
parameters forming the HDI illustrate well the general state of a particular society. This
affects the adaptation capacity rather indirectly, in our opinion the index can still not be
ignored: former studies (Pappné Vancsó J. 2014) already showed that adaptation to external
stress depends primarily on the stability and degree of organization of the local society.
As for the time being, the knowledge and information about technology and the change
of species required for adaptation are very low, it also determines the value of the indicator.
The last column of the table shows that none of the micro regions of Zala county have good
adaptation capacity, which could be remedied primarily with the training and education
of the farmers. If irrigation becomes considerable demand in the future, the micro regions
may find that they are not prepared for it, which is understandable considering that irriga-
tion for agricultural purposes has no tradition in the region. The state of the population in
the rural areas of Zala county is around the national average in terms of the HDI, which
reflects school qualifications, income and health conditions, but is far from the ideal sit-
uation. Apart from professional training organized for the farmers, a higher HDI could
also contribute to a further increase in the adaptation capacity.
Table 4. Adaptation capacity calculation for the micro regions of Zala county
Micro region
State of adaptive
agri-culture (change
of species and tech-nology, %)
Ratio of surface water in the terri-
tory of not irrigated plough
land (%)
Support amount
per farmer com-
pared to the best perform-ing micro region (%)
Ratio of farmers
receiving support within
the total farmers
com-pared to the best perform-ing micro region (%)
Ratio of indirect support within
total sup-port com-pared to the best perform-ing micro region (%)
HDI com-pared to the best perform-ing micro region (%)
Adap-tation
capacity (%)
Hévíz 17.0 1.2 23.0 58.0 68.0 93.66 12.0
Keszthely 17.0 5.0 58.0 77.0 74.0 94.10 16.0
Lenti 17.0 0.2 72.0 100.0 100.0 79.53 18.0
Letenye 17.0 0.9 38.0 42.0 58.0 45.58 9.0
Nagykanizsa 17.0 2.1 50.0 51.0 84.0 91.64 14.0
Pacsa 17.0 5.0 100.0 60.0 67.0 47.24 15.0
Zalaegerszeg 17.0 0.5 49.0 70.0 77.0 100.00 15.0
Zalakaros 17.0 5.0 41.0 59.0 69.0 35.62 11.0
Zalaszentgrót 17.0 0.3 56.0 67.0 64.0 57.74 12.0
Source: Based on the data of NYUDUVIZIG data supplies, TEIR database, MVH public
database, CSO data and field questionnaire based survey, edited by the authors
55
Defining the degree of vulnerability
The degree of vulnerability was established as a relationship between the degree of im-
pacts and the status of adaptation. Following the system of former vulnerability reviews
and the method described in the first part of the study, a distinction is made among
robust, endangered, fragile and vulnerable systems. As in our interpretation, the adap-
tation capacity is weak in each micro region, the robust and fragile category cannot be
used definitely. As practically there is no difference between the micro regions in terms
of adaptation, the degree of vulnerability is determined by the strength of the impacts. If
the impact is mild or moderate, we can only talk about risk and not vulnerability, while
the particular system i.e., in this case the agricultural population, might become vulner-
able if the impact is average or stronger. By increasing the adaptation capacity, risk and
vulnerability may make a system “robust” or “fragile”. In Zala county these objectives
may be achieved by making available the information required for adaptive agriculture,
extending irrigation options and disseminating multi-purpose effective plant cultiva-
tion where absolutely necessary. In relation to the storing of water, nowadays mostly for
regulating surface water courses and flood protection consideration should also be given
to the utilization of the reservoirs for irrigation in the future.
The farmers of Zala county may experience generally strong impacts by the end of this
century, yet for the time being they do not have any understanding or technical skills for
appropriate adaptation. Consequently, despite regional disparities in total the agricultural
population of Zala county should be classified into the category at risk.
Table 5: Vulnerability of the agricultural population
to drought in Zala Micro Regions
Micro region impact (1980-2100) adaptation capac-ity (at present)
Source: Based on the data of NYUDUVIZIG data supplies, TEIR database, MVH public
database, CSO data and field questionnaire based survey, edited by the authors
56
Summary
On the basis of the results of our survey of vulnerability of the agricultural population of
Zala county to drought, interpreted in the context of impacts-adaptation capacity, we can
conclude that the degree of vulnerability is determined by the strength of the impacts,
because practically there is no difference among the micro regions in terms of adaptation.
The agricultural population of each micro region, which has weak adaptation capacity,
will experience moderate, average and increasingly strong impacts in micro regions and
average or moderately average impacts in the whole county. Thus, the vulnerability of
the individual micro regions may be classified into the increasingly vulnerability and at
risk categories. By increasing the adaptation capacity, vulnerability may reduce, reaching
even the robust category in certain micro regions.
The weak adaptation capacity stems from the generally low standard of information of
adaptive agriculture, lack of irrigation traditions and weak access to irrigation facilities.
The lower than average school qualifications, health and income positions of the popu-
lation only contribute to that overall picture. It is clear therefore that in order to improve
adaptation capacity, farmers should be taught about adaptive agriculture through spe-
cialised training activity and they should also have access to effective irrigation solutions.
References
Csáki P. 2013: A klimatikus jellemzők párolgásra gyakorolt hatásai a felszínborítás függvényé-ben Zala megye példáján. Diplomamunka (Impacts of Climatic Features on Evaporation Depending on Surface Cover Based on the Example of Zala County, Thesis]. University of West Hungary, Faculty of Forest Engineering, Sopron.
Gálos, Borbála (2014):A9. Klímaszcenáriók időszakai, kiválasztott klímamodellekkel (Climate Scenario Periods with Selected Climate Models]. Manuscript.
Láng, I. – Csete, L. – Jolánkai, M. (ed., 2007): A globális klímaváltozás: hazai hatások és válaszok: a VAHAVA jelentés ( Global Climate Change: Hungarian Impacts and Responses; VAHAVA Report]. Szaktudás Kiadó Ház, Budapest, ISBN 978-963-9736-17-7
Obádovics Csilla, Kulcsár László (2003) A vidéki népesség humánindexének alakulása Magyarországon Területi Statisztika (Human Index of Rural Population in Hungary, Territorial Statistics] 43 4.
Pappné Vancsó J. 2014: Éghajlatváltozás és emberi alkalmazkodás a középkori meleg időszak-ban (Climate Change and Human Adaptation in the Hot Period of Middle Ages]. Földrajzi Közlemények (Geographic Publications] 138. 2. pp. 107-121.
57
Pálvölgyi, T. – Czira, T. – Dobozi, E. – Rideg, A. – Schneller, K. (2010): A kistérségi szintű égha-jlat-változási sérülékenység vizsgálat módszere és eredményei (Micro Region Level Climate Change Vulnerability Analysis. Method and Results.]. Climate-21 Leaflets, 2010. No. 62. pp. 88-103.
Pálvölgyi T. – Czira T. 2011: Éghajlati sérülékenység a kistérségek szintjén (Climate Vulnerability in Micro Regions]. In: Tamás P.-Bulla M. (ed.): Sebezhetőség és adaptáció - A reziliencia esélyei (Vulnerability and adaptability - Chances of Resilience]. MTA Research Institute of Sociology, 2011. pp. 237-253.
Pálvölgyi T. – Czira T. – Bartholy J. – Pongrácz R. 2011: Éghajlati sérülékenység a hazai kistérségek szintjén (Climate Vulnerability in Hungarian Micro Regions]. In: Bartholy J. – Bozó L. – Haszpra L. (ed.): Klímaváltozás – 2011. Klímaszcenáriók a Kárpát-medence térségére (Climate Change - 2011. Climate Scenarios for the Carpathian Basin]. MTA and ELTE Department of Meteorology, Budapest. pp. 235-257.
Pongrácz R, Bartholy J, Gelybó Gy, Szabó P. 2009: Detected and Expected Trends of Extreme Climate Indices for the Carpathian Basin. Bioclimatology and Natural Hazards pp. 15-28.
Sábitz J. - Pongrácz R. - Bartholy J 2013: Az aszályhajlam várható változása a Kárpát-medence térségében (Expected Variation of Inclination to Droughts in the Carpathian Basin]. In: Pajtókné Tari Ilona, Tóth Antal (ed.): Változó Föld, változó társadalom, változó ismeretszerzés, 2013: a megújuló erőforrások szerepe a regionális fejlesztésben: nemzetközi tudományos konferencia (Changing World, Changing Society, Changing Learning, 2013: Role of Renewable Energy Sources in Regional Development: international scientific conference]. 242 p. Venue and date of the conference: Eger, Hungary, 10.10.2013.-12.10.2013. Eger: EKF Department of Geography; Agria-Innoregion Knowledge Centre; Agria Geography Public Benefit Foundation, 2013. pp. 82-86.
Internet references:
National Climate Change Strategy 2008-2025.source: http://www.kvvm.hu/cimg/documents/nes080214.pdfDownloaded: 23 July 2014
National Drought Strategy - discussion paperhttp://2010-2014.kormany.hu/download/7/0a/90000/Aszalystrategia.pdfDownloaded: 15 July 2014
58
Second National Climate Change Strategy 2014-2025 with an outlook to 2050. Discussion paper. September 2013. http://www.kormany.hu/download/7/ac/01000/M%C3%A1sodik%20Nemzeti%20%C3%89ghajlatv%C3%A1ltoz%C3%A1si%20Strat%C3%A9gia%202014-2025%20kitekint%C3%A9ssel%202050-re%20-%20szakpolitikai%20vitaanyag.pdfDownloaded: 14 February 2014
Status analysis of Zala county regional development concept and programmehttp://www.zalamegye.hu/koncepcio/1Helyzetelemzes.pdfDownloaded: 02.01.2013
Data source: CARPATCLIM, ENSEMBLES EU– FP6, CSO, MVH NYUDUVIZIG, TEIR
Laws, decrees:
Act LVII of 1995. on water management (Vgt.) Ar cle 28(1)Decree of the Minister of Environmental Protec on, Communica ons and Water Management No. 18/1996. (VI. 13.) KHVM
59
Vulnerability of Society to Climate Change: Review of Vulnerability to Flash Floods in Zala Micro Regions
Judit Vancsó Ms Papp, Mónika Hoschek, Csilla Obádovics
ABSTRACT: In this study we present an analysis of vulnerability of the population to flash floods in the context of exposure, sensitivity and adaptation. By focusing on comparability and regional differences, we made our calculations at the level of micro regions.
As participants of the TÁMOP-4.2.2.A-11/1/KONV-2012-0013 “Agroclimate”8 project,
our responsibility was to assess the potential social impacts of the projected climate
change in Zala county by using the previously applied vulnerability analyses. On the
basis of the results of our questionnaire-based survey conducted in the county to assess
the impacts on the climate change on agricultural society, and the indicative national
documents describing the estimated impacts of the projected climate change (Láng
I. – Csete L. – Jolánkai M. 2007; Nemzeti Éghajlatváltozási Stratégia [National Climate
Change Strategy]: NCCS; National Disaster Risk Assessment and the planned second
NCCS) and publications (see e.g., Pongrácz et al 2009; Czigány Sz. et al 2010); we think
that, apart from the drought previously discussed in detail, rural society will have to face
more frequent conditions with potential floods, groundwater and flash floods in future
due to the uneven distribution of precipitation. This study is dedicated to the increasing
problems of flash floods, affecting society.
Floods, inland inundation and flash floods as threats
In relation to the threats of floods and inland inundation we found hardly (Vári A. –
Ferencz Z. 2011) any literature, while in the case of vulnerability to flash floods we did
not find any study at all in the Hungarian literature. On the basis of the documents
and publications referred in the introduction in terms of precipitation projections in-
volve a great deal more uncertainty than in the case of temperature or droughts, yet we
can say that in the long term we can expect more extreme weather with precipitation
8 “Agroclimate: Impact Analysis of the Projected Climate Change and Possible Adaptation in the
Forestry and Agriculture Sector”
60
leading to floods and inland inundation. This opinion is also supported with the ques-
tionnaire-based survey described in the previous study, because the majority of the
respondents (72% of the total respondents) indicated changes in the annual precipitation
distribution: typically there is no precipitation in summer or rainfall occurs suddenly,
often in the form of torrential rain.
While flood and inland inundation are well known expressions, the concept of a flash
flood is relatively new in the science. Nevertheless, researchers of the University of Pécs
already conducted numerous studies primarily in the settlements of Transdanubia (see
e.g., Czigány Sz. et al 2010; Czigány, S. et al 2011; Pirkhoffer E. et al 2009). Compared to
river floods, a flash flood generally occurs on sloppy hilly areas, mostly in relation to
small water flows, when a large amount of rain falls. It usually occurs intensively, between
half an hour and six hours. A flash flood is not associated with any season, it may occur
at any time from early spring to late autumn, and may cause significant damage to built
environment and in the agricultural sector according to experiences to date.
According to the maps contained in the National Disaster Risk Assessment docu-
ment, in terms of floods, Zala county is a low risk area, (with the exception of the high
risk region surrounded by Letenye-Murakeresztúr and Nagykanizsa), with a low risk
of inland inundation, but it is extremely at risk of flash floods. As exposure to flash
floods is well documented according to the flash flood risk map, the problem should
also be reviewed in the context of impacts - adaptation - vulnerability, as illustrated in
the method presented in the first half of the study.
Exposure and sensitivity
To define vulnerability to flash floods we relied on the methodology applied in the
previous vulnerability studies described in the first half of this publication (Pálvölgyi et
al 2010, Pálvölgyi T. – Czira T. 2011; Pálvölgyi et al 2011; NCCS 2). Our indicators were
calculated according to the parameters presented in Table 1. The exposure was calculated
with more than 30 mm daily precipitation, considering that insurance companies also
used that threshold and compensate for flood damage when it is exceeded and provid-
ing that it is also certified by OMSZ (National Meteorology Service) (Czigány Sz. et al
2010). As flash floods develop in a very short time, measured in hours and not in days,
the 30 mm/day may be taken into account only with a compromise, considering that if
precipitation is distributed evenly across a day, the water flow will not necessarily lead to
inundation. However, there are very limited options to predict precipitation with hourly
accuracy. However, according to climate models for the future, it may be projected for
a longer term (e.g., 30-year periods) how many times the daily total precipitation will
be over 30 mm each year during the particular period. Although this cannot be used to
project the probability of precipitation potentially leading to flash floods in future, but
the tendency of occurrence of a relatively large amount of precipitation falling within
one day may be concluded (increase, decrease of stagnation).
61
The values in our calculations stemmed from the European Union CARPATCLIM
project for the past and another EU project ENSEMBLES EU-FP6 for the future. Based on
that it may be concluded that in Zala county the volume of daily precipitation exceeded
30 mm on 50 occasions between 1981 and 2010, which between 2011 and 2040 is likely
to go up to 55 days (10% increase), for the period of 2041-2070 to 60 days (20% increase),
and for the period of 2071-2100 to 65 days which, compared to the reference period, will
result in 30% increase by the end of the period. On the basis of the potential increase in
the occurrence of a large amount of precipitation falling within a relatively short time,
therefore flash floods are likely to occur more frequently in the examined period.
Table 1. Vulnerability to Flash Floods among the
agricultural population in Zala (1980-2100)
Impact Adaptation
Exposure Sensitivity
probability >30 mm/day precipitation (day/year)
surface cover: forests, bare surface ratio carbonate rocks close to the surface ground thickness, physical features of soils slope parameters: average steepness of the slope on the water
catchment area slope range valley density water system parameters: confluence points, water flow density, water
network
availability or lack of plans to eliminate water damages
conformity of the plan for elim-inating water damage to regu-lations
infrastructure relating to water damage elimination (e.g., built rain reservoirs)
Source: CARPATCLIM; ENSEMBLES EU– FP6; NYUDUVIZIG; National Disaster Risk Assessment
in Hungary 2011. Based on the data disclosed by Szabolcs Czigány, edited by the author
To define sensitivity, we used the values of the flash floods risk map, broken down by
settlement.9. In defining the risk levels the first step of the research was to identify the
mountains and hilly areas, as well as water catchment areas in Hungary, to which the
authors always assigned an exit point, i.e. a settlement, or a part of a settlement (Czigány
et al 2010). The authors agree that a water catchment area arbitrarily defined for a settle-
ment is not a natural demarcation, but the truly serious damages are always associated
with a particular settlement. Within the marked areas, the authors classified the defined
areas into risk categories according to the passive factors indicated in Table 1. In the flash
flood risk map, included in the National Disaster Risk Assessment document, referred
to above, the authors defined four categories (high, average and low risk, and no risk).
9 The high resolution maps for Zala county and the related information were provided by Szabolcs
Czigány, a researcher of the University of Pécs, involved also in flash floods.
62
The entire Zala county falls in the average and high risk category, while the figures of
the micro regions are illustrated in Table 2.
Table 2. Overall estimated impacts based on the probability of
occurrence of >30 mm/day precipitation and the risk category
of flash floods in the Zala micro regions 1981-2100
micro region variation in the frequency of >30
mm/day precipita-tion (day/period)
flash flood risk category
Probability of occurrence
impact
Hévíz moderate increase high negligible moderately strong
Keszthely moderate increase average negligible moderate
Lenti moderate increase average negligible moderate
Letenye moderate increase high negligible moderately strong
Nagykanizsa moderate increase average negligible moderate
Pacsa moderate increase high negligible moderately strong
Zalaegerszeg moderate increase average negligible moderate
Zalakaros moderate increase average negligible moderate
Zalaszentgrót moderate increase high negligible moderately strong
Source: CARPATCLIM; ENSEMBLES EU– FP6; based on the data
disclosed by Szabolcs Czigány, edited by the author
Despite the relatively high risk, one can still not talk about strong impacts especially
by taking into account the extremely low probability of the occurrence of the event. It
was already indicated above that during the 30 years of the reference period, precipita-
tion exceeding 30 mm a day occurred on average on 50 occasions in Zala. According to
the data of the meteorology stations of 15 large towns (average of 1985-2013; CSO), we
can conclude that as a national average, there are 128.5 days with precipitation in a year.
Consequently, there were only 55 days among the 3,855 days with precipitation of the
thirty years when the volume of precipitation exceeded the threshold referred to above. In
a year on average precipitation more than 30 mm occurred on 1.8 days within the county.
There is therefore very little probability that more than 30 mm precipitation would fall
on the water catchment area of a particular settlement in just a few hours. Another factor
confirming the above idea is that over 25 years (between 1980 and 2005 based on the
reports from 41 settlements) on average 1.2 flood claims were submitted in relation to
the county10, and in fact not all claims stemmed from flash floods. If we accept the 1.2
10 Based on the data disclosed by Szabolcs Czigány (PTE).
63
cases/25 years, and assume that it will change according to an increase in the exposure
(30% increase by the end of the period), then number of cases may rise to 1.32 for the
first phase, 1.44 for the second phase and 1.6 for the last phase of the reviewed period by
settlement, i.e. the probability of occurrence of the event is still negligible.
If probability was not taken into account during the establishment of the strength
of the impacts, strong or at least moderately strong impacts could occur according to
exposure and sensitivity. As the probability of occurrence of the evens is negligible, the
strength of the impacts was deemed weak, classifying them into moderately strong and
moderate categories.
Adaptation capacity calculation
To define the adaptation capacity we tried to find out what the individual settlements
did to prevent potential damages. Generally water management directorates inform the
municipalities of settlement at risk of the need to prepare flood prevention plans (based
on 11 information from NYUDUVIZIG). Although the guide containing the local flood
prevention tasks of the settlement (Szunyog, Zalányi 1998) does not specifically refer to
flash floods, which is understandable because it is a relatively new concept, it defines
exactly the phenomenon which is identical with a flash flood. The document describes
in detail the task of the local management while preparing the plans, and the tasks to be
performed when the events occur. Having studied the guide, it may be concluded that
if a settlement prepares its flood prevention plan according to the requirements, it will
most likely be able to protect itself against flash floods. The settlements were classified as
to whether or not they had a flood prevention plan and whether the plans complied with
the guide, whether infrastructure projects were completed or plans are in place that can
mitigate the impact12 (e.g., rainwater reservoirs). A settlement scored 0% if they did not
have any flood prevention plan or related infrastructure project. As each Zala settlement
is at least moderately at risk according to the flash flood risk map, the need for protection
was considered fundamental for each settlement. If a particular settlement had a flood
prevention plant, but it did not comply with the requirements, the adaptation capacity
was deemed 50%. In such cases the settlement is fully aware of the threat and also took
steps to eliminate it, but the plan does not fully comply with the requirements. When we
did not find any plan for flood prevention but found planned or existing infrastructure
projects, the adaptation capacity was also considered 50%. For a settlement possessing
a flood prevention plan complying with the requirements, the adaptation capacity was
deemed 90%, if the settlement has already been hit by a flood at least once. In that case
it may be assumed that, based on the experience, the municipality revised and updated
its plans for defence. The adaptation capacity was deemed 80%, when the settlement had
11 West Transdanubia Water Management Directorate
12 The respective database was provided to us by NYUDUVIZIG.
64
flood prevention plans that satisfied the requirements, but had not yet been hit by any
flood. The ideal situation, when it is certain that a particular settlement will be able to
protect itself against the consequences of a potential flash flood was considered 100%.
As it is unlikely that the volume of water generated by a flash flood or the direction of
the flow can be perfectly modeled, in our opinion 100% adaptation capacity cannot be
assumed in the case of any settlement, even if the particular settlement already expe-
rienced a flood, because the next flash flood may involve twice or multiple times the
volume of the water experienced in the previous flood.
The micro regional adaptation capacity was calculated as the average of the respective
figures of the settlements of the particular micro region (Table 3). The relatively weak
adaptability of the micro regions stems from the fact that only 74 of the nearby 260 set-
tlements have any version of adaptation and 80% or 90%, indicating good adaptation,
was found only in 25 settlements. Generally, 80% of the settlements already experienced
a flood have some adaptation capacity. As the concept of a flash flood and the map indi-
cating the risk category are new, settlements often do not fully understand whether they,
or any part of them is/are situated on a potential flood plain because generations may
pass before a small water flow crossing the settlement would cause problems. Perhaps
that is the explanation for the relative unpreparedness
Table 3. Adaptability to flash floods in the micro regions of Zala micro region
Micro region adaptation capacity (%)
Hévíz 10.0
Keszthely 21.3
Lenti 27.4
Letenye 18.5
Nagykanizsa 12.6
Pacsa 7.5
Zalaegerszeg 11.9
Zalakaros 13.3
Zalaszentgrót 17.9
Source: Based on NYUDUVIZIG data, edited by the author
Defining the degree of vulnerability
The degree of vulnerability was established in the manner already described in the
second part of the series of the studies, as a relationship between the degree of impacts
and the status of adaptation. Following the system of former vulnerability reviews, a
65
distinction is made among robust, endangered, fragile and vulnerable systems. As in
our interpretation, the adaptation capacity is weak or moderate in each micro region,
the robust and fragile category cannot be used definitely. There is no great difference
between micro regions in terms of adaptation or impacts, generally moderate or aver-
age impacts reach relatively weakly adapting communities, vulnerability therefore is
no more than average and there is only a risk in the case of a moderate impact (Table
4). However, weak adaptation could be remedied fast, if the individual micro regions
recognised their vulnerability. Perhaps these ideas are confirmed by the fact that within
the micro regions, those settlements adapt well which are forced to protect themselves
against sudden floods at least once. By increasing the adaptation capacity, vulnerability
could be reduced significantly in the case of flash floods.
Table 4: Vulnerability of society to flash floods in Zala micro regions
Micro region impact (1980-2100) adaptation capac-ity (at present)
Source: CARPATCLIM; ENSEMBLES EU– FP6; NYUDUVIZIG; based on
the data disclosed by Szabolcs Czigány, edited by the author
Summary
On the basis of the results of our study assessing the vulnerability of the population of
Zala county to flash floods in the context of impact adaptability, we can conclude that
vulnerability is moderate or, as an overall result of moderately strong effects and weak
and moderate adaptation capacity, average, or it reaches only the degree of a risk in
certain micro regions. By increasing the adaptation capacity, vulnerability may reduce,
reaching even the robust category in certain micro regions.
Weak adaptability stems from the lack of recognizing the problem. Typically those
settlements adapt properly, which had to protect themselves against a flood at least
66
once. Hopefully, increasing research in the topic and the presentation of the problem to
responsible organizations can significantly increase the number of settlements which
can protect themselves properly against flash floods.
References
Czigány Sz. – Pirkhoffer E. – Balassa B. – Bugya T. –Bötkös P. – Gyenise P. – Nagyváradi L. – Lóczi D. – Geresdi I. 2010. Villámárvíz, mint természeti veszélyforrás a Dél-Dunántúlon [Flash Flood as a Natural Risk in South Transdanubia]. Geographic Publications 134. 3 pp. 281-298.
Czigány, S – Pirkhoffer, E – Nagyváradi, L – Hegedűs, P – Geresdi, I 2011: Rapid screening of flash flood-affected watersheds in Hungary, Zeitschrift für Geomorphologie 55: (1) pp. 1-13.
Láng, I. – Csete, L. – Jolánkai, M. (ed., 2007): A globális klímaváltozás: hazai hatások és válaszok: a VAHAVA jelentés [Global Climate Change: Hungarian Impacts and Responses; VAHAVA Report]. Szaktudás Kiadó Ház, Budapest, ISBN 978-963-9736-17-7
Pirkhoffer E.–Czigány SZ.–Geresdi I. 2009a: Impact of rainfall pattern on the occurrence of flash floods in Hungary. – Zeitschrift für Geomorphologie 53. pp. 139–157.
Pálvölgyi, T. – Czira, T. – Dobozi, E. – Rideg, A. – Schneller, K. (2010): A kistérségi szintű égha-jlat-változási sérülékenység vizsgálat módszere és eredményei [Micro Region Level Climate Change Vulnerability Analysis. Method and Results.]. Climate-21 Leaflets, 2010. No. 62. pp. 88-103.
Pálvölgyi T. – Czira T. 2011: Éghajlati sérülékenység a kistérségek szintjén [Climate Vulnerability in Micro Regions]. In: Tamás P.-Bulla M. (ed.): Sebezhetőség és adaptáció - A reziliencia esélyei [Vulnerability and adaptability - Chances of Resilience]. MTA Research Institute of Sociology, 2011. pp. 237-253.
Pálvölgyi T. – Czira T. – Bartholy J. – Pongrácz R. 2011: Éghajlati sérülékenység a hazai kistérségek szintjén [Climate Vulnerability in Hungarian Micro Regions]. In: Bartholy J. – Bozó L. – Haszpra L. (ed.): Klímaváltozás – 2011. Klímaszcenáriók a Kárpát-medence térségére [Climate Change - 2011. Climate Scenarios for the Carpathian Basin]. MTA and ELTE Department of Meteorology, Budapest. pp. 235-257.
Pongrácz R, Bartholy J, Gelybó Gy, Szabó P. 2009: Detected and Expected Trends of Extreme Climate Indices for the Carpathian Basin. Bioclimatology and Natural Hazards pp. 15-28.
67
Szunyog Z. – Zalányi T. 1998: Települések helyi vízkárelhárítási feladatai. Útmutató [Local Flood Prevention Tasks of Settlements. Guide]. General Directorate of Water Management, Budapest.
Vári A. - Ferencz Z. 2011. Az árvízi sebezhetőség társadalmi indikátorai: esettanul-
mányok két Felső-Tisza-vidéki területen [Social Indicators of Vulnerability to Floods:
Case Studies in Two Areas in the Upper Tisza Region] In: Tamás P.-Bulla M. (ed.):
Sebezhetőség és adaptáció - A reziliencia esélyei [Vulnerability and adaptability - Chances
of Resilience]. MTA Research Institute of Sociology, 2011.
Internet references:
National Climate Change Strategy 2008-2025.http://www.kvvm.hu/cimg/documents/nes080214.pdfDownloaded: 23 July 2014
National Disaster Risk Assessment, Hungary 2011. http://vmkatig.hu/KEK.pdfDownloaded: 13 August 2014
Second National Climate Change Strategy 2014-2025 with an outlook to 2050. Discussion paper. September 2013. http://www.kormany.hu/download/7/ac/01000/M%C3%A1sodik%20Nemzeti%20%C3%89ghajlatv%C3%A1ltoz%C3%A1si%20Strat%C3%A9gia%202014-2025%20kitekint%C3%A9ssel%202050-re%20-%20szakpolitikai%20vitaanyag.pdfDownloaded: 14 February 2014
Data source:
Szabolcs Czigány’s data supplies 2014.
CARPATCLIM
ENSEMBLES EU– FP6
NYUDUVIZIG
68
Climate Change Perception and Responses to the Challenges Among Agricultural Producers: Results of the Questionnaire-based Survey
László Kulcsár
ABSTRACT: In relation to climate change, the questionnaire-based survey conducted among agricul-tural producer families focused on two issues: the opinion about climate change and the views on protection against the impacts of climate change. Both issues were tested with answers given to a series of statements, which were then processed with multi-variable methods. The results showed the different types of climate change perception and the characteristics of the responses to challenges.
KEYWORDS: opinion about climate change, adaptation, perception, mitigation
Introduction
The social-economic processes are reflected in the conduct and behaviour of individuals
and group, who are influenced by the culture that dominates their social and natural
environment. The behaviors which are reactions to climate effects as well as attitudes
and opinions are the results of a cognitive filter which are practically coded in the social
status of an individual or family. It should be stressed because any sectoral policy decision
can gain legitimacy and exert a sufficient effect if they take into account the economic
and cultural position of the particular social groups.
Our questionnaire-based survey conducted among agricultural producers13 covered the
perception of climate change i.e., whether or not the respondent families felt those impacts
and to what extent they apply them to their own environment. The survey also tried to
identify whether the perception of risks, potential responses to risk mitigation and the ex-
pression of the importance of risk are related to the people’s social and demographic features.
Literature
The opinions on climate risks and related attitudes are frequently covered by the litera-
ture. A Swedish study (Sundblad, Garling 2007) e.g., analyzed the impact of numerous
demographic factors (gender, age, place of residence, qualification) in relation to the un-
derstanding of climatic change and perception risks. Information and knowledge-based
13 The questionnaire-based survey was conducted on 217 Zala county families in the autumn of
2013. We wish to take the opportunity here to thank for the contribution of the students of the University
of West Hungary Faculty of Economics and tutors Dr. Ferenc Jankó, Dr. Csilla Obádovics, Dr. Judit Vancsó
Mrs. Papp.
69
cognitive risk perception (the probability they attribute to the occurrence of the risk) in
fact had positive correlation only with qualifications, while affective risk perception fo-
cusing on emotions (fear) was related to gender, as women generally judged any problem
caused by climate change on emotional basis. The perception of climatic changes derives
from cultural background factors according to Kahan (2011) as well, who emphasizes
cultural polarization, according to which social groups with more decisive rationality
are likely to accept or ignore the evidence of climate change if they confirm/contradict
their existing beliefs.
The central position in the mitigation of the risks of climatic effects of the importance
of traditional knowledge in numerous studies also confirms the importance of the cul-
tural context. This happens despite the fact that traditional knowledge shows a declining
tendency in a global context. Gómez-Baggethun, Corbera and Reyes-García (2013) wrote
about the increasing role of traditional knowledge in local communities, and also suggest-
ed that we were witnessing a “hybridization” process, where traditional knowledge can
make the modernization process more flexible. In addition, traditional knowledge has a
localization feature and is closely related to agriculture or, in a slightly wider context, to
the rural economy. In 2004, the “Ecology and Society” journal dedicated a separate issue
to the correlation between traditional knowledge and climate change, emphasizing the role
of traditional knowledge in the mitigation of the negative effects of climatic changes. In
the introduction Folke (2004) also referred to problems and positive facts resulting from
the complexity of traditional knowledge, local institutions and various cultural effects.
Leclerc et al (2013) and their co-authors stress that traditional knowledge is applied purely
only in few cases and that it is rather mixed with “scientific” knowledge, which makes the
mitigation of risks more difficult in numerous cases. Boillat and Berkes (2013), as well as
Ruiz-Mallen and Corbera (2013) all stress the importance of traditional knowledge in the
perception and interpretation of climatic changes. In terms of the Hungarian traditions
it is clear that there is no uniform opinion about the role or applicability of traditional
knowledge. The transformation of the institutions of the agriculture and a new economic
structure created significantly different situations for producers and may also have triggered
different opinions and reactions in relation to climate effects.
In our questionnaire-based survey, we asked the respondents to evaluate eleven
statements, which focused on the perception of climate effects and various dimensions
of perception. Our other topic summarizes the opinions about adaptation, where we
processed the responses to seven statements. The applied Likert scales had five grades,
where the higher grade indicated a higher level of agreement.
Results
Let us take a look at perception first. The eleven statements offered to Zala farmers
represented different dimensions of climatic effects. The statements were classified with a
factor analysis, and the structure was illustrated in the following table. The reviewed aspects
70
were as follows: (1) climate change and agriculture and the relevance of that relationship,
(2) distancing climate change from local conditions, (3) causes of climate change not attrib-
utable to people, which may also suggest a kind of fatalism, because it detaches the causes
from people, (4) unreliability of mass communication concerning climate change and (5)
skepticism about the performance of science in terms of climate change.
The respondent agricultural producers think that the impact of climate change is
already present and important, although they are skeptical about scientific projections.
It is a very important result that people do not think that they are not affected by climate
change or that it was only a remote thing. They think so despite the fact that mass com-
munication strengthens that distance by presenting far away disasters and problems of
remote places. The prestige of television is very small in the area.
Table 1. Perception of climate change among the agricultural
producers of Zala county. Result of the factor analysis.
Significance of items in terms of demographic features
Factor/statement Average(1-5)
Deviation Sign.0.05
F1. The impact on climate change on agriculture is significant and relevant (explained variance: 29.491%) Climate change will alter production and product structure
in agriculture Climate change will cause a great deal of problem to farmers Confidence that climate change has started
4.28
4.354.34
1.028
0.981.051
ns
F2. Climate change is a remote phenomenon, we are not affected by it (explained variance 14.899%) Climate change affects only faraway places but not us I do not think that climate change would be a real problem Climate change is so far away in time and it will happen so far
ahead in the future that it is not worth talking about it
1.561.74
1.90
1.0351.217
1.097
ns
F3. Climate change is not the result of a human effect (explained variance 12.277%) The climate change experienced these days is the result of
natural causes and is not caused by people Climate change is cause more by human activity and not by
nature
2.56
(-)3.60
1.230
1.146
low qualification
F4. Lack of trust in mass communication (explained variance 9.167%) TV creates too much ado about climate change TV often spreads rumors about climate change
2.952.99
1.3931.525
ns
F5. Science skepticism (explained variance 7.408%) If scientists cannot forecast weather for the next week, how
would they would be able to predict what will happen in the next 50-100 years? 3.83 1.347
low qualification
women
71
Among the demographic features, the effect of qualifications and gender turned out to
be significant. Women respondents with lower qualifications were more skeptical about
the role of science and accepted in a higher number the opinion that climate change was
not caused by human activity.
Consequently, climate change was significant and relevant for the majority of agricul-
tural families of Zala county who felt that it was close to them. Perception is also significant
and that is the reason why it is important to analyze their adaptation skills and ideals and
how they envisage reducing the negative consequences of climate change in agriculture.
Table 2. Adaptation ideas about climate change among
the agricultural producers of Zala county.
Result of the factor analysis. Significance of items with demographic features
Factor/statement Average(1-5)
Deviation Sig.0.05
F1. Positive adaptation skill (explained variance: 25.042%) Climate change is unavoidable in agriculture and we must
learn to adapt to it I would be willing to change the established farming meth-
ods, if required by climate change I do not think that climate change will reach a degree that
I would have to change my farming habits
4.22
3.55
(-) 2.23
1.084
1.417
1.273
ns
F2. Failed adaptation due to lack of funding (explained var-iance 19.151%) Adaptation to climate change is very costly, not everyone
can afford it 3.84 1.204
low quali-ficationwomen
F3. Failed adaptation due to adaptation skill (explained var-iance 15.632%) Most people find it difficult to change their habits I do not think that climate change will reach a degree that
I would have to change my farming habits
4.01(-)2.23
1.1231.273 ns
F4. Traditional agricultural knowledge (explained variance 12.775%) If more people had the knowledge of peasants, agriculture
would find it easier to cope with the challenges of climate change
Even the farmers’ skills could not help in the adaptation to climate change because weather is becoming more and more extreme
3.35
(-) 3.31
1.394
1.372
women
The strongest factor14was clearly the positive adaptation skill, according to the results of
the analysis, but it is also clear that the adaptation skill, inadequate due to various reasons
14 The strength of the factor is indicated by the size of the explained variance in this case too
72
had overall stronger explained variance which definitely suggests adaptation problems
among agricultural farming families. Traditional agricultural knowledge as an adaptation
opportunity is similarly important, as seen in the literature, yet very thorough support is
required for sufficient implementation. The data also indicate that demographic features
were not important. Therefore cultural effects and personality features may come to the
forefront as factor affecting people’s opinion. The role of school qualifications matches the
findings of the Swedish study referred to above in this aspect too.
We then further analyzed the impact of school qualifications in relation to the ap-
proaches represented by the various factors. We applied discriminate analysis, which
shows how far the various dimensions (functions) representing the individual factors
place the different qualification groups from each other. The further away those groups
are from each other in a system of coordinates, the stronger is the influence of qualifi-
cations on the level of perception and adaptation views. Accordingly, among the various
demographic factors, we only looked at the impact of school qualifications with that
method, because the other demographic and social factors did not show enough influ-
ence on the attitude and understanding of agricultural producers about climate change.
Table 3: Result of a canonical discriminate analysis: perception of
climate change in groups with various school qualifications
School qualification
Dimensions (functions)
The climate change is a remote phenomenon, the scientific results are not convincing.
Human activity is involved in cli-mate change and there is no trust in
mass communication (television)
Primary school or less 0.573 -0.145
Secondary school 0.066 0.101
College, university -0.406 -0.372
Table 4: Result of a canonical discriminate analysis: adaptation options
to the consequences of climate change by school qualifications
School qualification
Dimensions (functions)
High adaptation skill, successful adaptation
Failed adaptation, bad adaptation skill
Primary school or less -0.561 -0.051
Secondary school 0.057 0.032
College, university 0.442 -0.110
With higher school qualifications the ratio of those who feel that climatic changes are
close to them and who positively acknowledge scientific results is likely to increase. They
73
are the ones who are the least critical about mass communication (television), although
the degree of trust is little in that group too. The group of agricultural producers with
low school qualifications generally feel that they are far away from climate change and
its problems and they do not consider scientific prediction results convincing either.
In terms of adaptation skill, agricultural producers with low school qualifications put
themselves into a more disadvantaged group. In the group who completed secondary
schools, their qualifications were not enough for being optimistic. Only those with the
highest school qualifications were optimistic about the possibility of successful adapta-
tion in their own situation.
Summary
The results also confirm the conclusion that the perception of climate change and the
adaptation categories are present differently among the agricultural population which
supports a need for similarly differentiated decisions concerning state and local inter-
ventions by taking into account social and cultural disparities.
References
Boillat, S., and F. Berkes. (2013). Perception and interpretation of climate change among Quechua
farmers of Bolivia: Indigenous knowledge as a resource for adaptive capacity. Ecology and
Society 18 (4)
Gómez-Baggethun, E., E. Corbera, and V. Reyes-García. (2013). Traditional ecological knowledge
and global environmental change: research findings and policy implications. Ecology and
Society. 18 (4): 72.
Kahan, Dan M. (2011): The Tragedy of the Risk-Perception Commons: Culture Conflict,
Rationality Conflict, and Climate Change. Cultural Cognition Project Working Paper No.
89 Yale University
Leclerc, Christian, Caroline Mwongera, Pierre Camberlin, Joseph Boyard-Micheau (2013):
Indigenous Past Climate Knowledge as Cultural Built-in Object and Its Accuracy. Ecology
and Society 18 (4)
Ruiz-Mallén, I. and E. Corbera. (2013): Community-based conservation and traditional ecological
knowledge: implications for social-ecological resilience. Ecology and Society 18 (4)
Sundblad, Eva-Lotta, Anders Biel, Tommy Garling (2007): Cognitive and affective risk judge-
ments related to climate change. Journal of Environmental Psychology 27 97–106.
74
Theory and Methodology Issues of Measuring Environmental Risks
Csaba Székely - Csilla Obádovics
ABSTRACT : The uncertainty factor has an important place in the concept of environmental risks. In risk assessment, time horizon is an important factor. Consequently, the study is dedicated to the risk management methods, a matrix type approach of risk assessment and the steps of volatility calculation.
Application of the Volatility Method for the Analysis of Changes in Climate Risks
Mónika Hoschek, Csilla Obádovics, Csaba Székely
ABSTRACT The assumption, according to which climate change also has an impact on social and economic processes can only be studied over a long period. The climate change, global warming and more extreme weather conditions cannot be identified from one year to another. The increase in the annual average temperature can clearly be detected from the data, available for the last 113 years (1901-2013). However, the appearance and more frequent extreme weather conditions, i.e. increasing fluctuation of daily temperature, number of heat wave days, number of droughts, etc., cannot be clearly proven. We conducted a volatility analysis of the climate factors to confirm this extremism or to reject the assumption. The weather forecast models show that climate volatility is increasing as more and more extreme weather conditions occur, which will entail severe consequences in agricultural countries and in areas in Hungary where economy is based on agriculture. The volatility tests were run on annual average temperature, daily minimum and maximum temperatures and the fluctuation data series. The average annual temperature began to rise significantly in the second half of the last century. The fluctuation of daily temperature, i.e. the difference between the daily maximum and minimum temperatures, is also clearly rising, although to a lesser extent.
KEYWORDS: annual average temperature, daily temperature fluctuation, frosty day, harsh days, heat wave day, hot day, volatility
Introduction
Volatility analysis as a method has been used by researchers mainly to analyse eco-
nomic processes, primarily stock exchange processes. The stock exchange processes
vary according to time series and are influenced by accidental factors. (Farkas, 2010.,
Hull J. and A. White, 1987.) The change in climate volatility has a significant impact on
the poverty of countries engaged in agricultural production too (Syud Amer Achmed
et al 2010). Climate change has a huge impact of the prices of agricultural products,
thus affecting the financial position of the population living in primarily rural and
agricultural areas.
The purpose of our study is to illustrate that the volatility analysis as a method may be
applied not only to analyze economic data series, but also to other data series, reflecting
volatility in time and affected by accidental factors. By relying on the similar features of
natural and stock exchange processes, we applied the volatility analysis to the analysis of
weather conditions. ‘Capricious, like the weather’. This saying also shows why we decided
to apply the volatility calculation method to weather data. In the course of our analyses
we calculated and analyzed historic volatility data.
86
Extension of the volatility analysis to natural phenomena
In economic sense volatility is the indicator of the risk of an investment. Volatility refers
to the variability and fluctuation of estimated or historic yields which, translated into
weather conditions, reflect the variability and fluctuation of forecast of historic e.g., daily
average temperature. In fact, when we want to know whether our weather has become
more capricious, what we would like to know is if its volatility is increasing. If e.g., the
daily average temperature increases or decreases continuously by 1-2 degrees from one
day to another, its volatility is not significant (irrespective of the direction of the change).
However, when the daily average temperature is increasing at one time and falling at
another time, it has greater volatility. That means that we can talk about the volatility
of the weather when it shows sudden changes within as short period as a result of any
unexpected or extraordinary weather condition. The unexpected weather conditions of
that nature and the consequential volatility is an uncertainty and the risk, and therefore
volatility may also be described as an indicator of risk in that sense. (Zsembery, 2003)
Used data and applied methods
The “Agroclimate”15 project focuses on a designated area within the country i.e., Zala
county, as a pilot programme. Zala county is part of the West Transdanubia Region.
The long time series data used for the analysis of the weather and climatic factors were
available at the Regional Centre of the Meteorology Service in Szombathely. So, these
data series were used for our analysis, and our figures were prepared accordingly.
The temperature data series were available from 1 January 1901 to 31 December
2013, and therefore we were able to analyze a sufficiently long time series. Apart from
the daily average temperature, the minimum and maximum temperature data and daily
temperature fluctuation were analyzed for the reviewed 113 years.
In the case of temperature data prior to calculating volatility we had to make one more
conversion, which is not required for stock exchange data. All stock exchange indices are
based on a positive number. The indices only increased from the initial point. This fact
is important because when subsequent logarithmic yield figures must be divided by one
another in order to calculate volatility, it is easily feasibly mathematically. However, 0°C
may also occur among temperature data, which prevents the division. In order to eliminate
the problem, we analyzed the temperature data series of the Kelvin scale instead of using
the scale of Celsius degrees.
As mentioned above, the stock exchange indices only increased from the initial value.
The increase reflected an almost exponential curve. A logarithmic scale had to be applied
to the yield values in the stock exchange volatility calculation because of the nature of
15 “Agroclimate: Impact Analysis of the Projected Climate Change and Possible Adaptation in the
Forestry and Agriculture Sector”
87
the curve. No such conversion is required for the temperature data, forming the subject
of our study, because here there are no differences in volume.
Deciding on the time horizon is a very important step in relation to a long time se-
ries, like the one available for our analysis. The obvious choice for the shortest period is
an annual time horizon. To calculate annual volatility, the spread, calculated from the
quotient of the daily temperature data had to be annualized. In order to do that, we had
to multiply the spread figures, calculated for the daily data by the root of the number of
days, examined during a specific period. That meant that the data had to be multiplied
by x-times. For leap years we used the X multiplication factor.
We performed analyses for 10-year periods with annual volatility. In that case we also
had to pay attention to leap years in the multiplication factors. In decades containing
two leap years, the multiplication factor was, while in other decades containing three
leap years, the factor was.
Volatility of daily average temperatures
The series of annual average temperatures of Szombathely over the analyzed more than
one hundred years showed a great deal of volatility each year. The difference between
the hottest and coldest years is 3.8oC, while the average temperature of the individual
years is on average 0.74oC different from the average 9.5oC.
In the first half of the last century, no change or increasing or decreasing trend can
be observed in the average temperature data series16 (Figure 1.)
Figure 1. Annual average temperatures 1901-2013
16 However, it should be noted that the variation of the measuring points led to inhomogeneous data
series. In the climate analysis, available on the website of the National Meteorology Service (OMSZ) the
data reflected 0.6oC increase following homogenization (Domokos, 2008, Szalai et al 2005) and a straight
line trend adjustment. It is especially important for our research area, because Alpokalja is one of the most
intensively warming up areas of Hungary. (OMSZ, Szalai et al 2005)
88
The coldest year of the analyzed period in Szombathely was 1940 when the annual
average temperature was only 7.4oC. The second coldest year was 1956, when the annual
average temperature was only 0.3oC higher. In the ranking order of cold years the average
temperature of the third subsequent years was higher than 8oC. Only two years of the
last quarter of the century were included among the ten coldest years.
The last year of the century i.e., the year of 2000, turned out to be the hottest, with
11.2oC average temperature. The second hottest year was in that millennium, when the
average temperature in 2008 was only 0.01oC lower than the average recorded for 2000,
followed by 1994 (11.0oC). The hottest 10 years included only three years from the 1900s,
and only one from the first half of the century (1934).
The curve starts to change from the 1960s, as parallel with the global changes, the
time series reflect a clearly warming up trend (Figure 2).
Figure 2. Annual average temperatures 1960-2013.
From the 1960s the increase in the average temperature is clearly obvious also with the
high reliability linear trend line. If the rise continues in a straight line in the future too,
the average temperature will increase by more than 1oC in every thirty years. However,
this tendency may not change in a straight line, in which case the consequences of the
global warming will demand fast adaptation and changes from society.
The annual volatility figures of average temperatures varied between 12.7% and
18.8%, i.e. within a range of 6.1% over the analyzed period. It may be concluded that
the average difference from the average annual volatility figures was only 7.2%, which
reflects very low distribution (Figure 3.).
These days we often hear that weather conditions have become more variable. The fact
that 1929 was the year with the greatest volatility of 18.8% seems to slightly contradict
to that statement. In that year people had to suffer the greatest changes from one day to
another. Looking at further elements of the ranking order, apart from one exception,
there are only years prior to 1950 among the ten years with the greatest volatility. Of
the years of the new millennium 2012 was the most extreme, but it still lies only in 10th
place in the order with its 16.9%.
89
Figure 3. Annual volatility of daily average temperatures 1901-2013.
If we take a look at the figures from the opposite side and look at the least volatile
year, then 1974 strikes out (12.7%). The changes from one day to another were the
smallest in that year. Looking at the ranking order of ten in that respect too, from the
fifty years prior to 1950 only 1916 is included in ninth place (13.7%). In our millenni-
um 2013 had the lowest volatility. With its 13.3% figure, it shares fourth place in the
list with 1972.
Figure 3 illustrates well that some change occurred at the beginning of the 1950. Since
then the data have been lower than before. The phenomenon is known as a level shift,
which is one of the typical examples of outstanding values. In the case of a level shift the
data are shifted equally in a positive or negative direction from a particular time. Among
the examined volatility data that shift took place in the negative direction. The reasons
behind the shift are not known (most probably they were caused by the changes in the
measuring method).
Apart from analyzing the volatility figures, we should also take a look at which years
showed the largest differences compared to the preceding year. If we look at the shifts
upwards, i.e. the largest positive differences, then 1929 is in first place again. In that year
not only volatility was high, it increased significantly even compared to the preceding
year (by 23.7%). There are in total five years, which are included among the years with
the greatest volatility and the highest positive change. 2012 was another year of the same
category. Although it was last in the previous order, it increased by 16.6% from 2011,
which landed in fourth place.
1951 brought the largest negative change from the preceding year (23.7%), when
volatility was also very low (13.1%, the second). Of the years of the 21st century, 2013
and 2010 are both included in the list of the years reflecting the greatest decrease. The
first is in second place, the latter is in eighth place with 20.9% and 12.9% decreases
respectively.
90
Figure 4. 10-year volatility data for the daily average temperature
The volatility of the daily average temperature data, calculated for ten-year peri-
ods are shown in Figure 4. The figure shows the level shift, observed also for annual
volatility, starting from the 6th decade. If the tendency of the first five decades had
continued i.e., if the shift had not occurred the volatility data could be described re-
liably with a straight line slightly increasing trend. For the actual data, if we intended
to apply a straight line trend, it would be declining and the accuracy of the adjustment
would reflect a relatively low figure (r2=37.4%). Choosing polynoms from the analytic
trends, however, will lead to relatively good correlation. It is generally true that by in-
creasing the number of degree of polynoms of the accuracy of the curve also improves.
Compared to a third degree polynom, a fourth degree polynom results in hardly any
increase in the data series forming the basis of our analysis (r2=66.8% to r2=66.9%), but
in the case of a fifth degree polynom, the improvement becomes significant (r2=79.6%).
These results may be important for a future model.
Volatility of daily maximum temperatures
The year-on-year volatility is significant also in terms of annual absolute minimum
and maximum temperatures, but both the minimum and maximum temperatures are
undoubtedly rising.
In terms of the annual absolute maximum temperatures we can observe that only one
of the top ten years was from the first half of the last century, and three were from the
2000s. The absolute peak so far was 2013 with 39.7oC. Looking at maximums, seven from
the ten “coldest years” were before 1950, with a negative peak of 28.0oC in 1926 (Figure 5.).
91
Figure 5. Annual maximum temperatures 1901-2013.
We began analyzing the volatility of daily maximum temperatures on an annual ho-
rizon too (Figure 6). The volatility figures spread between 18.2% and 25.7%, i.e. within a
range of 7.4%. The annual average volatility data are on average 6.4% different from the
average figures, which is smaller than the small distribution observed for daily average
temperatures.
Figure 6. Annual volatility of daily maximum temperatures 1901-2013.
Looking at Figure 6 it is clear immediately that at the end of the analysed period vol-
atility figures surpass a threshold (25.0%), which was never exceeded by the volatility of
any year in the previous period. The list of ten years with the greatest volatility is led by
2012 (25.7%). (That year was only 10th in the order of annual volatility figures, calculated
from the daily average temperatures.) Four more years from the 21st century are also
included in the top six places of the list. That means that the increasingly extreme weather
conditions these days may be verified more in terms of the daily maximum temperatures.
92
Concerning the least variable years, 1972 is at the top of the list with 18.2% volatility.
The other years of the list include five years prior to 1920 and none after 2000.
In terms of changes between individual years the list of positive changes is topped by
2011 with 24.3% rise over 2010. 2012, which showed the highest volatility, is not included
in the list, because volatility was already high in 2011. However, there are two other years
in the 21st century that showed remarkably high increase over the previous year. The
increase in 2001 was 2,.% and in 2009 it reached 10.7%.
Concerning negative changes, 2013 leads the list with 18.8% decrease. There are four
years when low volatility was also the result of a major decline. In 1996 volatility turned
out to be the third lowest figure (18.5%) following a drop of 15.7%. The volatility in 1910
reached the fifth lowest figure (18.7%) after a decrease of 14.4%. A fall of 13.3% could
be observed in 1972, which had the lowest volatility. 1978, which had the second largest
volatility, produced a decrease of 11.5%.
If we intend to capture the trend of the volatility time series, we should opt for a
polynomial trend again. However, in that case even with a fifth degree polynom only
a very weak (r2=30.0%) explanatory power could be achieved. The trend, however, is
definitely increasing.
The same may be said for volatility, calculated over a period of 10 years (Figure 7).
In other words, the figures reflecting the volatility of daily maximum temperatures are
increasing in the time series with a trend that can be described well with a fifth degree
polynom (r2=92.0%).
Figure 7. 10-year volatility data for daily maximum temperatures
It should be noted that the level shift, which could clearly be detected in the average
temperatures could not be found for the daily maximum temperatures either in the
annual, or the ten-year volatility time series.
93
Volatility of daily minimum temperatures
Analyzing the absolute minimums, it is clear that 1929 was the coldest year when
-29.3oC was measured. On the list of the ten coldest days 1985 represents the last third
of the 20th century with -21.9oC, which puts it into 7th place. The last of the negative
record holders, i.e. the years with the fewest cold days include only 1910 and 1911 from
the first half of the last century, and also two years from the 21st century. The lowest
minimum temperature of -5.5oC was measured in 1974 (Figure 8).
Figure 8. Annual minimum temperatures 1901-2013.
The annual volatility of minimum temperatures is distributed more (23.7%-15.5%),
than in relation to the other two weather data. The relative distribution is still only 8.3%
(Figure 9).
Figure 9. Annual volatility of daily minimum temperatures 1901-2013.
94
The volatility of minimum temperatures was the lowest, 15.5%, in 1916. The ranking
order shows that the five years with the lowest volatility were prior to 1920, but even
the list of ten years includes only 2011 from the years after 1951. As the figure also
shows, the volatility of the weather in terms of minimum temperature was very low in
the first third of the 20th century. The highest volatility was measured in 1927 (23.7%).
In the declining order based on the annual volatility of daily minimum temperatures
there are years only from the 20th century. However, three years of the last decade of
the century are included in the list. Volatility was 22.3% in 1996, 21.9% in 1997 and
21.7% in 2000.
Examining the changes, 1952 was an outstanding year, which also tops the list of the
positive changes with 40.1% increase. The most volatile year of 1929 is in second place,
with 37.0%. 2012 takes the fourth place in the list, structured according to the increase,
when annual volatility increased by 17.3% over the preceding year.
Rather large changes can also be observed downwards too. In 1936 the volatility
dropped by 20.4% compared to the preceding year. This is a negative record. The decline
in 1930 (20.0%) is only slightly behind. In our current century 2011 and 2013 were the
two years with a major drop (12.8% and 10.6%) in the volatility of subsequent years.
The trend of volatility data could be captured with a straight line method only with
very little accuracy. The accuracy of the trend reflecting a slight increase hardly reaches
r2=28.1%. The low determination coefficient also shows that a polynomial trend would
be more suitable for such purposes too. The accuracy of the third and fourth degree
polynomial trends is hardly different (r2=43.2% and r2=43.4%) and is significantly lower
than the result with the fifth degree polynom (r2=51.4%). As previously observed, the
most appropriate trend was falling until the 1920s, and then increasing until the 1940s.
It remained the same until 2000, and then started to decline.
The volatility of minimum temperatures over a period of ten years shows a very
similar picture (Figure 10). A fifth degree polynom can almost fully capture that time
series (r2=98,.0%).
Figure 10. 10-year volatility data for daily minimum temperatures
95
Volatility calculated from the data series of temperature fluctuation
The daily temperature fluctuation could put a strain on the human body, therefore
it is important to know the difference, i.e. the range between the maximum and mini-
mum temperatures. The annual maximum figures of such differences were falling until
the 1930s, then stagnating until the mid-1980s, but began to rise since then. The largest
difference occurred in 1943. There was a day in that year with a temperature fluctuation
of 23.8oC In 2011 the same figure was “only” 23.5oC, followed by 1911 and 1990 with the
third highest fluctuation (23.4oC). In that respect 1927 and 1982 were the most fortunate
years with only 17.4oC temperature fluctuation (Figure 11.).
Figure 11. Maximum daily temperature fluctuation, 1901-2013
The volatility figures, calculated for the intra-day temperature fluctuation, i.e. the
difference between the daily maximum and minimum temperatures, vary between 30.8%
and 22.1%, 7.6% relative distribution (Figure 12.).
Figure 12. Annual volatility of daily fluctuation, 1901-2013
96
The greatest volatility of 30,8% occurred in 2012. Volatility above thirty percent oc-
curred only three times over the examined 113 years, and all three respective years were
after the year of 2000 (2003, 2011, 2012). The analysis of the ten years with the greatest
volatility shows that five years were after the turn of the millennium. The lowest volatility
(22.1%) was observed in 1916, but volatility was only 0.1 percentage point great in three
other years: in 1903, 1907 and 1910. 1926 was the latest year included among the ten years
with the lowest volatility calculated from the daily maximum temperature fluctuation.
The degree of volatility increased most in 2011 (22.1%) i.e., that year was outstanding
not only due to the size of volatility, but also due to the size of the change. There are four
other years (1929, 1942, 1952, 1970), when similar phenomena could be observed, i.e. high
volatility and also a great deal of change over the previous year.
The list containing the year-on-year negative changes is topped by 1972. In that year
volatility was 15.5% lower than in the preceding year. In 1951 the decrease was only 0.5
percentage point smaller. There are only two years in the list (1910, 1926), which had
extremely low volatility and, simultaneously, the largest decline over the previous year.
Figure 13. 10-year volatility data for the daily temperature fluctuation
The time series of volatility, calculated for a ten-year period shows (Figure 13) that
a fifth degree polynom would almost perfectly capture it (r2=98.3%), because following
the decline after the initial increase the volatility of the last three decades shows a clear
increase.
Summary
The volatility of daily average temperature data does not reflect an increase. Although
an increasing tendency can be derived from the daily average temperatures, the even
increase was not affected by volatility. Volatility would increase, if the daily average
temperature data series showed a great deal of fluctuation.
97
However, the increasing trend in the variability and volatility of the daily maximum
temperatures is an important result. The volatility of the daily minimum temperature
data series has shown a decline over the last few years. However, in the case of the daily
temperature fluctuation the increased volatility is obvious.
The volatility of daily average temperatures over a period of ten years varied between
45.2% and 51.9%, that of daily maximum temperature was between 62.9% and 72.0%,
that of the daily minimum temperatures was within the range of 54.4% and 66.1%, and
that of daily temperature fluctuation ranged between 74.0% and 90.0%. We can conclude
that while the volatility of average temperature data over a period of ten years varied
within a relatively small range (6.7%), the same range was much wider for the other three
indicators (9.1%, 11.8%, 14.0%). The greatest deviations from average (5.6%) could be
measured among the daily fluctuation. The smallest differences can be observed among
the average temperatures, where the data departed from the average ten-year volatility
on average by 2.1%.
It was proved that volatility as method is suitable for capturing an increase in ex-
tremes not only in economics, in the analysis of stock exchange data or price fluctuation.
Just like prices, weather conditions are also data that can be described with increasing
or decreasing tendencies in time and the increase or decrease in the distribution or var-
iability of which can be described well with the volatility calculation.
References
Domonkos, Péter (2008): Homogenizáló módszerek alkalmazásának hatása a detektálható
hômérsékleti trendek megbízhatóságára [Impact of the Homogenisation Methods on the
Reliability of Detectable Temperature Trends]. Légkör Volume 53. No. 1.
Farkas, Péter (2010): „Devizaárfolyam-volatilitás a pénzügyi válság idején” [“Exchange Rate
Volatility during the Financial Crisis”], Gyula Kautz, Commemorative Conference publication
Syud Amer Ahmed, Noah S. Diffenbaugh, Thomas W. Hertel, David B. Lobell, Navin Ramankutty,
Ana R. Rios and Pedram Rowhani (2010): Climate volatility and poverty vulnerability in
Tanzania. In Global Environmental Change. Volume 21, Issue 1, February 2011, Pages 46–55
Szalai, Sándor, Konkolyné, Bihari, Zita, Lakatos, Mónika, Szentimrey, Tamás (2005):
Magyarország éghajlatának néhány jellemzője 1901-től napjainkig [Features of Hungary’s
climate from 1901 to the current days]. National Meteorology Service.
Zsembery, Levente (2003): „A volatilitás előrejelzése és a visszaszámított modellek” [“Volatility
Forecast and Denormalisation Models”], Közgazdasági Szemle, Volume L. pp. 519-542.
98
Management of Environmental Risks, Risk Management Methods
Csaba Székely
ABSTRACT: Risk assessment includes the risk identification, analysis and assessment phases. In relation to environmental risks it refers to the survey of the probability of occurrence of events which appear as a result of changes in the environmental conditions, caused by human activity. The risk analysis of the increasingly complicated environmental problems calls for considerable development in the methodology. These days quantitative and qualitative information may also be used in most methods.By projecting environmental risks, important information can be supplied for decisions on sustain-able development, but such information is often missing. The primary objective of environmental risk management is to satisfy the information requirement of decisions. Risk management means the selection and application of options which facilitate planned changes in the probability of occurrence and risk impacts and the implementation of options. The proposed strategy in general may result in the termination, reduction, transfer or bearing the risks. The implementation of the strategy must be monitored in order to keep the risk at an acceptable level. If it does not happen, the risk assessment and risk management processes will need to be repeated as required.
The risks discussed in the research assignment always originate from the environ-
ment. They actually emerge in space, the air, water, in the ground, the soil, or the bio-
logical food chain, or they convey the risk to people.
However, their reasons and characteristics may be very different. Some are created
by people introducing new technologies and products, others are the results of natural
processes and, as natural risks, are connected to human activity or settlements. Yet an-
other group of risk emerged totally unsuspectingly, in the period when the technology
or activity was developed (e.g., impact of fluocarbon spray on the ozone layer).
Environmental risks generally cause damage to people who are totally innocent: they
suffer the consequences but not as a result of their own decisions. The consequences may
exert their harmful impact in later periods on subsequent generations too (e.g., unrea-
sonable management of natural resources).
Types of environmental risks
The majority of environmental risks were brought into the centre of attention through or-
ganization and industrialization; they are the consequences of economic development. It is not
accidental that these risks are associated with countries and regions that are highly industrial-
ized. Other risks spread more in poorer countries with insufficient nutrition and housing, etc.
99
The most important risks can be derived from lists created from international obser-
vations. Table 1 presents the internationally observed risks that are considered the most
important according to the SCOPE 15 classification (1980).
The European Union also classified the environmental risks that it deems most im-
portant and assigned “euro codes” to such natural and industrial disasters as well (EC,
2010). The following table lists the disasters described by the EU and their codes (Table
2). The environmental risks occurring as a result of climate change may occur in several
dimensions, at several levels and in different scopes and may have different causes.
These days climate change is primarily associated with the global warming, which re-
lates to an increase in the emission of greenhouse gases. However, over the course of history,
the climate of the earth changed due to various reasons and with different consequences.
Certain analyses describe cyclical changes involving cooling and warming periods.
Table 1: Main internationally observed risks
ECOLOGICAL MONITORING soil degradation - globaltropical forest cover reductionrangelandsriver and sediment dischargeworld glacier inventoryisotope concentration increase in precipitation
BIOSPHERE Wildlife sampling and monitoringImpact of pesticide residuesLiving marine resources
POLLUTANTS Air quality monitoringwater qualityeutrophication in inland waterfood and animal feed contaminantsionising radiation
CLIMATE climatic variabilityWorld weather watchsolar radiationatmospheric ozoneclimate changeglacier mass balance and fluctuationatmospheric pollutants and effects
OCEANS Pollutants in regional seasOpen ocean watersMarine oil pollutionOcean-bed contaminants
Table 2: EURO codes of different types of natural and industrial disasters
Type of disaster Technical/normative framework
Forest fires Eurocode 1 (actions on structures) defines protective design measures against fire for buildings made of various materials (steel, concrete, wood, masonry)
Ground movements Eurocode 7 defines calculation and design rules for stability of buildings according to Geotechnical conditions of construction site (XP ENV 1997, PR EN 1997-2, ENV 1997-3)
Earthquakes Several rules were worked out in the framework of Eurocode 8: EN 1998-1 (general rules, seismic actions), EN 1998-3 (assessment and strengthen-ing of buildings), ENV 1998-4 (reservoir, pipes), EN 1998-5 (foundations, structures), EN 1998-6 (towers, masts …)
Storms, hurricanes Wind resistant design of buildings is covered by Eurocode 1 - EN 1991-1-4
Cold waves Eurocodes cover protection against cold and snow
Heat waves and drought EN 1991-1-5 includes design to resist heat wavesPartly covered by Eurocode EN 1997-1-1 (Geotechnics)
Industrial and technological hazards
Eurocode 1 (EN 1991-2-7) also defines building design rules against ex-plosions
Marine pollution and oil spills Technical norms for vessels
Source: EC, 2010
These days in relation to global warming science generally focuses on the various
changes and the risks associated with them. These risks also form a cause and effect
relationship, as indicated below:
global temperature increase,
melting of the arctic ice layer and mountain glaciers, as well as permafrost areas,
modification in the direction of the sea currents,
increase in the sea level and flooding the areas on the shore,
Scenario Analysis: Social-Economic Impacts of Long-Term Climate Changes Affecting Agriculture, Forestry and Local Communities
László Kulcsár - Csaba Székely
ABSTRACT: Scenario analysis is a method generally used for the analysis of the impact of climate change. It cannot be confused with projections. A scenario analysis provides an alternative future vision, the actual occurrence of which heavily depends on the occurrence of the identified key factors. The study deals in detail with the scenario analysis methodology and then outlines three alternative scenarios by relying on the background studies described in the book, estimating also the probability of their occurrence.
The methodology study dedicated to risk analysis (Székely 2014) clearly showed that un-
certainty makes it very difficult to properly forecast the future development processes of
individual environmental factors. That uncertainty equally applies to natural and social
sciences. The results and assumptions of natural sciences gain true importance when they
are applied to the development of society and economy. That is why, as also mentioned
in the introductory study, interdisciplinary research dedicated to climate change proved
to be very successful across the world.
The scenario analysis has been recently used more extensively to analyse complex
global problems. Bohensky and his partners (Bohensky et al 2011) pointed out that
scenarios should not be mixed up with projections even though they have some simi-
lar features. A scenario is an alternative vision for the future, which lies on qualitative
and quantitative observations. In other words, the scenarios are similar to a set of
hypotheses, which reflect a lot of uncertainty in terms of components and the future.
According to Bohensky, each scenario contains a great deal of uncertainty and low
controllability. Why are scenarios still needed in economics and in social sciences?
The fundamental reason is that the studied phenomena and processes, such as climate
change and its impacts, are rather complex and the triggering event itself is very com-
plex with a great deal of uncertainty.
The scenario analysis can be used to analyze the future of complicated processes
when traditional mathematical and statistical methods fail. Naturally, it does not
mean that with the help of a scenario analysis the same clear and provable quantitative
conclusions can be reached as with more simple mathematical models, suitable for
describing deterministic situations. Even so, methods need to be applied which can at
116
least provide some guidance about the future of complicated natural correlations even
if they cannot give a clear result.
Scenario analysis as a method
The scenario refers to a future vision that results from logically related assumptions. The
scenarios describe the hypothetical consequences of related events in order to identify
the cause and effect relationships and the decision making situations. Various versions
and options need to be outlined, which represent characteristic development trends
(Hungenberg, 2012).
In the course scenario analysis, alternative future trends need to be described that
lead to future situations. The scenario technique is based on the analysis of extreme
situations. By analyzing the best, worst and expected cases, potential consequences and
their probability may be identified in the form of sensitivity analyses. A scenario is a
possible alternative future vision.
The scenario analysis is based on a descriptive model that describes potential future
events. It may also be used to identify risks of potential future development and to
describe the impacts thereof. Although a scenario analysis cannot be used to project
probability, by considering the consequences, society and politicians can be assisted in
developing their strength and flexibility in adaptation to foreseeable changes.
Steps of a scenario analysis
In general, the scenario analysis is divided into phases in literature. There are two dif-
ferent approaches:
a forward approach,
and a backward approach.
The first two steps (task and problem analysis and influence analysis) are the same
in both approaches. It is then followed by the elaboration of the scenarios according to
the two different approaches. Finally, the evaluation and interpretation phases are also
the same in both approaches.
Task and problem analysis
In the course of the task and problem analysis, first the process forming the subject
of the analysis must be defined clearly. In general, it requires a longer iterative process,
because in the course of the definition and description of a complicated process, profes-
sional knowledge, experience and imagination are needed. The Delphoi method may be
an adequate methodology guide in defining the process.
Then key factors (descriptors) are defined, which may inf luence the analyzed
process and scenarios to be developed. The output of this phase could be a detailed
117
task and problem description and a list of factors. Table 1 contains a list of factors
as an example, which may inf luence the situation of as particular region in relation
to climate change. Brainstorming or the nominal group technique may be useful
methods when defining the key factors. These creative techniques use associations
to generate ideas that may be suitable for selecting the right key factors after suffi-
cient filtering.
Natural, technological and anthropogenic factors, where the changes (in anthropo-
genic and technology factors) primarily depend on society and economy have a key role
among the key factors.
Influence analysis
In the influence analysis we wish to identify how the various key factors influence
each other. In order to do that, first a network table has to be prepared. The descriptors
are compared in the table. The objective of a direct comparison is to identify the degree
of correlation between the various factors (no influence, average and strong influence).
Apart from that, indirect influence (cause and effect chains) can also be detected by using
e.g., Ishikawa diagram (otherwise known as fishbone diagram). The general structure of
a network table is illustrated below.
Table 1: Influence analysis
Influencing/
InfluenceFactor 1 Factor 2 Factor 3 … factor n.
Accumulated
influence
Factor 1 - 0 0 … 0 0
Factor 2 0 - 0 … 0 0
Factor 3 0 0 - … 0 0
… … … … … … …
factor n. 0 0 0 … - 0
Accumulated suscep-
tibility to influence0 0 0 0 -
Source: Baum et.al, 2007
After developing a network table, the impacts are summarized, the result of which
may be illustrated in an influence matrix.
The output of the influence analysis includes the network table and influence matrix
and an overview of the degree of influence of the various factors. By using those, the
generally large number of influencing factors can be reduced to a manageable quantity,
and only the factors exerting the greatest influence may be selected.
118
Source: Baum et.al, 2007
Figure 1: Influence matrix
Extrapolation of trends and definition of scenarios
According to the forward approach, the various development options of each selected
factor need to be defined according to the following question: what outcomes / future
development options can be envisaged for each factor?
The combination of the various factor outcomes will generate the individual scenarios.
As an example, the first outcome of factor No. 1 is combined with the second outcome of
factor No. 3 (e.g., politics will take a good turn, and all important countries will adhere to
the Kyoto Protocol and the local economy will become sustainable by using the available
options of environmental technology. As under certain circumstances not all combina-
tions make sense, or certain combinations exclude each other, or several combinations
may be merged due to similarity or significance, certain alternatives should be combined
or the analysis should be reduced to selected scenarios or sets of alternatives. In order
to work effectively with scenarios, at least 3 and no more than 8 scenarios should be
defined. Generally at least the two extreme scenarios and a few other selected scenarios
should be further analyzed.
By using the correlation analysis, various subsequently occurring events can be
analysed, focusing on their correlations. That is how the previously identified potential
scenarios can be investigated according to plausibility. Those scenarios need to be de-
fined, which illustrate, yet consistent statuses of each factor.
With the backward approach, the scenario refers to two or three alternative future
visions, describing e.g., the worst and best possible status of development and, as the
119
first scenario, the continuation of the current situation. Then those future visions “are
dissolved” in the most important factors. That is the basis on which consideration should
be given to what changes should occur in order to achieve a specific scenario.
The outputs of this phase include the potential outcomes of the various factors/de-
scriptors or their combination/connection into various scenarios. In relation to that the
various scenarios need to be described/formulated in order to make them understandable
and communicable.
Evaluation and interpretation
In this phase, further analyses are conducted on the selected scenarios. The prob-
ability of occurrence of the scenarios is estimated and the opportunities and risks
related to each scenario are compared. In addition, the scenarios are also evaluated
according to their plan and actual situation (the scenario in which we are at present
and direction in which future will develop). Accordingly, organizations can define
measures/action options for each scenario with which they can prepare for their im-
plementation. With the help of the scenario, an organization may review its previous
strategy. If it concludes that the current strategy is unlikely to be successful in any
elaborated scenario, the strategy needs to be revised. Thus, the scenarios may help find
robust strategies for the future.
The output of this phase may be the evaluation and comparison of the selected sce-
narios and derived action options and measures.
Features of the analytic process
A scenario analysis can be conducted in a formal or informal structure. After estab-
lishing the research team and putting in place adequate communication channels, and
wants the relationships and topics of the problem have been identified the nature of the
potential changes should also be defined. In order to do that, the main trends and the
probable time of occurrence of the changes need to be identified, which also requires
imagination about the future.
The following changes may need to be considered:
• changes in the needs of the stakeholders,
• decisions to be made in the future, which may have several potential outcomes,
• external (e.g., technology) changes,
• Changes taking place in the macroeconomic environment (regulations, demo-
graphic changes, etc.).
Some changes will inevitably occur, and others may be uncertain.
An actual change may also be the consequence of a different risk. An example can be
when consumer demand for foodstuffs changes due to climate change. That influences
food exports and the food products that may be produced locally.
120
The local and macro economic factors and trends can be listed and ranked according
to their importance or uncertainty. Special attention must be paid to the most important
and most uncertain factors. The key factors and trends may also be illustrated relative to
each other (in the form of a “map”) in order to highlight the areas from which scenarios
can be developed.
Such scenarios need to be developed which focus on the potential changes of the
parameters. Then a “story” has to be written for each scenario, explaining the path to
be followed from the present until the respective scenario is reached. The stories may
contain life like parts, which increase the value of the scenario.
Then the scenario can be used for testing or evaluating the original issue. The
tests take into account any significant, predicable factor (e.g., use of samples), and
then seeks to identify how “successful” the policy, activity can be in the new scenar-
io, and identifies the “pre-test” results from “what if ” questions asked in the model
assumptions.
As the scenarios can only be defined as “segments” of the potential future, it is im-
portant to make sure that they take into account the probability of occurrence of indi-
vidual events (scenarios) i.e., the risk framework system. As an example, when analysing
the worst and estimated scenario, an attempt must be made to classify and express the
probability at which the various scenarios will occur.
We may not be able to find the test matching scenario but the review must be
closed with a clearer and more straightforward result about the existing options and
how to modify the selected development direction or action to reflect the changes in
the indicators.
Strengths and limitations of the scenario analysis
A scenario analysis takes into account several future situations, which may be advanta-
geous compared to traditional approach, which trusts high average and low projection
types. It assumes that when historic data are used, future events will occur as a likely
continuation of historic trends. This is important in situations when there is little knowl-
edge available based on which long-term risks can be defined. However, in such a case,
strength can be coupled with a problem that there is such a great uncertainty about the
future that part of the scenarios cannot be considered real.
The greatest problem of the scenario analysis relates to the availability of data and
the ability of analysts and decision makers to develop real scenarios that can be used to
test potential consequences.
A scenario analysis as a decision making tool may also hide threats when the elabo-
rated scenarios are not sound enough, when the data are based on speculations and when
the realistic result is not recognised as such.
121
Potential scenarios concerning climate change
in the agriculture and forestry sector
Macroeconomic and political approach
In that approach, based on Bohensky et al. (2011) the basic value of the scenario is
reflected in the market-economy-competition based theory of social-economic develop-
ment and in its contrary, development towards well-being driven society and economy.
In other words, at one end point of the value set, traditional market development is
given the green light and consumption has central significance, while ecological and
environmental difficulties are pushed into the background. At the other end of the scale
development, driven by market conditions faces strong environmental problems and so-
cial resistance, while ecological threats become visible and near. On coordinate therefore
is the dimension of a rural economy and society, and the other coordinate is a dimension,
typical for the whole economy and society of the country. In both dimensions, there is
a scale of the described basic value. The scenario shows the development expected in
each combination and how the experienced/likely climate change will affect the rural
areas and population in the various types of development-visions.
Source: edited by the authors
Figure 1. Development visions based on different basic
values: Main pillars of the scenario analysis
122
One scenario recalls the situation of classic market capitalism to the general public. The
situation here is similar to the relationship between developing and developed countries.
To a certain extent “island type” Modernization hubs are created as described by Korten.
Social-economic equalization is still missing and regional social-economic disparities do
not decrease. There is another scenario, the contrary of the former one but, as we will see,
its implementation is hindered by cultural, political and financial impediments.
Special trends in the development of the country and rural areas
The individual scenarios clearly show that we cannot avoid the processes of macro
level development of economy and society and we must take into account positive and
negative aspects of each scenario, yet the probability of the specified scenarios is different.
Earlier we saw that macro social and political relations are not always favorable to
economic and social actors facing the problem of climate change. Certain support is still
available on the production side of agriculture and forestry management, but it should
also be said that support and amounts received from insurance do not sufficiently en-
courage either an increase in the adaptation ability, or the mitigation of negative effects.
A further extremely important problem is to enforce social justice in the local and
central measures that mitigate the negative impacts of the risks of the climate change.
Brisley et al (2012) highlight that the enforcement of social justice assumes the identifi-
cation of vulnerable social groups and preference to information and advisory services
to them besides financial support. In the scenario analysis, we tried to make sure that
those aspects were given the required significance.
Below we shall present the potential social-economic consequences of climatic effects
primarily in agriculture and forestry management, as well as the environment of the
settlements which are likely to prevail in the country and in the rural areas in the second
half of the 21st century (Gálos 2014).
It is clear from the described key factors that state intervention cannot be avoided in the
case of vulnerable groups of farmers. It is also clear that the agricultural and forestry man-
agement scenarios relating to climate change go beyond the analyzed sectors. We strongly
argue that within the analyzed economic sectors the negative consequences of climate
change cannot be remedied in the long term. The scenarios go beyond those boundaries.
Some of the negative impacts of climate change directly affect the analyzed sector of
the economy, while others exert a primarily negative impact on the total local community
or on certain demographic groups thereof. The scenario analysis must take into account
those discrepancies even if the impacts cannot be clearly separated from each other. E.g.,
due to a sudden great volume of rainfall and soil erosion, there may be groundwater or local
floods (Judit Vancsó Mrs. Papp, Csilla Obádovics, Mónika Hoschek 2014), which do not
simply affect agricultural producers, but the total population of the region, especially the
disadvantaged groups of the population, even though they are not significant actors in the
particular economic sector. Reisinger et al. (2011) pointed out that local governments have
123
an outstanding role in creating awareness of the risk factors that result from the vulner-
ability caused by climate change, and in the assessment and elimination of the risks. The
required rules must be adopted accordingly, and the necessary interventions must be made.
Table 2. Key factors in the relationship of agriculture and
forestry management, as well as climate change
Problems reflected in the
key factors
Social-economic determination of sensitivity/vulnerability
Adaptation/Response/Strategy
Agriculture: Increase in the frequency of ex-treme weather conditions. Increase in the frequency of unfavorable natural condi-tions (drought or sudden pre-cipitation, ma-jor temperature fluctuation).Increasing volatility in agricultural production.
Economy1. Decrease on the economic performance and
income generating capacity of agricultural producers, more uncertainty and deterio-rating market position.
2. Increasing regional disparities.3. Missing capacities to prevent vulnerability
and disadvantages.4. Inadequacy and increasing expense asso-
ciated with resources received from insur-ance.
Society1. Increase in the sensitivity of families signif-
icantly exposed to agriculture.2. Increasing sensitivity of poorer and more
disadvantaged families engaged in agricul-ture, deterioration in their position.
3. Increasing social differences.4. Lack of financing for measures that reduce
sensitivity and vulnerability5. Lack of the required knowledge, cultural
capital and information6. Low level of adaptation capacity 7. Low level of cooperation, solidarity and in-
terest enforcement (social capital)8. Unfavorable demographic composition of
the population (low qualifications, aging)
1. Response: Increasing diversifica-tion in the economy,
2. Response: Profile change in agri-culture,
3. response: Reduction of agricultur-al activities,
4. Response: Creation and devel-opment of local institutions, strengthening cooperation
5. Effective and intensive advisory services
6. Increase in planning capacities7. Response: abandoning agricultur-
al activities8. Response: Migration of the family
engaged in agriculture or certain parts of the family
9. Increase in state aid that depends on vulnerability
Forestry: Unusual weath-er and variable weather condi-tions. Drought, sudden rainfall, erosionAppearance of pests and alien species, change in the previous composition of species
cerning income,2. Difficulties in the business strategy of forest
users and owners.3. Increasing expenses4. Market loss Society1. Decreasing employment, lost income2. Increase in the role of traditional knowledge
and skills
1. Response: Search for new markets2. Response: Change of technology3. Increase in the planning capacity,
supply of information, advisory activities
4. Increase in the role of traditional knowledge, greater cooperation
5. Response: Strengthening the di-versification of the economy
6. State aid depending on vulnera-bility
7. Response: Abandoning forestry
Source: edited by the authors
124
From a different aspect, climate changes, and the large and increasing intensity of
temperature volatility triggers not only economic but also significant health problems,
generating difficulties to certain social groups.
The risks affecting settlements may continue to exist for a long time, especially in
disadvantaged and highly exposed regions and settlements. In these areas, sensitivity
is rather large, not only in community areas (settlement protection infrastructure), but
also in health conditions.
Table 3: Key factors in terms of climatic changes affecting
the local community and population
Problems in the scenario
Social-economic determination of sensitivity/vulnerability
Adaptation/Response/Strategy
Increase in the frequency of extreme weather conditions. Increase in the frequency in unfavorable natural conditions (drought or sudden extensive precipi-tation, floods and inland water, major temperature fluc-tuation, volatility).
Settlement, regional, community threatsFloods and inland water 1. Costs local floods threatening urban and subur-
ban areas of settlements (defence, etc.), 2. Household and municipality costs of reaching
flooded areas3. Health expenses (population, municipality)4. Construction expenses (population, municipal-
ity)5. Inadequacy and increasing expense associated
with resources received from insurance.Health threats1. Heart and vascular system problems at the vul-
nerable population (old people)2. Increase in the health causes of traffic accidents3. Increase in the number of accidents at work tak-
ing place in the open air4. Increase in the frequency of auxiliary events
(e.g., bathing accidents)
Settlement, commu-nity adaptationMigration of the population1. Creation of the required
institutions and regula-tions
2. Increase in state aidIndividual adaptation1. Adherence to health reg-
ulations2. Prevention of traffic con-
duct errors (drinking liq-uids, rest, etc.)
3. Adherence to rules per-taining to (illegal) bathing
Source: Edited by the author
The results of the referred studies confirm the conclusion that the perception of
climate change and adaptation categories are present differently among the agricul-
tural population, backing up the similarly differentiated decisions in state and local
intervention which take into account the social and cultural disparities of the different
territories.
The potential scenarios relating to the social-economic impact of climate change af-
fecting the agricultural and forestry management sector rely a great deal on the results
of our analyses. Based on the results those key factors can be created that may serve as
the basis of likely scenarios. The key factors listed below also indicate that state inter-
vention in the interest of vulnerable economic groups cannot be avoided. It is also clear
that the agricultural and forestry management scenarios relating to climate change go
125
beyond the analyzed sectors. We strongly argue that within the analyzed economic
sectors the negative consequences of climate change cannot be remedied in the long
term. Consequently, the scenarios go beyond those boundaries and take into account
the significant regional differences in vulnerability and exposure to climate change.
The health threats may be interpreted at individual and family level and relate to the
size of the territory and sensitivity of the social groups. The high level of vulnerability is
associated with the social-economic disadvantages and their continuation in the longer
term. On the basis of natural scientific studies (Gálos 2014) and due to the increasing
severe climatic effects, the social-economic disadvantages are likely to remain in the
second half of the century.
Summary
The scenarios of climatic impacts, affecting agriculture and forest management and
settlements can be interpreted correctly by taking into account the hierarchy of the
scenarios. The first interpretation framework includes the previously mentioned four
development scenario (A, B, C, D), each of which has a different outcome reflecting the
consequences of climatic impacts related to agriculture and forest management and
regional, community and health factors.
Scenario “D” is the ideal solution for society and the economy (see Table 4.). Social
justice and positive discrimination are extremely important in those local and
central measures that will mitigate the negative impacts of the risks of the climate
change. The enforcement of social justice assumes the identification of vulnerable
social groups and preference to information and advisory services to them besides
financial support.
As stressed above, each scenario is a vision, the future occurrence of which is a hy-
pothesis. The scenarios intend to present strongly different situations in order to enable
decision makers interpreting the phenomena to face the estimated consequences of their
decisions.
It is clear from the above that in terms of the development of the economy and society,
the social-economic consequences of climatic effects will be most relevant for our topic.
The consequences are strongly culture dependent (Jankó 2014, Kulcsár 2014), the natural
scientific results could be the antecedent starting points of the consequences, which are
also uncertain in their effect and time and spatial prospects. As we saw before, even
despite the uncertainty, certain alternatives can be outlined with different probability.
Scenario “A”, which illustrates a market centered situation, seems the most likely. The
biggest threat associated with it is that climatic effects will increase regional differences
and social-economic disparities.
126
Table 4. Summary of the social-economic consequences
of climatic impacts - scenario table
Possible directions in social-economic future of national and rural regions (2015-2050)
Scenario “A” dominance of mar-
ket conditions “worst case”
Scenario “B” abandoned local
endeavors
Scenario “C” low fund ac-
cessing ability
Scenario “D”Positive discriminance
The unfavorable social-eco-nomic position is counter-
balanced by the state“best case”
Climatic impacts
The exposure of sensi-tive and vulnerable social groups and regions will depend on their market position and competitive-ness
The state will not support enough local endeavors to reduce vulnerability and ex-posure, and therefore most of them will re-main unsuccessful
The central endeav-ors and support will not be useful due to lack of local recep-tive skills and lack of preparation
Reduction in the exposure of sensitive and vulnerable social groups and regions
Visions for social-economic consequencesIncreasing disparities, dif-ferentiation of regions, more concentrated eco-nomic advantages and disadvantages, abandon-ing the sector, migration. Increasing disadvantages in settlements. Low level of state intervention
Lack and low amount of central support, slowly disappearing local initiatives and their inefficiency. Funding and informa-tion deficit of the rel-evant social groups
Small isolated groups cannot use central funding effectively. The attraction of support is rather low in the social groups and regions, there is a shortage of funding and a disadvantaged situation, with con-tinued exposure and migration
Major support to the most vulnerable groups in the ad-aptation process, in the reduc-tion of disadvantages and in diversification, in social and health services, preparation of settlements and their commu-nities for reducing the disad-vantages, professional advice, information and preparation. Funding is available and can be used, increasing abilities.