Background Paper on
Assessment of the Economics of Early Warning Systems
for Disaster Risk Reduction1
Submitted to
The World Bank Group
Global Facility for Disaster Reduction and Recovery (GFDRR)
for Contract 7148513
Submitted by
A.R. Subbiah
Lolita Bildan
Ramraj Narasimhan
Regional Integrated Multi-Hazard Early Warning System
1
This paper was commissioned by the Joint World Bank - UN Project on the Economics of Disaster Risk
Reduction. We are grateful to Apurva Sanghi, Saroj Jha, Thomas Teisberg, Rodney Weiher, and seminar
participants at the World Bank for valuable comments, suggestions, and advice. Funding of this work by the Global
Facility for Disaster Reduction and Recovery is gratefully acknowledged. The findings, interpretations, and
conclusions expressed in this paper are entirely those of the author(s).
Facilitated by the Asian Disaster Preparedness Center
1 December 2008
Background Paper on Assessment of the Economics of Early Warning Systems for Disaster Risk Reduction
Contents
Executive Summary ............................................................................................................................. v
1.
1.1
1.2
Introduction and Methodology ................................................................................................ 1
Introduction ................................................................................................................................. 1
Methodology for Quantification of Benefits of EWS ................................................................. 2
2.
Case Studies on Cost-Benefits of EWS.................................................................................... 6
Case Study 1: Sidr Cyclone, November 2007, Bangladesh ........................................................ 8
Group 1: .................................................................................................................................... 12
Case Study 2: 2003 Floods, Sri Lanka ...................................................................................... 12
Group 2: .................................................................................................................................... 16
Case Study 3: Bangladesh Floods ............................................................................................. 16
Group 3: .................................................................................................................................... 24
Case Study 6: 2006 Floods (July – September) Thailand ......................................................... 24
Group 4: .................................................................................................................................... 26
Case Study 5: Climate Forecast Applications- Philippines (2002-2003 El Niño) .................... 26
Case Study 6: India Drought 2002 ............................................................................................ 28
Category 2: Geological Hazards (e.g. Tsunami) ....................................................................... 32
Case Study 7: Regional Integrated Multi-Hazard Early Warning System (RIMES)................ 33
2.1
2.2
2.3
2.4
2.5
3.
3.1
3.2
Non-Market Factors ............................................................................................................... 39
Factors Influencing Adoption of EWS at Government or Institutional Levels ........................ 39
3.1.1 At policy level .................................................................................................................. 39
3.1.2 At political level ............................................................................................................... 42
3.1.3 At technical institutions ................................................................................................... 45
3.1.4 At the community level .................................................................................................... 47
Incentives for EWS ................................................................................................................... 48
Annex A: Methods of Calculating Flood Damage Reduction due to Early Warning ....................... 50
Annex B: Basic Services vs. Value-Added Services ......................................................................... 52
Annex C: Avoidable Damage for Various Sectors – Perception of Small Farmers in Bangladesh .. 55
Annex D: Additional Case Studies .................................................................................................... 56
Annex E: Climate Field Schools in Indonesia ................................................................................... 64
Annex F: List of References .............................................................................................................. 56
Annex G: Terms of Reference for the Paper ..................................................................................... 68
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Background Paper on Assessment of the Economics of Early Warning Systems for Disaster Risk Reduction
Figures
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
Flood affected areas – Sri Lanka, May 2003 ........................................................................13
Historical flood event: extent and crop damage ...................................................................16
Area under production: major crops .....................................................................................17
Cereal production (1972-2001) .............................................................................................18
Improvement in forecast lead time due to CFAB technology, Bangladesh ..........................21
June-July rainfall (1993-2002)..............................................................................................29
RIMES Member Countries ...................................................................................................33
Integration of tsunami and hydro-meteorological subsystems .............................................35
Integration of tsunami and hydro-meteorological subsystems: common elements ..............35
Integration of tsunami and hydro-meteorological subsystems: human resource ..................35
Integration of tsunami and hydro-meteorological subsystems: human resource ..................35
Addressing various gaps in an end-to-end early warning framework ..................................36
Central Water Commission (CWC) of Government of India ...............................................46
Boxes
1.
2.
3.
4.
5.
6.
7.
8.
9.
Benefits of adopting early warning systems ............................................................................2
Benefits of fostering community and institutional involvement..............................................6
Climate forecast applications in Bangladesh,flood forecasting technology ..........................20
Institutional responses to the July 2007 flood forecasts in Bangladesh.................................23
Forecasting technology options & avoidable damages ..........................................................25
Possible measures that could have led to reduction of impacts of 2002 drought ..................32
Agro-meteorological station in Dumangas Municipality, Iloilo Province.............................43
Bird flu claims first Thai victim.............................................................................................44
August 2003 heat wave in France ..........................................................................................44
Tables
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
Case study findings on cost-benefits of EWS ........................................................................ vi
Application of lead time for agriculture...................................................................................3
Decision table- probabilistic forecast information ...................................................................3
Damage reduction due to early warning of different lead times ..............................................4
Summary of damage and losses – Cyclone Sidr ......................................................................8
EWS costs for Bangladesh Sidr Cyclone .................................................................................9
Identifying EWS benefits for Bangladesh Sidr Cyclone .......................................................10
Quantifying EWS benefits for Bangladesh Sidr Cyclone ......................................................11
EWS costs for Sri Lanka ........................................................................................................14
Avoidable damage in two of the five districts affected – 2003 floods, Sri Lanka .................14
Estimated avoidable damage from floods in Sri Lanka, last 3 decades .................................15
Return period of floods ..........................................................................................................16
Major floods affecting Bangladesh in last five decades ........................................................17
Quantifying benefits: July-Aug 2007 Floods .........................................................................18
Estimated avoidable damage for floods in Bangladesh, last 3 decades .................................20
Potential impacts in food and agriculture sector due to various floods ................................21
Actions for utilizing improved flood forecast information ....................................................22
Agricultural risk management options in case of 10 to 15 days early warning .....................23
2006 Thailand Floods – summary of damages and losses .....................................................25
Estimates of cumulative coverage under rice, Orissa 2002 ...................................................30
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Background Paper on Assessment of the Economics of Early Warning Systems for Disaster Risk Reduction
21. Crop damages as per state report, Orissa 2002 ......................................................................30
22. Crop production losses due to drought, India 2002-2003 ......................................................31
23. Impacts of some severe cyclones (1977 to 2006) in Andhra Pradesh ...................................41
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Background Paper on Assessment of the Economics of Early Warning Systems for Disaster Risk Reduction
Abbreviations
ADB
ADPC
BDT
BMG
CBO
CFA
CFAB
CWC
DAE
DITLIN
DoM
ECMWF
EDRR
ENSO
EWS
FFWC
GDP
GFDRR
IMD
INR
IOC
ICG
IOTWS
IPB
IRI
MAO
MM5
MT
NIA
NLM
NMHS
NWMP
NWP
NWRB
OFDA
PAGASA
PAO
RIMES
SLR
TMD
UNESCO
UNISDR
USAID
USD
VND
WRF
Asian Development Bank
Asian Disaster Preparedness Center
Bangladesh Taka
Meteorological and Geophysical Agency, Indonesia
Community-Based Organization
Climate Forecast Applications
Climate Forecast Applications in Bangladesh
Central Water Commission
Department of Agricultural Extension
Directorate for Crop Protection, Indonesia
Department of Meteorology, Sri Lanka
European Centre For Medium Range Weather Forecasting
Economics of Disaster Risk Reduction
El Niño Southern Oscillation
Early Warning System
Flood Forecasting and Warning Centre
Gross Domestic Product
Global Facility for Disaster Reduction and Recovery
India Meteorological Department
Indian Rupee
Intergovernmental Oceanographic Commission
Intergovernmental Coordination Group
Indian Ocean Tsunami Warning and Mitigation System
Bogor Agricultural University, Indonesia
International Research Institute for Climate and Society
Municipal Agriculture Office
Meso-scale Model 5
Metric ton
National Irrigation Administration
Northern limit of monsoon
National Meteorological and Hydrological Services
National Water Management Plan
Numerical Weather Prediction
National Water Resources Board
Office of U.S. Foreign Disaster Assistance
Philippine Atmospheric, Geophysical and Astronomical Services Administration
Provincial Agriculture Office
Regional Integrated Multi-Hazard Early Warning System
Sri Lankan Rupee
Thailand Meteorological Department
United Nations Educational, Scientific, and Cultural Organization
United Nations International Strategy for Disaster Reduction
United States Agency for International Development
United States Dollar
Vietnamese Dong
Weather Research Forecasting
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Background Paper on Assessment of the Economics of Early Warning Systems for Disaster Risk Reduction
Executive Summary
This paper on Assessment of the Economics of Early Warning for Disaster Risk Reduction
provides arguments for investing in a) an early warning system (EWS) that aims to reduce
damages, impacts and disruptions, in addition to saving lives, by integrating high-frequency,
low-impact hazards to systems that only consider high-frequency, high-impact hazards and; b) a
collective EWS for low-frequency, high-impact hazards.
National Meteorological and Hydrological Services (NMHSs) of many countries in the region
are focused on providing basic forecast requirements for high-frequency, high-impact hazards,
such as cyclones. High-frequency, but low-impact hazards, such as storms and floods, are not
given much attention, although cumulative economic impacts are huge. With some investment,
these NMHSs can build their capacities to provide value-added services to meet user
requirements for weather and climate information, in addition to actionable, longer-lead time
early warning information. The benefits of such value-added services, in the form of early
warning information for long-lead (3-10 days) forecast, as well as seasonal forecast, are
elaborated through several case studies. For purposes of this paper, countries were clustered
into four groups:
Group 1: Countries, which currently have only the very basic services in place and
require assistance in upgrading their basic systems and services, comprising of
Lao PDR, Myanmar, Cambodia, East Timor, Afghanistan, Comoros,
Seychelles, Yemen, Madagascar, Bhutan, Nepal, and Sri Lanka
Group 2: Countries with some capabilities for an effective EWS, but which are not
entirely operationalized due to inadequate human resources or other such gaps;
comprising of Bangladesh, Mongolia, Mozambique, Pakistan, the Philippines
and Vietnam; and
Group 3: Countries with robust observation networks and technical capacity to forecast
events with lead time of up to 3 days, but which are trying to address key gaps
relating mostly to generation of location-specific products matching user
requirements and reducing the disconnect between downscaling, interpretation,
translation and communication of such specific forecast information. China,
Thailand and India could be grouped together.
Group 4: Countries with demonstrated potential in seasonal forecasting and application.
It covers countries like Indonesia and the Philippines, which have successfully
demonstrated the application of seasonal forecasts. Cases from Sri Lanka and
India also highlight the immense potential for application of current technology
for boosting agriculture production by forecasting the season ahead, enabling
appropriate response measures.
Table 1 provides a summary of the case study results presented in Section 2 and in Annex D.
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Background Paper on Assessment of the Economics of Early Warning Systems for Disaster Risk Reduction
Table 1: Case study findings on cost-benefits of EWS
Bangladesh,
Sidr Cyclone case
study
 Enhancement of computing resources – i.e. advanced computing equipment, latest

Sri Lanka,
May 2003 floods case
study



Vietnam,
2001-2007 hydrometeorological
hazards case study


Bangladesh,
2007 Flood case
study


Thailand,
2007 Flood case
study

Indonesia,
Seasonal forecasting
case study

Philippines,
Seasonal forecasting
case study
Sri Lanka,
Seasonal forecasting
case study
India,
2002 Drought case
study






numerical weather prediction (NWP) models, trained human resources – in addition to
existing level of services in the Bangladesh Meteorological Department, would help
increase lead time and accuracy of forecast information.
With additional investment for building capacity for translating, interpreting and
communicating probabilistic forecast information, the case study demonstrates that for
every USD 1 invested, a return of USD 40.85 in benefits over a ten-year period may
be realized.
Existing NWP models, coupled with use of model outputs from regional and global
centers, could help anticipate events such as the extreme floods of May 2003.
Cost-benefit analysis reveals that for every USD 1 invested, there is a return of only
USD 0.93 in benefits, i.e., the costs outweighs the benefits, since the significantly
damaging flooding is not very frequent.
In such a case, it makes great sense for such countries to join a collective regional
system, due to economies of scale, as demonstrated in the case study on the Regional
Integrated Multi-Hazard Early Warning System (RIMES).
Increased lead time as well as accuracy due to incorporation of the advanced Weather
Research Forecasting (WRF) model run at much higher resolutions could help reduce
losses and avoidable damages. Due to increased accuracy in predicting landfall point,
as well as associated parameters such as wind speed and rainfall, it would be possible
to reduce avoidable responses – such as evacuation across hundreds of kilometers
along the coast, as well as disruption of fishing and other marine activities.
The case study shows that every USD 1 invested in this EWS will realize a return of
USD 10.4 in benefits.t
Using the damages and losses of the severe 2007 floods, the case study estimates the
avoidable damages and losses due to increased lead time of three to seven days, over a
longer period of 10 and 30 years based on return period information. The technology
to provide this long-lead forecast information is already operational at the Flood
Forecasting and Warning Center of the Bangladesh Water Development Board, and is
called the CFAB technology.
The cost-benefit study reveals that, over a ten-year period, for every USD 1 invested in
EWS, there is a return of USD 558.87 in benefits.
The value of a long-lead weather forecast model is demonstrated in this case study, to
better manage water resources and thereby avoid flooding.
The cost-benefit study however reveals that over a ten-year period, for every USD 1
invested in EWS, there is a very low return of USD 176 in benefits.
Seasonal climate forecasting model has already been replicated in over 50 districts by
the Indonesian government (and is being replicated in other districts).
The case study shows that the indicative value of each seasonal forecast is USD 1.5
million (currently in 50 districts), and potentially USD 7.5 million (for 250 districts)
per season. The actual one-time investment to produce this forecast is not more than
USD 0.25 million, with a marginal recurring cost of USD 0.05 million per year.
The total value of a single seasonal forecast, even if farmers had used the forecast for
planting decision only is USD 20 million. Other sectors could also benefit from this
forecast.
In monetary terms, seasonal forecast applications in the 1992 season and 1997
agricultural seasons would have resulted in benefits of 57 mi USD, with an additional
one-time investment of less than 1 mi USD.
The total value of seasonal forecast-guided decisions in agriculture only, in just one
state, over a ten-year period is USD 160 million.
Further, just at the farm level, application of this early warning information could have
resulted in a saving of USD 1.2 billion in the whole of India during the 2002 drought.
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Background Paper on Assessment of the Economics of Early Warning Systems for Disaster Risk Reduction
For low-frequency, but high-impact hazards, such as the Indian Ocean tsunami in 2004, a
regional or a collective approach is far more economical and sustainable than individual national
systems. A case of the Regional Integrated Multi-Hazard Early Warning System brings home
the point that integrating a multi-hazard approach is economical due to common features (e.g.
data communication and processing facilities and human resources). An integrated or end-toend approach, addressing downscaling of forecast information and interpretation, translation and
application for specific user needs, is also vital in ensuring that the full benefits of early warning
are derived.
The total capital investment in establishing RIMES is only USD 6 million, compared to about
USD 200 million for the tsunami systems of Australia, India, Indonesia, and Malaysia,
combined. The latter estimate includes observation systems, the budget for which may be
significantly reduced by optimizing distribution in a regional observation system. Total annual
recurring cost for RIMES is only USD 2.5 million, compared to the USD 30 million combined
for the four national systems.
Despite the benefits, the case studies also reveal several constraints in adopting EWS as below:
At policy level:
Perception. There is still a lingering perception that natural disasters are ‘Acts of God’, i.e.,
governments/ institutions/ communities cannot do anything but live with disasters. Becker and
Posner suggest, “Politicians with limited terms of office and thus foreshortened political
horizons are likely to discount low-risk disaster possibilities, since the risk of damage to their
careers from failing to take precautionary measures is truncated.” Hard evidence, based on a
systematic study of the cost and benefits of EWS for the country, can convince politicians to
invest in EWS.
Not tangible enough? The benefits from an effective early warning system are not tangible
enough for policy makers as opposed to benefits from an essential early warning system (saving
lives) to divert public finance towards it. While it is easy to survey and estimate the damage and
losses post-disaster, it is still not easy for responsible agencies to convince decision-makers
about the ‘preventable or avoidable damages’ that an effective early warning system can bring.
Creating and demonstrating tools for measuring intangible benefits, engaging the media, and
creating awareness among policy- and decision-makers may be undertaken to make the benefits
of EWS visible.
Unwelcome harbinger? Public awareness on disasters and, by association, early warning
systems are considered as unwelcome in some cases where it could hurt the economic potential
of the area. Local governors in southern Thailand discouraged probabilistic conjecture-based
tsunami forecasts, for fear of losing tourists. Certification for a hazard-ready community, as
practiced in the U.S., would be welcomed by foreign tourists.
Essential EWS vs. Effective EWS? Public policy is somewhat insensitive to invest in
improvements in EWS unless the unwritten disaster threshold tolerance is breached. Mobilizing
public finance for the transition of an essential EWS (saving lives) to the next level of an
effective EWS (saving lives and reducing damages, impacts and disruptions) is very difficult.
Some possible explanations for this could be the removal of the emotive factor once the loss of
lives is avoided, or due to a greater tolerance of disaster thresholds, which limits the impetus to
establish warning and appropriate response systems. In a country with a huge population like
India, this threshold could well go to a few hundred casualties, while in neighboring Bhutan,
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Background Paper on Assessment of the Economics of Early Warning Systems for Disaster Risk Reduction
even one casualty would be treated as a disaster. Hence, a very big event would be required to
precipitate changes in the system to allow the experimentation and adoption of a new, emerging
early warning technology.
At political level:
Political disincentives – lack of continuity? In some cases, an early warning system established
by a previous administration does not receive due backing and financial support from the next
administration, as demonstrated in the case of Dumangas municipality, Iloilo Province in the
Philippines. However, the intervention of the Governor of Iloilo Province ensured that the
system was kept alive, inspiring other municipalities to emulate it.
Political system? Cuba and Vietnam have managed to reduce loss of lives considerably, despite
the high frequency of hurricanes and typhoons, respectively. It is quite provoking to attribute
the success to the socialist model in place in Cuba. However, more likely reasons are that Cuba
has a command state and a highly educated and disciplined professional class, which can be
easily organized for large evacuations and coordinated action among water, power, gas, health,
and other sectors, along with Cuba's neighborhood organization.
In many countries, despite a long culture of multi-party political system, the administration and
political systems are not so accountable to the public, for public opinion to force them to invest
on costly EWS technology. India, for example, still does not have a robust drought early
warning system, despite periodic, massive losses due to drought.
Relief and rehabilitation offers more visibility? Post-disaster relief and rehabilitation provides
an opportunity for the government to increase its visibility and be seen as responsive. However,
public, as well as media, attention is focused on the response, and not on underlying causes
which result in such increasing losses and damages. Investment on EWS, on the contrary,
would be a hard sell as it is abstract and lacks the visibility of expenditure for post-disaster
response and relief.
The poor has no voice? In the Jakarta city floods, Dhaka urban floods, and Mumbai floods,
majority of the people affected are the marginal population who, though numerous, do not have
a ‘loud’ voice. The spurt in economic growth of Shanghai city in recent years demanded a
Multi-Hazard Early Warning System project, as more and more assets are exposed to disaster
risks. Larger populations at risk in the hinterlands still have no access to such warning facilities.
At technical institutions:
Uncertainty of science. There is a lack of incentive in an operational forecasting agency for
identifying, experimenting and operationalizing new technologies. The system is amenable only
towards technology that is proven and demonstrated. In Bangladesh, when the long-lead flood
forecast technology was experimental, there was little interest. Use of longer-lead time forecast,
which is probabilistic and with inherent uncertainties, requires whole-hearted acceptance from
users and commitment from the NMHS to connect and engage with users. This culture is not
commonly seen among the countries of this region.
Multi-disciplinary? First order early warning services that save lives are more straightforward
to implement through the disaster management machinery, as compared to the next level of
services that reduce damages or impacts, using longer-lead time probabilistic forecast
information whose utility encompasses multiple sectors, demanding greater coordination,
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Background Paper on Assessment of the Economics of Early Warning Systems for Disaster Risk Reduction
cooperation and a multi-disciplinary approach. For a developing country, this multi-sectoral
cooperation around an effective early warning is a difficult task to accomplish, and hence does
not take off as rapidly as an essential early warning.
Lack of accountability? Forecasters consider it a success if forecast figures are close to 70% of
the observed figures, irrespective of the damages that occur despite the ‘accurate’ forecast.
No early warning for surprises. The Indian Ocean tsunami of December 2004 (most of the
countries had not faced a tsunami in living memory), the Myanmar Nargis severe topical
cyclone of May 2008 (no cyclone in living memory had crossed Ayerwaddy delta), the recent
Kosi floods in India due to structural failure upstream in Nepal (which was unprecedented in
recent memory), and the typhoon Frank of June 2008 in Philippines which crossed central
Philippines while typhoons only cross northern part of Philippines at that time of the year, are all
considered ‘surprises’. It is quite acceptable for institutions to defend their failure to forewarn
by arguing that the hazard event was a ‘surprise’ for which the early warning was not quite
possible. However, institutions and systems could be sensitive to risk knowledge as there were
cases in the past – 1881 Indian Ocean wide tsunami, 1941 Andaman tsunami, 1945 Pakistan
tsunami – which meant that these ‘surprise’ events were not actually surprises.
Disconnect of early warning with response. Even if early warning information is issued only
one hour ahead, the national institution generating early warning information considers that its
job is done, for it is the responsibility of notified institutions and communities to respond.
Evaluation of early warning is still connected to the dissemination, not to the response that can
be attributed to it. Ideally, the response should be a measure of the effectiveness of early
warning. A set of performance criteria that includes forecast accuracy, rapid notification, userfriendliness, and recipient responses, among others, may be used to evaluate EWS.
At the community level:
Community responses guided by recent experiences. Community responses are influenced by
their recent experiences – if there has been a major event such as a cyclone in the last few years,
then a cyclone early warning results in an over response and panic. If the last known event was
beyond recent memory, then it results in an under response. However, some communities can
keep alive their experiences and pass memories on from one generation to another. In less prone
areas, a major hazard event is treated as a surprise resulting in ineffectual response.
User-friendliness of early warning. Response to early warning is determined by the information
being personalized into knowledge specific to ones’ context. The Orissa Super Cyclone of 1999
illustrates that though coastal population were aware of the cyclone, they did not personalize the
storm surge intensity, which meant people were at risk even in places far away from the coast.
Channel is as important as warning content. Early warning information for Cyclone Nargis was
disseminated up to 48 hours in advance in Myanmar through official channels, including staterun television media. Anecdotal information suggests that communities were informed verbally
by military personnel based in the area. However, there is a general mistrust among the public
of both the media and the armed forces, and hence this did not elicit an appropriate response
from the public. For action to be predicated, ‘It is not enough to believe the message, but also
important to trust the messenger.’
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Background Paper on Assessment of the Economics of Early Warning Systems for Disaster Risk Reduction
Incentives for EWS
To improve early warning system adoption, the following ideas are proposed:
Public awareness. A big push for adoption of early warning could come from empowered civil
society or mass-based organizations. They are mostly unaware of the advances and potential
benefits of technology, but once empowered with the knowledge that many of the events which
have claimed lives or damage to property could be anticipated and impacts mitigated, they
would be able to influence communities and governments to adopt technologies for improved
early warning.
Accountability. If institutions and governments are held accountable for the loss of even a single
human life due to the hazard event, there is definitely a great scope and incentive for
improvement of early warning systems.
Economic sense. The public and government need to be convinced that a large percentage of
damages and losses could be avoided through improved early warning at a fraction of the cost,
for it to invest on improving technologies. Emphasizing the linkages to development by
sensationalizing the avoidable economic damages and losses through the argument that the
amount spent on recovering from avoidable damages or losses could be better utilized for other
pressing development concerns, would also act as an incentive to strengthen early warning
systems.
Removal of barriers. One of the ways to remove some of the barriers is for early warning
institutional systems to incorporate economic and social aspects of EWS, and for early warning
to evolve into a multi-disciplinary field by incorporating pre-impact assessment or potential
damage assessment, including avoidable damages, and identify appropriate response options to
avoid these damages.
Financial instruments. Innovative financial instruments to support proven, but untested,
technologies, and capacity-building of institutions to accept and make use of probabilistic
forecasts in a risk management framework could also be an incentive. As demonstrated by
CFAB, technical research and development capabilities of scientific institutions can be
harnessed to tackle priority hazards, such as floods in Bangladesh, through financial support
from willing donors to develop innovative, emerging technology-based solutions for pilot testing
and improvement through government institutional involvement.
Once successfully
demonstrated, the same can be operationalized and integrated within existing EWS institutional
structure of the government, with necessary financial support from interested donors.
Avoidance of free-rider syndrome. Free early warning services provided by resource-rich “big
brother” countries to neighboring resource-poor countries has led to dissatisfaction among early
warning recipient countries. Reasons for this include not up to expected level of services in
terms of lead-time, inadequate inter-personal communication during hazard situations, national
pride involving provider and receiver, superior and inferior complexes, and other political
factors. These non-market factors, coupled with economic advantages provided by recent
advances in science and technology and information technology revolution, encouraged
resource-poor countries to look for alternatives to collectively own and manage EWS by
themselves in the context of increasing frequency and intensity of natural hazards due to
climatic and non-climatic factors.
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Background Paper on Assessment of the Economics of Early Warning Systems for Disaster Risk Reduction
During the meeting of UNESCO/ Intergovernmental Oceanographic Commission’s
Intergovernmental Coordination Group for the Indian Ocean Tsunami Warning and Mitigation
System in Kuala Lumpur in April 2008, resource-poor countries expressed a desire to establish
by themselves a collectively-owned and managed EWS. A catalytic investment of USD 4.5
million by UNESCAP has successfully encouraged this process for Indian Ocean and South East
Asia for establishing the Regional Integrated Multi-Hazard Early Warning System. This kind of
strategic, small investments could act as incentive to establish a regional EWS not only for lowfrequency, high impact hazards such as tsunami, but also for high frequency, but low impact
hazards.
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Background Paper on Assessment of the Economics of Early Warning Systems for Disaster Risk Reduction
1. Introduction and Methodology
1.1
Introduction
The Global Facility for Disaster Reduction and Recovery (GFDRR)/World Bank and the United
Nations International Strategy for Disaster Reduction (UNISDR) have jointly commissioned an
Assessment of the Economics of Disaster Risk Reduction (EDRR) to evaluate economic
arguments related to disaster risk reduction through an analytical, conceptual and empirical
examination of the themes identified in the Project Concept Note. Findings of the Assessment
are intended to influence broader thinking related to disaster risk and disaster occurrence,
awareness of the potential to reduce costs of disasters, and guidance on the implementation of
disaster risk-reducing interventions. This paper was written to contribute to this Assessment.
The 2004 Indian Ocean tsunami has highlighted the massive losses that can be incurred due to
low-frequency, high-impact hazards. A similar event may have a return period of 50 to 100
years and, for each of the affected countries, to put up an early warning system (EWS) to
provide forewarning of such a rare event would be individually prohibitively costly. However,
by several countries coming together, a collective system becomes economical due to the scale
of operations. If such a system also integrates warning services for high-frequency, low-impact
hazards, in other words more common but lesser damaging events such as heavy rainfall, floods,
storms, etc., cumulatively, the higher costs (relatively) would appear even more justifiable.
If the economic losses due to natural disasters over the last 30 years in any country are
calculated, and even by assuming that the scale of the events remains the same for the next 30
years, given the economic growth and accumulation of wealth, it is clear that more elements
would be at risk with a greater chance of larger direct losses. So, by integrating early warning
systems, the society stands to benefit.
Early warning, though always an important aspect of disaster risk reduction, has gained greater
public attention and, hopefully, more investments after the 2004 Indian Ocean-wide tsunami.
Yet, there is a lot more that remains to be done in the area of early warning systems. This paper
aims to highlight the benefits of early warning systems, identify common constraints, and offer
suggestions to address them.
Specifically, the objective of this paper is three-fold:
1) to show the benefits of early warning systems
2) to explain why, despite these benefits, implementation of EWS is poor
3) to propose how decision-makers could be motivated to improve EWS
This paper introduces the concepts of basic services and value-added services for early warning,
and identifies additional inputs required to upgrade to value-added services, as well as benefits
that may be derived from it. Several case studies are also presented to quantify the costs and
benefits of EWS. Calculations highlight the direct economic benefits due to EWS, as well as the
investments required in terms of institutional arrangements and capacity building, so as to derive
the maximum benefits of EWS.
The non-market factors that stimulate, or constrain, EWS are highlighted towards the end of the
paper, along with recommendations on how success stories could be replicated elsewhere.
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Background Paper on Assessment of the Economics of Early Warning Systems for Disaster Risk Reduction
1.2
Methodology for Quantification of Benefits of EWS
There are several studies on quantifying benefits of early warning systems, especially for flood
damage reduction, such as the studies by Day (1970), US Army Corps of Engineers’ Institute of
Water Resource (IWR) (1991), Chatterton and Farrell (1977), as well as other studies on
economic value of hurricane forecasting, meteorological forecasting and warning services, and
benefits of ensemble-based forecasting (refer to Annex A for further reading). This paper
illustrates, through case studies, the benefits of adopting early warning systems against the
investment required for establishing and operating a suitable early warning system. This paper
adopts the following generic methodology, drawing basic principles from these references to
estimate cost-benefits of early warning systems:
If loss due to a disaster without early warning is ‘A’, and if the decreased loss that may be
incurred after appropriate measures following early warning is ‘B’, then the potential reduction
in damages due to early warning is A - B. However, there may be a cost or investment required
for providing the early warning services ‘C’. Therefore, the actual benefit due to early warning
is A-B-C.
The benefits due to the early warning may be estimated by summing the monetary benefits
accrued as in Box 1 below:
Box 1: Benefits of adopting early warning systems
1.
Direct tangible benefits in the form of damages avoided by households and various sectors due to
appropriate response by utilizing the lead time provided by the early warning
+
2.
Indirect tangible benefits such as avoidance of production losses, relief and rehabilitation costs, and costs
involved in providing such services
In some case studies, the paper also utilizes the concept of opportunity costs, or economic
opportunity loss incurred by either inaction or by inappropriate action to early warning; for
example, the cost of leaving land fallow in response to El Niño forecasts, or planting
inappropriate crops where an appropriate action would have been to shift to short-term crops
such as water melon, maize, etc.
In a developing country context, no accepted tools are available to quantify the value of life, and
emotional and psychological trauma. Hence, the paper does not account for the economic
benefits of lives saved, or direct and indirect intangible benefits such as risk of injuries, trauma,
or suffering avoided due to appropriate actions.
Cost of EWS
The cost of EWS is calculated under three broad components:


Scientific component costs: input costs for technical institutions required to generate
forecast information
Institutional component costs: refers to costs of training and other capacity development
required for institutions to be able to use forecast information, especially to facilitate its
use at lower levels
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Background Paper on Assessment of the Economics of Early Warning Systems for Disaster Risk Reduction

Community component: refers to the input costs at community level to enable them to
adopt forecast information and respond appropriately
Details of the basic services and value-added services with examples are provided in Annex B.
Lead time and application of climate information products
Long-lead time of early warning is greatly beneficial in reducing loss of lives and saving assets.
However, careful utilization of the advance notice provided would also enable planning, which
could reduce even indirect losses by undertaking appropriate responses as warranted by the
situation. A case of use of lead time for the agricultural sector is illustrated below.
Table 2: Application of lead time for agriculture
Forecast product
Weather
Medium range
Lead time
1-3 days
5-10 days
Extended range
(sub-seasonal)
Seasonal
2-3 weeks
1 month and beyond
Application
Securing lives
Emergency planning, early decisions for flood and drought
mitigation, preserving livelihoods
Planting/ harvesting decisions, storage of water for irrigation,
logistics planning for flood management
Long-term agriculture and water management, planning for
disaster risk management
Probability
The issue of forecast accuracy, or the probabilistic nature of the forecast, is also incorporated.
Accuracy of short-term (less than 10 day) forecasts is taken as 90%, i.e., the forecast would be
correct in 9 out of 10 cases, while that for seasonal forecasting is 70%, based on field
experiences with the Climate Forecast Applications in Bangladesh (CFAB), and Climate
Forecast Applications (CFA) in Indonesia and Philippines, respectively. The probabilistic
nature of forecast information with 90% probability for up to 10-day flood forecast is taken into
account by adopting a 2x2 simplified decision table as below.
Table 3: Decision table - probabilistic forecast information
Forecast
Decision
EW not heeded –
response actions not taken
EW heeded –
response actions taken
x
√
√
x
Correct
9 cases out of 10
Wrong
1 out of 10 cases
The loss accrued due to ‘wrong’ forecast (one in ten cases) is deducted from the benefits due to
‘correct’ forecast (nine in ten cases) to arrive at the actual benefits. In other words, the actual
benefits, taking the probabilistic nature of up to 10 days forecasts into account, is calculated by
multiplying the benefits by a factor of 0.8 (i.e. (9-1)/10), since there are 10 possible occurrences,
and also assuming loss due to one ‘wrong’ forecast is equal to the benefit due to one ‘correct’
forecast. (This assumption is conservative, and is taken in the absence of data required to enable
a detailed assessment.) For seasonal forecasting, since forecast skill is taken as 70%, the actual
benefits, taking into account probabilistic forecasting, is arrived by multiplying the benefits by a
factor of 0.4 (i.e. (7-3)/10), since there is a possibility of being wrong in 3 out of 10 cases.
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Background Paper on Assessment of the Economics of Early Warning Systems for Disaster Risk Reduction
Return period
Estimating the benefits over a longer period of time is done through incorporating the concept of
return periods, where readily available, or may be inferred from historical records.
Assumptions made in calculations of avoidable damages
1) A proportion of damage due to one particular event is taken as representative for similar
events in the past or future, if a robust historical damage database is not available. For Sri
Lanka, based on data for the extreme floods of 2003 (one in 50-year return period) which is
readily available, damage for annual floods is taken proportionately as 5%, and that for
major floods (one in ten years) is taken as being 25% of the 2003 floods.
2) In cases where disaggregate damage data is available, such as for movable assets – livestock,
school or office equipment, vegetables or fruit crops, small irrigation structures such as
anicuts – a percentage of such damages is treated as avoidable damage, as listed in Table 4
below. This estimate is based on field experiences (refer to Annex C for further details).
Table 4: Damage reduction due to early warning of different lead times
Item
Lead time
Household
items
24 hrs
48 hrs
Up to 7 days
24 hrs
48 hrs
Up to 7 days
24 hrs
48 hrs
Damage
reduction
(%)
20
80
90
10
40
45
10
30
Up to 7 days
70
Fisheries
24 hrs
48 hrs
Up to 7 days
30
40
70
Open sea
fishing
School or
office
24 hrs
48 hrs
24 hrs
48 hrs
Up to 7 days
10
15
5
10
15
Livestock
Agriculture
Actions taken to reduce damages
Removal of some household items
Removal of additional possessions
Removal of all possible possessions including stored crops
Poultry moved to safety
Poultry, farm animals moved to safety
Poultry, farm animals, forages, straw moved to safety
Agricultural implements and equipment removed
Nurseries, seed beds saved, 50% of crop harvested, agricultural
implements and equipment removed
Nurseries, seed beds saved, fruit trees harvested, 100% of crop
harvested, agricultural implements and equipment removed
Some fish, shrimps, prawns harvested
Some fish, shrimps, prawns harvested, nets erected
All fish, shrimps, prawns harvested, nets erected, equipment
removed
Fishing net, boat damage avoided
Fishing nets removed, boat damage avoided
Money, some office equipment saved
Money, most office equipment saved
Money, all office equipment, including furniture protected
3) In cases where available, the same percentage (as above) of the relief or compensation paid
for direct damages is also used as avoidable damage.
4) In cases where crop adjustment is predicated by the forecast information, and data is
available, input costs are used as indication of direct benefits or savings that could be
accrued due to forecast information.
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Background Paper on Assessment of the Economics of Early Warning Systems for Disaster Risk Reduction
5) Damage data, in some cases, is also extrapolated to the national level based on available data
in some representative sites, e.g. to five districts of Sri Lanka based on data from two
districts.
The case study of Cyclone Sidr, November 2007, in Bangladesh demonstrates the ideal level of
detail in cost-benefit calculations possible due to data availability. Other country case studies,
while adopting this methodology, are not as comprehensive due to data limitations. The Sidr
case study is presented as the first case study so that the reader is familiar with this
methodology, though it could also have been placed with the other Bangladesh case study.
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Background Paper on Assessment of the Economics of Early Warning Systems for Disaster Risk Reduction
2. Case Studies on Cost-Benefits of EWS
Case studies are drawn, applying this methodology, to illustrate the benefits of EWS considering
investments with respect to economy of scale, enhancing basic services, enhancing efficiency of
EWS through institutional and community involvement, and incorporating emerging
technologies, as outlined below.

Economy of Scale: What is the economy of scale, i.e., the threshold at which an early
warning system can be justified as economical, with benefits outweighing the initial
establishment and subsequent operational costs? Further, how much would such
threshold be lowered by integrating more common, but low-impact events within such an
early warning system?

Benefits of enhancing basic meteorological services: Most national meteorological and
hydrological services (NMHSs) have the infrastructure and technical and human
resources to provide basic or first order services to stakeholders. These services are
appreciated by stakeholders and, hence, supported by national budgets. Some additional
marginal investments could enable NMHSs to provide special (or value-added) services,
such as long-lead forecasts, location-specific forecasts, or inputs for detailed potential
impact assessments, resulting in greater benefits. Would the benefits be sufficient to
convince national governments to provide these additional budgets to NMHSs?

Institutional and community involvement: While scientific and technical investment is
vital, marginal investment on ensuring institutional and community involvement in early
warning will go a long way in ensuring further saving of lives and property, and thus in
economic benefits. While there is no doubt that this societal investment has direct
economic benefits, the linkages can be detailed and the tangible benefits elaborated
further.

Emerging and new technologies: Even in relatively advanced systems, incorporation of
emerging technologies, with minimal investment that enables systems to use the latest
advances in science, can result in maximizing benefits manifold. What are the new
technologies and what are the benefits that can accrue to society due to them?
However, it is important to note that established institutional structures and empowered
communities are essential pre-requisites in order to derive the full benefits of EWS, as illustrated
in Box 2 below.
Box 2: Benefits of fostering community and institutional involvement
While new technology is being developed and applied (at a cost) to improve warnings, simultaneous efforts also
have to focus on how to make the system and its warnings more relevant to users, so that the warning is more
useful, effective and applicable. The efficacy of warnings could be increased only if the system also has the
capacity to influence response at institutional and community levels. Otherwise, an early warning, despite its long
lead time or high accuracy, will still not lead to saving of lives or property, as illustrated by the severe topical
cyclone Nargis which, despite being forecast several days ahead, killed over 10,000 people in Myanmar.
System efficiency could be defined as eff = Frw Fw Fc (where eff = efficiency of warning; Frw = fraction of the
public that receives a warning; Fw = fraction of the public willing to respond; Fc = fraction of public that knows how
to respond effectively and is capable of responding (or has someone to help)).
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Background Paper on Assessment of the Economics of Early Warning Systems for Disaster Risk Reduction
Thus an early warning system has to also involve the downstream, i.e., communities at risk who would have to
receive and respond appropriately – leading to the ‘end-to-end’ or ‘integrated’ early warning system.
In parts of Cambodia, between October and early December, three coastal communes, Tuek La’k, Tuek Thla and
Samekki in Prey Nup district, Sihanoukville province, experience strong dry winds (Kachol Kodeauk in Khmer),
which cause severe damage to houses and harvestable crops. Damages caused by strong winds are also reported in
many other provinces during the same period each year. Though there is no proper record of the strong winds
occurring every year, according to the communities in Tuek La’k village, strong winds experienced every two or
three years inflict serious damages.
In the past, villagers, based on their indigenous knowledge, were able to predict the strong winds two days in
advance. Villagers were able to hear a loud roaring noise from Kam Chay Mountain due to the wind striking the
hill sides. But these days, due to deforestation along the windward side of the mountain range, they are unable to
hear any sound and they have very little time to react. Studies show that this phenomenon is linked to the reversal
of trade winds from east to west during November, which is part of a large-scale phenomenon. It is, however,
possible to provide such information in advance so that the communities can take necessary measures to reduce
damages.
It is worth noting that these communities have evolved damage reduction strategies for the two days lead time
available. They work collectively to use a light log as a roller to flatten crops and reduce the impact of the strong
dry wind. Such efforts actually increase the value of the early warning and the benefits derived from the system.
This section illustrates the benefits of EWS through several case studies. For convenience,
hazards are grouped into two categories:
Category 1: Weather- & climate-associated. This category includes recurrent events, such
as floods, flash floods, cyclones/ typhoons, and landslides which have lesser impact in
comparison with tsunami, as well as extreme variants of the same which result in very high
impacts. Several country case studies are presented. For purposes of this paper, countries are
classified into four groups, as below:
Group 1: Countries with basic level of forecasting and warning services: Lao PDR,
Myanmar, Cambodia, East Timor, Afghanistan, Comoros, Seychelles, Yemen,
Madagascar, Bhutan, Nepal, Sri Lanka
Group 2: Countries with existing capabilities, but are not entirely operationalized due to
inadequate technical or human resources: Bangladesh, Mongolia, Mozambique,
Pakistan, the Philippines and Vietnam
Group 3: Countries with operational capabilities, but having some gaps relating mostly to
generation of location-specific products matching user requirements and a
disconnect between downscaling, interpretation, translation, and communication
of specific forecast information: Thailand, China, India
Group 4: Countries with reliable seasonal forecasts: Indonesia and the Philippines;
additional cases from Sri Lanka and India are included to demonstrate the
potential benefits of such forecasts, though it is not operational yet
Category 2: Geological hazards – Tsunami. One regional case study is presented.
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Background Paper on Assessment of the Economics of Early Warning Systems for Disaster Risk Reduction
Case Study 1: Sidr Cyclone, November 2007, Bangladesh
On 15 November 2007, Cyclone Sidr struck the coast of Bangladesh with winds up to 240
kilometers per hour, and moved inland, destroying infrastructure, causing numerous deaths,
disrupting economic activities, and affecting social conditions, especially in the poorer areas of
the country. The category 4 storm was accompanied by tidal waves of up to five meters high
and surges of up to 6 meters in some areas, breaching coastal and river embankments, flooding
low-lying areas and causing extensive physical destruction. High winds and floods also caused
damage to housing, roads, bridges and other infrastructure. Electricity and communication were
knocked down; roads and waterways became impassable. Drinking water was contaminated by
debris. Many fresh water sources were inundated with saline water from tidal surges. Sanitation
infrastructure was destroyed.
Damage and loss from Cyclone Sidr was concentrated on the southwest coast of Bangladesh.
Four of Bangladesh’s 30 districts were classified as “severely affected”, and a further eight were
classified as “moderately affected”. Of the 2.3 million households affected to some degree by
the effects of Cyclone Sidr, about one million were seriously affected. The number of deaths
caused by Sidr is estimated at 3,406, with 1,001 still missing, and over 55,000 people sustained
physical injuries. Improved disaster prevention measures, including an improved forecasting
and warning system, coastal afforestation projects, cyclone shelters, and embankments are
credited with the lower casualty rates than expected, given the severity of the storm.
Table 5: Summary of damage and losses – Cyclone Sidr
Cyclone Sidr in Bangladesh: Damage, Loss and Needs Assessment for Disaster Recovery and Reconstruction
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Background Paper on Assessment of the Economics of Early Warning Systems for Disaster Risk Reduction
Possible early warning
An advanced numerical weather prediction (NWP) technique, such as Weather Research
Forecasting (WRF), in conjunction with a high performance computing system and trained
human resource, would be in a position to provide enhanced lead times of both landfall point
and cyclone track beyond 5 days. Also, associated hazard parameters, such heavy rainfall and
strong wind over specific locations at a very high resolution (up to 3 km or even 1 km grid), may
be quantified. A system of this nature is already operational in the Regional Integrated MultiHazard Early Warning System (RIMES), which forms the basis for cost calculations for the
scientific component of this paper.
Due to the probabilistic nature of forecasts generated by using NWP techniques, additional
investment at intermediary user institutions, such as the Department of Agriculture Extension,
and Disaster Management Bureau are required to enable them to translate, interpret, and
communicate forecast information to users at the district (zilla) level, and to prepare appropriate
response options at local and community levels. This investment is categorized under
institutional and community component, and is calculated on the basis of the Flood Forecasting
and Warning Centre’s (FFWC) ongoing CFAB project.
Cost-benefit analysis
The cost-benefit model was developed using excellent and readily available data from the study
entitled Cyclone Sidr in Bangladesh: Damage, Loss and Needs Assessment for Disaster
Recovery and Reconstruction, and based on field experiences mentioned in the methodology to
analyze the costs and benefits over the lifetime of the EWS project (assumed 10 years).
Table 6 lists the EWS costs calculated under one-off (fixed) costs, and variable costs that occur
on a regular basis. Table 7 lists the qualitative impacts, i.e., the current scenario without this
additional EWS when compared to the scenario with the additional EWS, to describe all changes
that would take place as a result of the EWS. Impacts were analyzed under natural, physical,
economic, human, and social categories. Table 8 lists the benefits assessed for quantifiable
areas and, for each quantifiable benefit, the calculated change in impact.
Table 6: EWS costs for Bangladesh Sidr Cyclone
Item
Scientific component2
EWS technology development costs
High performance computing system
Additional training for human
resources to generate forecast
information
Institutional component3
Capacity building of national and subnational (district) institutions for
translation, interpretation and
communication of probabilistic
forecast information
2
3
Fixed costs
(million USD)
Yearly variable costs
(million USD)
Other costs
(million USD)
1.0
1.0
0.1
0.10
0.01
-
-
0.20
-
Scientific component costs refer to input costs for technical institutions to generate forecast information
Institutional component costs refer to costs for training and other capacity development for institutions to be able to use forecast information
and facilitate use at lower levels
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Background Paper on Assessment of the Economics of Early Warning Systems for Disaster Risk Reduction
Community component4
Training of Trainers at local levels to
work with ground level users –
farmers, fishermen, small businesses,
households
Total (million USD)
-
0.10
2.1
0.41
-
-
EWS costs for 10 years
Fixed costs remain @ USD 2.1 million:
Variable costs @ 0.41 million per year for 10 years:
USD 2.1 million
USD 4.1 million
Total costs for 10 years
Total costs for 10 years (cyclone only) (C):
USD 6.2 million
USD 3.1 million
(This investment has multiple uses. In addition to cyclone forecast improvement, it can also be
used for heavy rainfall, thunderstorm and flash flood forecasting. Hence a proportion (50%) of
the total costs is considered.)
Table 7: Identifying EWS benefits for Bangladesh Sidr Cyclone
Type of
Impact
Natural
Physical &
Economic
Without EWS
Damage to coastal forests,
ecosystems
Housing damaged; household
possessions lost
Damage to coastal forests, ecosystems
Agriculture: crops damaged;
implements and equipment damaged
or lost
Agriculture: damage to crops avoided,
where applicable, by early harvesting;
agricultural implements and equipment
saved
Fishery: all fish, shrimps, prawns harvested;
nets erected; equipment removed (70%
reduction in damages)
Livestock: all poultry, farm animals,
forages, and straw moved to safety (45%
reduction in damage)
Offices and schools: cash saved; equipment
and furniture protected (15% reduction in
damages)
Many lives lost
Many injuries avoided
Many illnesses avoided as a result of
increased preparedness measures
Fishery: fish, shrimps lost; nets and
other fishing equipment damaged
Livestock: most poultry, farm
animals, forages, and straw damaged
or lost
Offices and schools: cash lost;
equipment and furniture damaged
Human
Social
4
With EWS
Several lives lost
Several injuries sustained
Several affected people exposed to
various illnesses as a result of
inadequate or no preparedness
Trauma, suffering among affected
and their relatives
Housing damage avoided in some cases
(damage due to fallen trees reduced in 10%
of partially damaged houses by maintenance
of trees), and many or most household
possessions saved depending on lead time
Reduced trauma and suffering among
affected and their relatives due to
anticipation and preparedness
Included in
analysis
No
Yes.
Household
possessions
taken as 5% of
housing
damages is
considered as
avoidable
Yes
Yes
Yes
Yes
No
No
No
No
Community component refers to the input costs at community level to enable communities to adopt forecast information, and respond
appropriately
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Background Paper on Assessment of the Economics of Early Warning Systems for Disaster Risk Reduction
Table 8: Quantifying EWS benefits for Bangladesh Sidr Cyclone
Impact
Housing
Magnitude without
EWS
957,110 houses partially
damaged
Household
possessions
Possessions in most
houses damaged are lost.
Total housing damage is
BDT 57.9 billion.
Possessions damaged is
5% of this amount.
Agriculture
Standing rice crop
damaged
2,105 ha of Boro rice seed
bed damaged
177,955 MT (in 19,464
ha) vegetables damaged
25,416 MT (in 3,614 ha)
betel leaves damaged
93,383 MT (in 5,676 ha)
banana damaged
24,488MT (in 1,322 ha)
papaya damaged
Fishery
Livestock
Schools and
offices
BDT 324.7 million worth
of fish, shrimp,
fingerlings washed away
BDT 130.29 million
worth of boats (1,855)
and fishing nets (1,721)
damaged
BDT 1.25 bi of damages
due to dead animals (cow,
buffalo, sheep, goat),
poultry (chicken, ducks),
and feed
BDT 16 mi of stationery,
learning materials, etc.
damaged
Magnitude with EWS
Damage to 95,711
houses by fallen trees
avoided
Possessions saved in
additional 10% of the
cases.
Standing rice crop
damaged
At least 50% Boro rice
seed bed (1,050 ha)
avoided by manually
collecting and
preventing exposure
Damage of at least
25%, i.e. 44,488 MT
(in 4,866 ha) avoided
by early harvesting
Damage of at least
10%, i.e., 2,541 MT (in
361 ha) avoided by
early harvesting
Damage of at least
10%, i.e., 9,338 MT (in
567 ha) avoided by
early harvesting
Damage of at least
10%, i.e., 2,448 MT (in
132 ha) avoided by
early harvesting
70% of damages could
have been avoided
Value
Repairs @ BDT
10,000
Total yearly benefit
(avoided cost)
BDT 957.11 million
(USD 13.84 million)
Total possessions
damaged is 5% of
BDT 57.9 billion
= BDT 2.895
billion Additional
10% saved with
EWS
-
BDT 2,895 million
(USD 41.87 million)
1 ha = BDT
44,000
BDT 46.31 million
(USD 0.67 million)
1 MT= BDT
12,000
BDT 533.86 million
(USD 7.72 million)
1 MT= BDT
25,000
BDT 63.54 million
(USD 0.92 million)
1 MT= BDT
15,000
BDT 140.07 million
(USD 2.03 million)
1 MT= BDT
10,000
BDT 24.49 million
(USD 0.35 million)
-
-
BDT 227.29 million
(USD 3.29 million)
15% of damages could
have been avoided
-
BDT 19.54 million
(USD 0.28 million)
45% of damages could
have been avoided
-
BDT 562.5 million (USD
8.14 million)
15% of damages could
have been avoided
-
BDT 2.4 million
(0.03 million USD)
Total
BDT 5,472.11 million
(USD 79.14 million)
Note: USD 1 = BDT 69.14
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Background Paper on Assessment of the Economics of Early Warning Systems for Disaster Risk Reduction
Total benefit considering probabilistic forecasting (90%): 79.14 x 0.8
USD 63.31 million
Cost-benefit analysis for 10 years
Total costs for 10 years (C):
Total benefits for 10 years, assuming 2 instances of
such damages over 10 years: 63.31 x 2
USD
Total benefit = 126.62
Total costs
3.10
40.85
3.10 million
USD 126.62 million
In other words, for every USD 1 invested in this EWS, there is a return of USD 40.85 in
benefits.
2.1
Group 1: Lao PDR, Myanmar, Cambodia, East Timor, Afghanistan, Comoros,
Seychelles, Yemen, Madagascar, Bhutan, Nepal, and Sri Lanka
Most of the least developed countries (and many developing countries) have NMHSs which can
provide only basic services of forecasting/ early warning. These services cannot help prevent
the severe recurrent losses witnessed. Hence, there is a need (and demand) for value-added
services which can help reduce the impacts and losses due to disasters. Value-added services
include increased lead time and more localized and relevant warning information. These valueadded services will almost always require some additional investment (usually marginal), but
will result in certain benefits including increased lead time to save lives, movable assets, and
securing, to some extent, even immovable assets.
In these countries, basic early warning services from NMHSs are already available, such as daily
forecast of weather parameters including temperature, cloud cover, wind, and qualitative rainfall
forecast over a broad area; outlook for three to five days based on other regional or global center
products; and seasonal outlooks, again, based on outputs from other centers. These basic
services are not adequate to reduce disaster losses, as even a cursory examination of the past 30
years’ data indicates.
These countries also have many other priorities such as economic development, building roads,
providing electricity, and bringing more facilities to the communities. Hence, meteorological
services rarely get the support they require to establish dense networks of observation systems,
purchase technology, such as Numerical Weather Prediction (NWP), or develop skilled human
resources.
Case Study 2: 2003 Floods, Sri Lanka
Floods in Sri Lanka occur from excessive monsoon rainfall during both the southwest monsoon
and the northeast monsoon seasons. Rivers along the western slopes of the hilly central region
suffer excessive flows that lead to inundation of the flood plains of Kalu Ganga and Kelani
Ganga. Major floods in the Kelani Ganga occur almost every 10 years, while minor floods
occur every year. Major floods in the past 50 years occurred in 1957, 1967, 1968, 1978, 1989,
1992 and 2003. Encroachment of floodplains, conversion of paddy fields that used to hold
floodwaters into commercial and residential areas, and inadequate drainage system have all
contributed to increased vulnerabilities to floods.
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Background Paper on Assessment of the Economics of Early Warning Systems for Disaster Risk Reduction
The existing system of meteorological and hydrological networks and forecasting was not able
to anticipate the factors which led to the May 2003 extreme floods:
 A cyclone (01-B) that was formed in the Bay of Bengal in the first week of May 2003
headed for the Indian Coromandel (East) coast. Though it was at least 700 km away from
Sri Lanka, it brought intense low-level westerlies over Sri Lanka.
 The southeastwardly track of the cyclone was stalled for a few days by anomalous north-
westerly geostrophic winds over South Asia, and induced high wind speeds in Sri Lanka.
The seasonal Inter Tropical Convergence Zone (ITCZ) clouds were over Sri Lanka.
 Orographic rainfall induced by these factors, from Adam’s Peak and Koggala mountains,
over Sri Lanka led to the deluge.
Figure 1: Flood affected areas – Sri Lanka, May 2003
The track of the cyclone was very far from Sri Lanka and, hence, no cyclone warnings were
issued. Further, no cyclones have made landfall in Sri Lanka in May in the last 100 years.
However, this flood, or at least the unprecedented heavy rainfall which led to the floods, could
have been predicted with high-resolution weather prediction models, such as the WRF with at
least 3 days of lead time.
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Background Paper on Assessment of the Economics of Early Warning Systems for Disaster Risk Reduction
Table 9: EWS costs for Sri Lanka
Item
Fixed costs
(million USD)
Scientific component
Cluster computing system for NWP forecasting
Additional training for human resources to generate
forecast information
Institutional component
Capacity building of national and sub-national (district)
institutions for translation, interpretation and
communication of probabilistic forecast information
Community component
Training of Trainers at local levels to work with ground
level users: farmers, small businesses, households
Total (million USD)
Yearly variable costs
(million USD)
0.10
0.05
0.01
-
0.05
-
0.10
0.15
0.16
EWS costs for 10 years
Fixed costs remain @ USD 0.15 million:
Variable costs @ 0.16 million per year for 10 years:
USD 0.15 million
USD 1.60 million
Total costs for 10 years (C):
USD 1.75 million
Table 10: Avoidable damage in two of the five districts affected: 2003 floods, Sri Lanka
Damage without EWS
(million LKR)
Galle District
Household possessions
Horticulture crops
Paddy
Vegetable
School equipment
Banks equipment
Minor irrigation: anicuts, other
small structures only
Cooperatives
Livestock
Sub-total million LKR
Sub-total million USD
Matara District
Household possessions
Horticulture crops
Paddy
Vegetables
Other crops
School equipment
Banks equipment
Minor irrigation: anicuts, other
small structures only
Cooperatives
Livestock
Sub-total million LKR
Damage reduction with EWS
(%)
(million LKR)
13.96
2.55
32.00
3.96
6.63
5.08
5%
30%
5%
30%
10%
10%
0.698
0.765
1.600
1.188
0.663
0.508
1.54
9.70
94.00
169.42
1.69
50%
10%
40%
0.770
0.970
37.600
44.762
0.447
21.81
13.00
144.00
11.00
3.74
5%
30%
5%
30%
30%
15%
15%
1.091
3.900
7.200
3.300
1.122
0.000
0.000
4.50
28.00
5.07
231.12
50%
10%
40%
2.250
2.800
2.028
23.691
-
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Background Paper on Assessment of the Economics of Early Warning Systems for Disaster Risk Reduction
Sub-total million USD
Total million USD
2.31
4.00
0.236
0.683
Note: USD 1 = LKR 100.25
Sources: Assistant Agricultural Directors Office – Galle, Department of Animal Production and Health (Southern), Department of Agrarian
Services, Planning Department, Southern Provincial Cooperative Ministry
The above table lists only those items which could have been easily saved by taking appropriate
response measures in Galle & Matara districts, and could be treated as a conservative estimate.
Total avoidable damage cost for the 5 districts affected, assuming
at the same average rate as for the two districts: (0.683/2) x 5:
USD 1.708 million
Benefits considering probabilistic forecasting: 1.708 x 0.8:
USD 1.366 million
Table 11: Estimated avoidable damage from floods in Sri Lanka, last 3 decades
Type of floods
Extreme floods
(once in 50 years)
Major floods
(once in 10 years)
Yearly floods
Severity
No. of events
(last 3 decades)
0.6
Estimated avoidable
damage cost
(million USD)
0.6 x 1 x 1.708 = 1.025
25% of 2003 floods
3
3 x 0.25 x 1.708 = 1.281
5% of 2003 floods
30
30 x 0.05 x 1.708 = 2.562
Same as in 2003
Total avoidable damages, last 30 years (million USD)
4.868
Thus the total avoidable flood damage costs in the last 3 decades could have been USD 4.868
million, just by appropriate response actions on receipt of increased lead-time (3 to 5 days) early
warning.
Total benefits for 10 years: (4.868/ 30) x 10
USD 1.623 million
Cost-benefit analysis for 10 years
Total costs for 10 years (C):
Total benefits for 10 years:
USD 1.75 million
USD 1.623 million
Total benefit = 1.623
Total costs
1.75
0.927
In other words, for every USD 1 invested in this EWS, there is a return of only USD 0.927 in
benefits, i.e. the costs outweigh the benefits, since the significantly damaging flooding is not
very frequent. In such a case, it makes better sense for such countries to join a collective
(regional) system such as RIMES, and benefit from the economies of scale (refer to case study
on RIMES).
15
Background Paper on Assessment of the Economics of Early Warning Systems for Disaster Risk Reduction
2.2
Group 2: Bangladesh, Mongolia, Mozambique, Pakistan, the Philippines and
Vietnam
The NMHSs in this set of countries have some capabilities, but these are not entirely
operationalized due to inadequate technical or human resources.
In Bangladesh, an investment of about USD 1 million for developing and applying new
technology to anticipate monsoon flooding has resulted in a probabilistic forecast with lead time
of up to 10 days, which is unprecedented in the region. There is some additional investment
required for capacity building and creating awareness to derive full benefits given the
probabilistic nature of forecasting. However, even without it, the system has already
demonstrated its efficacy in the 2007 floods (refer to case study on Bangladesh 2007 floodsCFAB).
This system could be easily replicated in India and in the Mekong River countries, resulting in
enormous benefits and reduction of losses and damages due to the recurrent monsoon flooding.
Case Study 3: Bangladesh Floods
Floods in Bangladesh are a regular occurrence and may be classified into early floods, late
floods, normal floods and high floods, based on occurrence and magnitude.
120000
3.5
3
2.5
2
1.5
1
0.5
0
Area (sq.km)
100000
Million
Tonnes
80000
60000
40000
20000
1953
1958
1963
1968
1973
1978
1983
1988
1993
1998
2003
0
Figure 2: Historical flood event: extent and crop damage
The return period of floods may be tabulated as under, with a flood of 50 year return period
being much more severe than that of 20 years, which in turn is many times more severe than that
with 5 year return period.
Table 12: Return period of floods
Return Period (years)
Flooded Areas (%)
2
5
10
20
50
100
500
Mean
20
30
37
43
52
60
70
22
Source: Bangladesh National Water Management Plan, 2000, Table 9.1
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Background Paper on Assessment of the Economics of Early Warning Systems for Disaster Risk Reduction
Table 13 shows the major floods affecting Bangladesh in the past 5 decades. Figures 3 and 4
below illustrate the sharp decrease in the areas under production for major crops and the cereal
production corresponding with the 1988 and 1998 floods. The same could also be observed in
all the other major flood events.
Table 13: Major floods affecting Bangladesh in the last five decades
Year
Area affected
sq km
36,800
50,500
52,600
57,300
89,970
100,250
55,000
1954
1955
1974
1987
1988
1998
2004
(%)
25
34
36
39
61
68
38
Area under Production : Major Crops
6.00
Aus
B.Aman
Wheat
5.00
1998
T.ama
Boro
1988
3.00
Irrigation
infrastructure
2.00
1.00
Figure 3: Area under production: major crops
17
2000-2001
1998-1999
1996-1997
1994-1995
1992-1993
1990-1991
1988-1989
1986-1987
1984-1985
1982-1983
1980-1981
1978-1979
1976-1977
1974-1975
0.00
1972-1973
Million ha
4.00
Cereal production in Bangladesh (1972 – 2001)
Background Paper on Assessment of the Economics of Early Warning Systems for Disaster Risk Reduction
14.0
Aus
B.Aman
Wheat
12.0
T.Aman
Boro
1998
Million tonnes
10.0
1988
8.0
6.0
4.0
2.0
2000-2001
1998-1999
1996-1997
1994-1995
1992-1993
1990-1991
1988-1989
1986-1987
1984-1985
1982-1983
1980-1981
1978-1979
1976-1977
1974-1975
1972-1973
0.0
Figure 4: Cereal production (1972-2001)
The floods of August 2007 is classified as a medium flood, yet still resulted in significant
damages and losses totaling USD 1.07 billion. The current floods of August - September 2008
are low- floods, occurring annually.
Table 14: Quantifying benefits: July- August 2007 floods
No.
Sector
Damage elements
Damage cost
(million BDT)
Food and Agriculture
1 Agriculture
Crop (Transplanting Aman
(crop)
seedlings, jute, vegetables, T
Aman, B. Aman and other
crops)
2 Livestock
Cattle, buffaloes, sheep,
goats, chicken, ducks,
forages and straw
3 Fisheries
Fish fingerlings, freshwater
fishes, shrimps/prawns,
pond embankments
4 Deep and
Pump house and Deep tubeshallow tube well machineries and
well
irrigation canals
5 Seeds &
Pump house, underground
irrigation
pipe line, water pump,
control structure and
connecting roads
6 Forest
Forests, nursery, roads and
buildings in forests
Total damage cost - Food & Agriculture
(million BDT)
Remarks
42,165.44
30
12,649.63
608.55
70
425.99
1,964.95
50
982.48
509.40
-
-
Unavoidable
10.00
-
-
Unavoidable
37.80
5
1.89
45,296.14
18
Avoidable
Damage
(%) (million BDT)
14,059.99
For crops at harvest stage
only - 30%
For livestock, forages/ straw
moved to safe
ground/shelters only - 70%
For fish, shrimps/ prawns
harvested only - 50%
For nurseries only - 5%
Avoidable damage (million
BDT) (31% of actual
damage in sector)
Background Paper on Assessment of the Economics of Early Warning Systems for Disaster Risk Reduction
Infrastructure-Health
7 Tube wells (TW) and platforms
8 Health infrastructure (Health Centers,
clinics, medicine and other items damages)
9 Health sub-centers, community clinics
Total damage cost – Infrastructure-Health
(million BDT)
Transport, Communication and Public Works
10 Roads, bridges and culverts and other
infrastructures, approach roads, drain, UP
building, growth centre, embankments
11 Flood shelters
12 Highway, roads, bridges and other
infrastructures
13 Embankment, bridge culvert, roads and
building, sluice gate, regulator, inlet, outlet
etc.
14 Handloom
15 Building, roads, culverts and drain
16 Infrastructure (cabinet, telephone pole,
cables, offices)
17 Infrastructure (meters, poles, and
transmitter)
18 Electricity-related infrastructure
19 Disaster shelters
20 Bridges/ culverts
21 Railway infrastructure (rail line and
bridges)
22 Infrastructure like pontoon
Total damage cost – Transport, Communication
and Public Works
Education
23 Primary School buildings and other related
offices/infrastructures books and furniture
24
Schools, colleges and Madrashas buildings
and other related offices/ infrastructure,
books, laboratory and furniture
Total damage cost – Education
(million BDT)
137.22
344.40
Unavoidable
Unavoidable
34.42
516.04
Unavoidable
11,425.35
Unavoidable
45.00
6,904.90
Unavoidable
Unavoidable
5,549.74
Unavoidable
282.26
17.00
6.15
Unavoidable
Unavoidable
Unavoidable
94.05
Unavoidable
29.13
73.00
13.20
370.97
Unavoidable
Unavoidable
Unavoidable
Unavoidable
367.38
25,178.13
Unavoidable
1,114.20
5
430.23
5
1,544.43
Total damage cost (million BDT)
Total USD (1 USD=68 BDT) million
72,534.74
1,066.69
55.71
For moveable assets only equipment, books, light
furniture- 5%
21.51 For moveable assets only laboratory equipment,
books, light furniture- 5%
77.22 Avoidable damage (million
BDT) (5% of damage in
sector)
14,137.21 Avoidable (approx. 20%)
207.90
Note: USD 1 = BDT 68
Source: Consolidated Damage and Loss Assessment, Lessons Learnt from the Flood 2007 and Future Action Plan, Government of the People’s
Republic of Bangladesh
Total benefit considering probabilistic forecasting (90%): 207.90 x 0.8
USD 166.32 million
In a thirty-year period, say the last three decades, the occurrence of floods (as per severity)
would be as follows:
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Background Paper on Assessment of the Economics of Early Warning Systems for Disaster Risk Reduction
Table 15: Estimated avoidable damage for floods in Bangladesh, last 3 decades
Type of floods
Severity
(compared to 2007
floods)
25% of 2007 floods
Annual with spatial variations
(2008-type)
5-year
Same as 2007
(2007 type)
10-year
Twice as severe
(2004 type)
30-year
Four times as severe
(1987 type)
50-year
Eight times as severe
(1998 type)
Total avoidable damages, last 30 years (million USD)
No. of events
(last 3 decades)
20
Estimated avoidable damage
cost
(million USD)
20 x 0.25 x 207.9 = 1,039.5
6
6 x 1 x 207.9 = 1,247.4
3
3 x 2 x 207.9 = 1,247.4
1
1 x 4 x 207.9 = 831.6
0.5
0.5 x 8 x 207.9 = 831.6
5,197.5
Cost-benefit analysis for 10 years
Total costs for 10 years (C) (from Case Study 1):
USD
3.1 million
Total benefits for 10 years: (5,197.5/ 30) x 10
USD 1,732.5 million
Total Benefit = 1,732.5
Total Costs
3.1
558.87
In other words, for every USD 1 invested in this EWS, there is a return of USD 558.87 in
benefits.
To be able to extract such benefits on a national scale, some investment would be needed at
national, provincial, upazilla, district and union levels on building capacity of institutions,
systems and user communities to utilize warning lead time for saving assets. Tables 19, 20 and
21 show the actions for utilizing short- and long-range forecast information. Additional
infrastructure, including shelters and safe sites to store assets, would also need to be constructed,
which would be a one-time investment, with some maintenance costs only.
CFAB technology, which has been successfully tested and operationalized in five pilot areas in
Bangladesh, can be expanded to provide 1 to 10 days advance warning to the entire country.
The investment required may be less than even 1% of the total avoidable damages, and would be
for local level activities, such as establishing correlations between danger levels and possible
inundation, communication infrastructure, and capacity building for communities and local
institutions to enable them to use such probabilistic forecasts.
Box 3: Climate forecast applications in Bangladesh, food forecasting technology
Large-scale floods occur through excessive discharge into the Bangladesh delta, retardation of outflow into the
Bay of Bengal by high sea levels, and by excessive precipitation over the delta. Of these factors, the major source
of floods is through discharge from the Ganges and Brahmaputra Rivers. Thus, forecasting of river discharge into
Bangladesh beyond 1-2 days means forecasting of rainfall over the catchment basins, the flow of water through
the Ganges and Brahmaputra, and the variability of sea level in the Bay of Bengal.
The catchment basins of the Ganges and Brahmaputra are extremely large, extending over 1,073 and 589 km 2,
with annual discharges of 490 and 630 km3/year, respectively. Furthermore, the basins extend over a number of
countries – a fact that complicates the collection of data necessary for forecasting.
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Background Paper on Assessment of the Economics of Early Warning Systems for Disaster Risk Reduction
To address the problem of catchment precipitation forecasting, a nest of physical models are developed that
depend on satellite data, forecasts from operational centers (e.g. the European Center for Medium-range Weather
Forecasting (ECMWF)), and statistical post-processing.
Through the CFAB project, forecast of rainfall and precipitation in probabilistic form is updated every day, and
probability of flood levels being breached at the entry point of the Ganges & Brahmaputra is provided, which is
useful for emergency planning and selective planting or harvesting to reduce potential crop losses at the beginning
or end of the cropping cycle. It is also incorporated to drive the Bangladesh routing model (MIKE), resulting in
extending the 2-3 day Bangladesh operational forecasts to 12-13 days.
The CFAB forecasting scheme is outlined below:
 The short-term prediction scheme depends on the ECMWF daily ensemble forecasts of rainfall and
thermodynamical variables over the Indian Ocean, Asia and the Western Pacific Ocean.
 Forecasts are corrected statistically to reduce systematic error.
 Rainfall is introduced into a suite of hydrological models, which allow calculation of Ganges &
Brahmaputra discharge into Bangladesh.
 Statistical probabilities are then generated.
 The approach comprises key steps of initial inputs, statistical rendering, hydrological modeling, generation
of probabilistic forecasts and inputs from users for application. This ensures that multi-model Ganges and
Brahmaputra discharge forecasts for 1 to 10 days are arrived at.
The CFAB has resulted in the following:
 Flood Forecasting and Warning Centre (FFWC) of the Ministry of Water Resources of Bangladesh is able
to increase the lead time from 72 hrs to 10 days.
 The model performs consistently well and correctly predicted the 2007 and 2008 floods. The flood
forecasts provide onset of flood, duration and dates when floods recede.
 1-10 days long-lead forecasts provide enough lead time to interpret, translate and communicate forecast
information to users through established communication channels.
 The pilot testing of this long-lead forecast information at high-risk locations reveals tangible benefits to
communities at-risk.
Forecast updates from 72 hrs to 10 days
Traditional 3 days forecasts
Forecast extended to 10 days
Figure 5: Improvement in forecast lead time due to CFAB technology, Bangladesh
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Background Paper on Assessment of the Economics of Early Warning Systems for Disaster Risk Reduction
Table 16: Potential impacts in the food and agriculture sector due to various floods, and alternative
management plans in case of early warning
Disaster
Early
flood
Crop
Stages
T.Aman
Seedling &
vegetative
stage
T.Aus
Harvesting
Jute
Near
maturity
Vegetables Harvesting
High
flood
Kharif I
Jun-Jul
Jun-Jul
Jun-Jul
Impacts
Damage seedlings
Damage early-planted
T.Aman delay planting,
Soil erosion
Damage to matured
crop
Yield loss
Poor quality
Damage; yield loss;
Poor quality
Total crop damage
Time of
forecast
Early June
Alternative management
plans
Delayed seedling raising,
Gap-filling, skipping early
fertilizer application
Early June
Advance harvest
May end
Early harvest
Mar-Apr
Pot culture (homestead)
Use resistant variety
Late varieties
Direct seeding
Late planting
Use of late varieties
Direct seeding
Early winter vegetables
Mustard or pulses
Pre-flood harvesting,
Net fencing/bana,
Fingerlings stocked in
flood-free pond
High stock density
T. Aman
Tillering
Kharif II
Jul-Aug
T. Aman
Booting
Kharif II
Aug-Sep
Yield loss and crop
damage
Early July
Jun-Aug
Inundation of fish
farms;
Damage to pond
embankments;
Infestation of diseases;
Loss of standing crops
Apr-May
Late
flood
Flood
Season/
month
Kharif II
Jun-Jul
Nursery
table fish
Brood fish
-
Early June
Table 17: Actions for utilizing improved flood forecast information
For short-range forecast (from 5 days to 2 weeks)
For long-lead forecast (1-2 months)
1. Acceleration of crop harvesting when threatened by floods
(example: late sown Boro rice crop in the first week of June
and Aus paddy crop in the first week of July)
2. Rescheduling and postponement of broadcast of seeds in the
case of deepwater B. Aman / transplanting of Aman crops.
3. Undertake mid-season corrections and crop life saving
measures wherever possible.
4. Raise store houses for storing grains above the maximum
flood level.
5. Protect farm assets like livestock and essential farm
implements. Other strategies: reduce harvest/ storage losses,
and protect young seedlings/ crops from flood to enable
farmers to preserve investments and retain capacity to
undertake next year’s sowing
6. Short forecast may not be of any value when crops are at
vegetative stage or milking stage and are too premature to
harvest
1. Adopt a flood escaping cropping strategy of early
Aus paddy (planted in February and harvested in
June) and late transplanted Aman paddy (to be
planted before mid-September) for flood-prone
areas.
2. Pre-requisites for flood escaping cropping
strategy:
– Supplementary irrigation facilities to start
operation during pre-monsoon and protect the
crops during dry season
– Availability of short duration varieties of
crops
– Extension and market support
3. Opportunities to procure and use shallow water
pumps to tap ground water source.
4. Short duration varieties that have been developed
through research efforts that could be used for
contingency crop planning
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Background Paper on Assessment of the Economics of Early Warning Systems for Disaster Risk Reduction
Table 18: Agricultural risk management options in case of 10 to 15 days early warning
Crop
Agricultural
practices
May 1 –
Jun 15
Jun 15 –
Jul 30
Planting
Aus
Harvest
Harvest
Aug 15 –
Oct 31
Transplanting
Jul 1 –
Aug 15
Sep 1 –
Sep 20
B.
Aman
T.
Aman
Decision
window
(time)
Fertilizer
application
(split)
Sowing/ seed
bed
Nov 15 Dec 31
Boro
Harvesting
Apr 1 –
May 15
Type of disaster risk and
impacts
Early flooding causes
submergence
High flood causes heavy
damage to crops and
submergence
Late season flood causes
submergence, low quality
grains and loss of
investments
High floods affect early
seedling
Inundation reduces the
efficiency of applied
fertilizers
Inadequate rainfall during
Nov/Dec affects
establishment
Flooding in low lands affects
establishment
Flash floods or hail storms
Information
requirement
for
preparedness
Chance of
early flooding
Chance of high
floods/
warning
Chance of high
floods
Time Management plan to
lag
reduce risk
(days)
10
10
10
Chance of high
floods
Chance of late
flood
15
Chance of
rainfall
15
Chance of late
flooding
Flash floods/
hail storms
15
15
10
Protection from
floods
Advance harvest
after physiological
maturity
Advance harvest
Planning for extra
seedlings
Skipping first split
application
Early sowing of boro
coinciding with
rainfall during
October
Delayed sowing in
late December
Advanced harvest to
reduce yield loss
Box 4: Institutional responses to the July 2007 flood forecasts in Bangladesh
Based on CFAB forecasts, FFWC issued the forecast of an impending disastrous flood from the Brahmaputra
River 10 days before the water levels crossed the danger-level. Following are the institutional responses to the 10day flood forecast:
 Upazilla level organizations, in partnership with non-government organizations (NGOs), communicated
the forecast to communities in the pilot sites
 Local project partners used community vulnerability maps to assess the risk of flooding
 Local NGOs and implementing partners in Lalmunihat and Gaibandha prepared evacuation and response
plans to protect lives and livelihoods
 Union Parishad chairmen in Gaichuri (Sirajganj) and Fulchuri (Gaibandha) prepared evacuation plans in
partnership with community-based organizations (CBOs)
 District level relief and emergency organizations planned to mobilize resources for relief activities
 Local NGOs and Department of Agriculture Extension (DAE) prepared work plan for relief and
rehabilitation activities
 Local NGOs, government organizations, and CBOs mobilized mechanized and manual boats to rescue
people and transport livestock from char areas
Following are the lowland community-level responses:
 Stored food and safe drinking water to last for 10 days, knowing that relief operations will start only 7 days
after the initial flooding
 Secured cattle, poultry and homestead vegetables, and protected fishery by putting nets in advance
 Secured cooking stove, small vessels, firewood and dry animal fodder, which were then transported to
highlands and embankments
 Identified high grounds with adequate communication and sanitation facilities for evacuation
 Harvested jute crop
 Planned alternative livelihood options immediately after flooding (e.g. small-scale fishing, boat making,
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Background Paper on Assessment of the Economics of Early Warning Systems for Disaster Risk Reduction
seedling raising, jute retting)
Highland community responses included:







Abandoned plans to transplant T. Aman rice, anticipating floods in Mohipur (Gangachara upazilla)
Secured traditional seedlings for double planting of rice after the first floods
Protected homestead vegetables by creating adequate drainage facilities
Reserved seeds of flood-tolerant crops for subsequent seasons
Planned to grow seedlings in highlands in Rajpur union (Lalmunirhat district)
Planned alternative off-farm employment during floods
Early harvesting of B. Aman rice and jute, anticipating floods in Gaibandha and Sirajganj, respectively
 Protected livestock in highlands with additional dry fodder
2.3
Group 3: Thailand, China, India
Past 30 years of disaster data would indicate the enormous cumulative loss accrued, which has
probably precipitated several initiatives to build up more robust observation networks and
technical capacity to forecast events with lead time of up to 3 days. However, as data from the
last 5 years bear evidence, there are still some gaps which relate mostly to generation of
location-specific products matching user requirements, and the disconnect between downscaling,
interpretation, translation and communication of such specific forecast information.
Human resources are also available, but these countries need improvement in downscaling, and
in relating operational forecasts to disaster managers, and further for disaster managers to relate
to users. Investment of about USD 1 million per country will assist in building up this system.
In case of Thailand, despite all the investment on equipment, observation network, etc., a
marginal investment on additional skills such as data assimilation would enable fuller utilization
of existing technologies, resulting in more accurate forecasting and, thus, reduction of losses by
a certain percentage. Further, these countries could get an even greater benefit by investing in
promising, but untested, experimental technologies, as in the case of Bangladesh’s CFAB
technology.
Case Study 4: 2006 Floods (July – September) Thailand
During 2006, Thailand was badly affected nationwide by floods from several storms, most
particularly from severe Tropical Storm Xangsane (which turned into a tropical depression in the
country) and Tropical Storm Prapiroon. Out of 75 provinces, 46 were locally inundated.
By mid-October, Thailand’s Department of Disaster Prevention and Mitigation (DDPM)
reported that 47 people had been killed, two were missing and more than 2.4 million people
were affected to various degrees across the country. In 2006, rainfall intensity in May and
October were the highest in 30 years.
At the beginning of August, Tropical Storm Prapiroon passed over the South China Sea to the
northern part of Thailand and created heavy rainfall in the north, northern central region, the
north east and the east coast of Thailand. From 19 to 21 August 2006, the strong low pressure
that passed over the northern and northeastern part of Thailand produced intense rainfall, which
measured a maximum of 259 mm in Nan province and caused flash flooding of the Nan river.
Water levels rose very quickly and created floods of 2–3 m at Amphoe Tha Wang Pha on the
morning of 20 August, followed by 1–1.5 m floods at Amphoe Muang and Amphoe Phu Piang.
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Background Paper on Assessment of the Economics of Early Warning Systems for Disaster Risk Reduction
Between 27 August and 4 September, a strong low pressure passed over the northern part of the
country and brought heavy rainfall that caused water-levels in rivers in the Ping, Kuang, Tha,
Yom and Wang river basins to rise very rapidly. Shortly after this (from 9 to 12 September and
from 18 to 23 September), another strong low pressure cell passed over the northern and north
eastern part of the country. This combined with the southwestern monsoon and low pressure in
the Southern China to become severe Tropical Storm Xangsane. This depression generated very
heavy rainfall in the southern parts of the northern provinces and the central part of the country,
bringing with it fast rising water levels and floods in many areas.
Incessant monsoonal storm rainfall, particularly during August and September, also caused flash
floods in Chiang Rai and Nan provinces, with three people killed or missing. The flooding in
Nan was reported to be worst in more than 40 years (Bangkok Post, 13/9/2006), reaching depths
of between 1.20 –1.80 meters. Flash floods in mid-August caused the flooding of 500 houses
and the inundation of 5,000 rai of farmland in Chiang Rai province alone. The loss of crops
and, therefore, income reportedly caused the temporary migration of many rural family members
to Bangkok to find work (Bangkok Post. 13/9/2006). Provincial public health authorities
reported that stagnant floodwaters were a constant threat to public health, leading to significant
outbreaks of conjunctivitis and leptospirosis (The Nation, 15/9/06).
Table 19: 2006 Thailand floods - summary of damages and losses
Description
Areas affected (number of
districts/villages)
Total population affected
No. of flood-related deaths
No. of people suffering from
flood-related diseases
Estimated number of houses and
property damaged
Assessed losses and damage (Oct 2006)
32 provinces (217 districts; 1,302 sub-districts; 7,372 villages)
2,212,413 people from 605,401 households
164 deaths (149 drowned; 10 electrocuted; 2 snake bite; 3 other)
591,968 people
54 houses totally damaged
9,137 houses partially damaged
5,241 roads and 326 bridges destroyed
3,007,431 rai or 481,189 hectares of farmland destroyed (6.25 rai = 1 hectare)
35,152 fish ponds and 1,132 schools/ temples destroyed
Cost of damages to government structures such as roads and bridges from
initial surveys estimated at US$9.94 million. This figure does not include
damages to farmland, houses and personal belongings.
Source: WHO SE Asia Regional Office Website
Box 5: Forecasting technology options & avoidable damages
There was a mild El Niño prevalent in 2006 and, as a result, very little rains were expected in Thailand at the end
of the monsoon season. Water was stored in all the dams, anticipating the El Niño impacts. However, the region
experienced successive typhoons. Typhoon occurrences in a mild El Niño year are unpredictable. Still, a 5- to 7day forecast system would benefit in this case, as the system could have monitored the series of typhoons coming.
Hence, with each occurrence, the water level could have been lowered (retaining a cushion) and released
gradually. Such a treatment would not eliminate the flooding entirely, but would result in lesser inundation.
Avoidable damage cost:
481,189 hectares of farmland were destroyed. Pro-active response measures undertaken such as early harvesting
of crops and produce could have resulted in savings in up to 25% of farmlands destroyed.
Area saved: 481,189 x 0.25
Avoidable damage cost: 120,297 x 6.25 x 250
120,297 hectares
Baht 188 million or USD 5.73 million
Notes: Each rai of farmland destroyed was compensated by 250 Baht; 6.25 rai = 1 hectare; USD 1 = Baht 32.8
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Background Paper on Assessment of the Economics of Early Warning Systems for Disaster Risk Reduction
Calculation for avoidable damage cost in Box 5 above is conservative, as it uses only the
compensation paid-out by the government for each rai of affected farmland. Pro-active
response measures may have resulted in saving of crops, which may have fetched more returns
per rai. Further, similar savings in the fisheries sector could be calculated, since a much higher
compensation amount (up to Baht 1,400 per rai) was provided for farm ponds.
Total benefit considering probabilistic forecasting (90%): 5.73 x 0.8:
USD 4.58 million
Total benefit for 10 years, assuming recurrence every 5 years: 4.58 x 2:
USD 9.16 million
Cost-benefit analysis for 10 years
Total costs for 10 years (same as Vietnam, Annex D):
Total benefits for 10 years:
USD 5.2 million
USD 9.16 million
Total benefit = 9.16
Total costs
5.2
1.76
In other words, for every USD 1 invested in this EWS, there is a return of USD 1.76 in
benefits.
2.4
Group 4: Indonesia and Philippines
With some investment, both Indonesia and Philippines have monthly and seasonal scale
forecasts in place, leading to some quantifiable benefits, as demonstrated in the case studies
below. Of course, the fact that there is a very strong co-relation between El Niño and
agricultural production is also very important. There are some similarities with Sri Lanka as
well, hence the country could also greatly benefit from seasonal forecasting. With intra-seasonal
monitoring of weather and climate parameters, along with other factors, as was done in India, it
is possible to provide more reliable seasonal forecasts.
Case Study 5: Climate Forecast Applications - Philippines (2002-2003 El Niño)
In collaboration with the Philippine Atmospheric, Geophysical and Astronomical Services
Administration (PAGASA), Provincial and Municipal Agriculture Office (PAO and MAO),
National Irrigation Administration (NIA), and the National Water Resources Board (NWRB),
ADPC implements the CFA program in Dumangas municipality (Iloilo province) and in Angat
Dam (Bulacan province). PAGASA provides user-demanded localized seasonal climate
forecasts at the demonstration sites, at least a month before the onset of the dry and wet seasons:
in Dumangas, through PAO, in a local climate forum, and in Angat Dam, through NWRB.
Information on season onset, rainfall characteristics, and length of dry spell in the wet season are
provided.
The Dumangas MAO and NIA field office, trained in risk and potential impact assessments, use
the information from PAGASA to assess the potential impact in the municipality for the
incoming season, prepare response options, and communicate these to farmers through
agricultural extension workers and farmers’ group representatives. Farmers were trained in
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Background Paper on Assessment of the Economics of Early Warning Systems for Disaster Risk Reduction
Climate Field Schools to understand forecasts and their constraints, crop management practices
appropriate for the climate outlook, and receive information on new cropping practices and
support mechanisms, such as establishing farmers’ cooperatives. Meeting once a week, the
Climate Field School is an important institutional mechanism that allows regular interaction
between PAGASA, PAO, MAO, NIA and farmers.
Fifty percent of Iloilo’s total agricultural area of 200,000 ha has assured irrigation through
irrigation schemes, so there is no impact of El Niño on over 100,000 ha. The other 100,000 ha
were potentially affected due to the 2002-2003 El Niño to varying extents, depending on
farmers’ decision-making:
1) Farmers not adopting forecast information for planting decisions: 25% of farmers who
planted rice and lost all their cultivation – their total loss was direct loss, i.e., cost of
inputs, plus the opportunity cost of profit from growing an alternate crop (@ PHP 8,000/
ha)
Input costs @ PHP 4,000/ ha for 25,000 ha:
Potential profit from alternate crop: 25,000 x 8,000
Total Loss:
PHP 100 million
PHP 200 million
PHP 300 million
(USD 7.5 million)
2) Tactful Farmers: 25% who grew alternate crops, such as maize, short-duration pulses,
and vegetables – their gain was the value of the maize (or any other alternate crops)
harvested
Production value of alternate crop: 25,000 x 8, 000
Gain:
PHP 200 million
PHP 200 million
(USD 5 million)
3) Risk averse/ passive farmers: 50% of the farmers who left their fields fallow – their loss
would be the opportunity cost of profit missed from alternate crop
Opportunity cost of profit missed from
alternate crop: 50,000 x 8,000
Loss:
The total value of forecast (if every farmer had used the
forecast for planting decision): 100,000 x 8,000
27
PHP 400 million
PHP 400 million
(10 mi USD)
PHP 800 million
(USD 20 million)
Background Paper on Assessment of the Economics of Early Warning Systems for Disaster Risk Reduction
Case Study 6: India Drought 2002
Indian southwest monsoon – general features
Around 74% of the annual rainfall in India is received during June-September. Performance of
the Indian economy is directly linked to the rainfall that occurs during these months. The
summer monsoon sets in on the first week of June in the southeastern corner of the country, and
gradually proceeds towards the northwestern region, covering the entire country by the second
week of July. The monsoon starts withdrawing from the first week of September from the west
and north, and withdraws from the entire country by mid-October. The northwest region is left
with less than a month of rainy season due to the late arrival and early cessation of the monsoon.
Conversely, Kerala and the northeastern parts of India are blessed with more than four months
of rainfall due to the early arrival and late withdrawal of the monsoon.
Onset and advance of southwest monsoon in 2002
In 2002, the onset of the southwest monsoon over Kerala was on 29 May, three days earlier than
its normal arrival of 1 June. By 12 June, the southwest monsoon covered peninsular India,
northeastern region and some parts of east central India as per its normal pattern. Thereafter, the
progress was halted for about a week. The monsoon strengthened along the west coast after the
first fortnight of June, in association with an off-shore trough. Subsequent low pressures
resulted in abundant rainfall, so that the cumulative rainfall for the country as a whole towards
the end of June was 4% above normal.
Hiatus in progression of the monsoon
The first half of July was characterized by a dry spell, which resulted in prolonged summer
conditions over north and northwest India. This pronounced ‘break’ in the southwest monsoon
season did not spare even the northeastern region where rainfall activity was also subdued.
Abnormal features in the advance of monsoon 2002
During 2002, there were 3 hiatus in the monsoon’s advance, which delayed the onset of the
monsoon over large parts of the country. It. was observed that the number of days the northern
limit of monsoon (NLM) stagnated was highest in 2002 (35 days) in three spells. It was also
found that during 2002, the monsoon took 72 days to cover the entire country after its onset over
Kerala (the longest in the past 40 years). The number of monsoon days was a record minimum
of 31 days, compared to 45 and 60 days during 1972 and 1987, respectively.
July dry spell characteristics
July is the rainiest month of the monsoon season, registering more than one third of the seasonal
rainfall, and is therefore critical to agricultural operations. Normally, 75% of districts receive
normal rainfall in July. However, in 2002, less than 25% of the districts received normal
rainfall. Rainfall deficiency of 51% in July 2002 on an all-India basis is the least minimum
rainfall since 1875. Only on 2 occasions in the past (1911 & 1918) was it over 45%, and both
the years ended up as major drought years.
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Background Paper on Assessment of the Economics of Early Warning Systems for Disaster Risk Reduction
Figure 6: June-July rainfall (1993-2002)
Monsoon surprises
The monsoon of 2002 ranks fifth, among the major droughts since 1877. However, the failure
of the monsoon was drastic and unprecedented in July 2002. Unlike other monsoon years when
2 or 3 months of the season add up to make a major drought, in 2002 it was only the dry spell of
July which brought on the drought, and its partial recovery in August could not offset the
prevalent drought conditions over India because of the very high rainfall deficiency in July. But
for scattered showers at few places, the monsoon did not set-in in most parts of northwest India,
until 26 August 2002. Hence, in the northwest region of India, two-thirds of the monsoon
season was without rainfall to sustain agriculture and fodder growth.
Drought impacts
A decline in the rainfall has an initial impact on agriculture, fodder availability, livestock and
dairy production, hydro-electric power generation, and availability of potable water supplies.
These impacts have cascading effects on industrial and service sectors, and the national
economy.
Cropped area left unsown during the kharif season due to drought was around 18.53 million ha.
One of the striking features is that even during the rabi season, when crops are grown under
irrigated conditions, the area left unsown was around 3 million ha. The monsoon 2002 not only
affected sowing operations during July, but also reduced water availability in reservoirs, which
could not support normal planting of crops during rabi.
Kharif grain production of 90.48 million tons for 2002-2003 was the lowest since 1987-1988
(when it touched 74.57 million tons), and is the best indicator of the devastation caused by poor
monsoon rains. During the rabi season, rice, wheat, coarse cereals, and pulses recorded negative
growth rates of 30.9%, 3.5%, 13.2%, and 10.2%, respectively over the corresponding season in
the previous year. Among the commercial crops, oilseeds and cotton production fell to 15-yearlows. Oilseeds production declined by 13.7%, while cotton production declined by 7.7%. The
estimated oilseeds production of 15.57 million tons was the lowest since the 1987-88 (pre29
Background Paper on Assessment of the Economics of Early Warning Systems for Disaster Risk Reduction
Technology Mission) crop of 12.65 million tons; while cotton production was 8.57 million
bales, compared to 6.38 million bales in 1987-88.
Decline in food grain production was most pronounced with coarse cereals, with an estimated
production of 25.08 million tons, the lowest since the 23.14 million tons level recorded in 19721973. Output of bajra, which is mainly cultivated in Rajasthan, plummeted to 4.19 million tons,
a little higher than the 3.27 million tons level of 1974-1975.
Case of Orissa: drought impacts
Table 20: Estimates of cumulative coverage under rice, Orissa 2002 (100,000 ha)
As on 31 July 2002
Actual
21.19
Broadcasting
Normal
20.88
Transplanting
9.29
1.25
8.04
30.17
22.44
7.73
Total
Deficit
-0.31
Source: Department of Agriculture, Government of Orissa
Table 21: Crop damage as per state report, Orissa 2002
Reason for Damage
Beushaning not undertaken
Area damaged
(100,000 ha)
19.22
Gajarudi
0.85
Damage after timely Beushaning
0.32
Transplanted crop damaged
0.34
Opportunity cost:
Paddy production
value of
loss
production loss
(100,000 tons)
(INR)
19.8
5
1.54
0.30
0.38
Damage total
Area unsown
Total
20.73
7.73
28.46
22.07
13.3
5
35.4
2
20,000x 2207=
44.14 billion
Notes: 1. Normal per ha yield of paddy is taken at 17.60 quintal.
2. 1 MT paddy cost is Rs 20,000
Source: Department of Agriculture, Government of Orissa
In a drought, Orissa suffers severe crop losses because of its dependence on monsoon rainfall for
agricultural operations. In July, even a small deviation of rainfall to the extent of -15% has a
serious impact on crop production in Orissa. In July 2002, the rainfall deviation from the
normal was around 46%. The extreme dryness in July 2002 caused serious impact on
agricultural operations, particularly for rice. Against the expected rice production of about 6.6
million tons, actual production was 2.8 million tons, a reduction of 58 %.
In 2002, the timing and extent (number of days) of dry spells in June and July were responsible
for the damage to rice crops in the state, as well as hampered rice transplanting. About 702,000
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Background Paper on Assessment of the Economics of Early Warning Systems for Disaster Risk Reduction
ha remained unsown at the end of the season. Early forecast could have resulted in savings of
input costs for the 2.244 million ha, which were cultivated and in which paddy was lost.
Input cost @ INR 4,000/ha for 2.244 million ha
(potential savings in 2002 alone)
INR 8.98 billion
(about USD 200 million)
Total benefit considering probabilistic forecasting (70%): 200x 0.4
USD 80 mi
Recurrence every five years is common, hence over a thirty-year period, this saving would be
increased by 6 times, i.e., about USD 480 million could be saved, in only one of the 10
drought-prone states in India.
Major interruption to the monsoon, especially in the month of July, and the interrupted intercultural operations in the broadcast areas resulted in a decline in paddy production. The area
damaged and production loss estimates are tabulated below.
Table 22: Crop production losses due to drought, India 2002-2003
Crop
Rice
Coarse cereals
Wheat
Pulses
Total food grains
Groundnut
Rapeseed/ Mustard
Soyabean
Other Oilseeds
Total nine oilseeds
Cotton (mil. bales)
Jute, Mesta (mi bales)
Sugarcane
2001-2002
production
(million tons)
93.08
33.94
71.81
13.19
212.02
6.9
5.0
5.9
2.7
20.5
10.1
11.6
300.1
2002-2003
actual
production
(million tons)
75.72
26.22
69.32
11.31
182.57
4.7
4.5
4.3
1.9
15.4
8.9
11.5
285.4
Loss in Crop
Production
(million tons)
17.36
7.72
2.49
1.88
29.45
2.2
0.5
1.6
0.8
5.1
1.2
0.1
14.7
Total Loss
Loss in crop
MSP
production
(INR per ton) (10 million INR)
5,100
8,853.60
5,400
4,168.80
4,450
1,108.05
12,000
2,256.00
16,250
12,200
11,700
12,000
3,575.00
610.00
1,872.00
960.00
590
785
590
70.80
7.85
867.30
24,349.4
Source: Department of Agriculture Extension, Ministry of Agriculture
Input costs associated with the cultivation could have been saved at the national level as a
result of early warning. Input costs may be assumed as 50% of production value.
Input costs saved (all India): 0.5 x 243,494 million
INR 121.75 billion
(about USD 3 billion)
Total benefit considering probabilistic forecasting (70%): 3 x 0.4
USD 1.2 billion
Thus an early warning could have resulted in a savings of approx. USD 1.2 billion in India
during 2002 drought just at the farm level.
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Background Paper on Assessment of the Economics of Early Warning Systems for Disaster Risk Reduction
Box 6: Possible measures that could have reduced the impacts of 2002 drought
1.
Currently, the generated climate information products only cater to broad policy making at the macro level on
the one hand, or are at the fine scale of the weather. As a result, intermediary scale products, ranging from
several weeks to seasonal and inter-annual, which are important to a variety of climate-sensitive decisions and
policies, were not put to use for resource management at the community, local and state levels.
2.
Advance weather information during kharif season, with reasonable lead-time and sufficient specificity to
enable farmers to modify their decisions before and during the cropping season, would have helped reduce the
impacts. After all, the break and active cycles of monsoon, like the one experienced in 2002, affect farming
operations in varying degrees almost every year in one part of the country or the other. About 20-30% of the
districts suffered from deficient/ scanty rainfall even in so-called normal monsoon years.
3.
Spatially and temporally differentiated weather information with a lead-time of 20-25 days could have been
of great value to policy planners and farmer service organizations to provide critical agriculture input support
services to farmers. For example, if the July 2002 monsoon break was forecasted and disseminated to the
agricultural community 25 days before, it would have minimized damage to agriculture significantly.
4.
Assuming that a prediction was available by the first or second week of June 2002 about the likelihood of dry
spell in July 2002, farmers could have been motivated to postpone agricultural operations, saving investments;
water resource managers could have introduced water budgeting measures. Similarly, the prediction of the
revival of the monsoon in August 2002, could have motivated planners and farmers to undertake contingency
crop-planning during pre-rabi season.
5.
In conclusion, efforts to generate farmer-friendly weather information has to run parallel with efforts to
develop systems to interpret, translate and communicate probabilistic forecast information to farmers, sector
managers, and end users, and receive feedback with the active participation of State Governments, local
institutions, and civil society organizations. A continuous feedback from end users would help improve
quality, timeliness, and relevance of climate/ weather information. An end-to-end climate information
generation and application system, with feedback mechanism, that connects end users and weather
information providers and make use of latest advances and downscaled predictions, supported by utilization
of past climate data for planning drought management and mitigation practices, would have resulted in
significant direct savings in the agriculture sector.
2.5
Category 2: Geological Hazards (e.g. Tsunami)
The 2004 Indian Ocean tsunami has galvanized public and government attention, and thus paved
the way for the establishment of extensive earthquake monitoring and tsunami detection
networks. However, a tsunami of similar magnitude may have a return period of at least 50 to
100 years and, for each of the affected countries (or countries at risk), to put up an early warning
system (EWS) is very costly.
Despite this, there are several tsunami warning systems for the Indian Ocean, unlike in the
Pacific. Of these, Australia, India, Indonesia, and Malaysia have currently operationalized their
national early warning systems and have also expressed willingness to provide tsunami watch
and alert services as regional providers to other countries in the Indian Ocean. Each of these
systems cost about USD 50 million individually. Thus, the total one-time cost of these systems
amounts to about USD 200 million. These systems, however, are not multi-hazard nor end-toend, hence may not be very sustainable in the long-run. In addition, these countries would be
spending between USD 5 to 10 million each year for operations, or about USD 30 million
collectively in a year.
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Background Paper on Assessment of the Economics of Early Warning Systems for Disaster Risk Reduction
In light of the low-frequency of tsunamis in the Indian Ocean, tsunami warning services would
be better (economically) served in a regional or a collective manner. Ideally, a collective system
may not require more than USD 1.5 million operating expenditure (for data processing and
communications). Additional cost of incorporating hydro-meteorological hazards into such a
system would be approximately USD 1 million per year. Hence, ideally, an annual operational
budget of USD 2.5 million should serve all the countries of the Indian Ocean.
Case Study 7: Regional Integrated Multi-Hazard Early Warning System (RIMES)
The collective system mentioned above is already in operation in the Indian Ocean, comprising
of over 26 countries5 from the Asian and African continents (Figure 7). The Regional Integrated
Multi-Hazard Early Warning System (RIMES) is facilitated by ADPC, and the regional facilities
are located at the Asian Institute of Technology campus in Bangkok, Thailand.
Figure 7: RIMES Member Countries
RIMES consists of earthquake monitoring and tsunami detection functions as a core. However,
localized disaster risk information, provided at higher spatial and longer temporal resolutions, is
the service which is found to be more immediately relevant by member states’ NMHS. This
allows constant engagement with NMHSs, given the more recurrent nature of hydrometeorological hazards, and thus ensures system sustainability. RIMES’ tsunami and hydro-
5
Bangladesh, Bhutan, Cambodia, China, Comoros, India, Lao PDR, Maldives, Mauritius, Mongolia, Myanmar, Nepal, Philippines, Sri Lanka,
Thailand, Vietnam and Yemen (17 countries) have signed formal agreements to collaborate with the regional system, and 9 more countries –
Indonesia, Kenya, Madagascar, Mozambique, Pakistan, Seychelles, Somalia, Tanzania and Timor Leste are at different stages of completion of
formalities of signing agreements.
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Background Paper on Assessment of the Economics of Early Warning Systems for Disaster Risk Reduction
meteorological sub-systems share common facilities, such as physical location; observation,
communication and data processing facilities; and human resources.
Figure 8: Integration of low-frequency, high impact (tsunami) and
high-frequency, low-impact (hydro-meteorological) hazards
Figure 9: Common elements - hydro-meteorological and tsunami subsystems:
computing resources
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Background Paper on Assessment of the Economics of Early Warning Systems for Disaster Risk Reduction
*Oceanographer
* Data analyst
Geophysicist *
* IT Expert
* Climatologist
Seismologist *
* Watch Standers
* Synoptician
* Telecommunications
Specialist
* Risk Communication
Specialist
Tsunami subsystem
Hydro-meteorological
Subsystem
Figure 10: Integration of tsunami and hydro-meteorological subsystems:
human resource component
*Sea level stations
* Front end system
EQ monitoring *
EQ data processing
*
* Data processing
* Center Infrastructure
* Communication
*Upper air/ surface
observation network
* Data assimilation
system
* Research
Tsunami subsystem
Hydro-meteorological
subsystem
Figure 11: Integration of tsunami and hydro-meteorological subsystems:
system component
RIMES is more economical, by pooling resources and by rational distribution of observation
systems to fill critical gaps needed for optimal functioning of the regional system. RIMES also
provides capacity building services for user agencies, for both hydro-meteorological as well as
tsunami components.
RIMES operates a core Regional Early Warning facility to cater to “differential needs and
demands” of countries to “address gaps” in the end-to-end multi-hazard early warning system.
The 26 member-countries are at different capacity levels in hydro-meteorological forecasting, in
terms of observation systems, data communication and computing facilities, trained manpower,
and in downscaling to generate tailor-made forecasts, as well as in interpretation and translation
of forecasts into user-friendly formats. RIMES focuses particularly on addressing the
differential needs and demands in the areas of downscaling, and interpretation and translation of
forecasts.
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Background Paper on Assessment of the Economics of Early Warning Systems for Disaster Risk Reduction
Figure 12: Addressing various gaps in an end-to-end early warning framework
The concepts of economy of scale and economy of scope are particularly valid in this regional
context.
Economy of scale: Countries pool resources, as individual investment is costly, especially when
return periods for an ocean-wide tsunami is once in 100 years, notwithstanding other
development priorities for most countries in the region. The annual recurring costs for
maintaining the regional tsunami component of RIMES is about USD 1.5 million.
Economy of Scope: Inclusion of a multi-hazard approach to RIMES enlarges its scope.
Integration of other common hazards, such as floods, thunderstorms, cyclones/ typhoons, also
acts as a pull-factor for some countries for whom tsunami is not a major concern compared to
other more frequent, low-impact hazards. The additional services integrated in RIMES, beyond
tsunami alert and warning, has an added capital cost of about USD 1 million, but this has
resulted in greater interest and participation among the member countries.
RIMES also assists, through its engagement with the countries, in improving response to
warnings, making the early warning information even more effective and increasing the benefits
accrued due to the system, and thus the economy of the system. Integrating such value-added
and special services into the regional system also has the benefit of ensuring constant
engagement, greater participation of member countries, and economy of scale due to diversified
services. These services have an annual recurring cost of less than USD 0.5 million.
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Background Paper on Assessment of the Economics of Early Warning Systems for Disaster Risk Reduction
RIMES offer the following unique benefits to member countries:





Provision of tsunami watch
Capacity building and technology transfer to NMHS for providing localized hydrometeorological disaster risk information
Enhancing capacities to respond to early warning information at national and local levels
for disaster preparedness and management
Acting as a test-bed to identify promising new, emerging technologies, and pilot test and
make it operational through demonstration of tangible benefits
Apolitical nature of the system fosters cooperation and addresses the constraints relating
to national pride and rivalry
RIMES capital cost
Capital cost in meeting tsunami information and capacity
building requirements of all member-countries (UNESCAP-funded):
USD 4.5 million
Capital cost in meeting weather and climate information and capacity
building requirements of all member-countries (Danida-funded):
USD 1.5 million
Total capital investment (tsunami and hydro-meteorological hazards):
USD 6 million
RIMES annual operating cost
Annual operating cost in meeting tsunami information and
capacity building requirements of all member-countries:
USD 1.5 million
Annual operating cost in meeting weather and climate information
and capacity building requirements of all member-countries:
USD 1 million
Total annual recurring cost (tsunami and hydro-meteorological hazards): USD 2.5 million
These compare very favorably with the USD 200 million capital cost and USD 30 million
annual operating cost for the tsunami systems of four countries – Australia, India, Indonesia and
Malaysia. Budgets for each of these systems include observation systems. A regional system
would, however, optimize distribution of observation systems, reducing capital investment
requirements.
Thus RIMES, with an annual recurring cost of USD 2.5 million could enable member
countries to accrue the benefits of early warning as tabulated in the case studies. Collective
savings for the system would be at least in the order of a few hundreds of million US dollars
each year.
Despite this demonstrable savings, Indian Ocean countries were unable to replicate the Pacific
tsunami warning system due to the following reasons:

Firstly, the Indian Ocean does not face the frequency of tsunamis as is experienced in the
Pacific Ocean, which has its rim of fire – an active region generating more frequent
tsunamigenic earthquakes. Hence, there is no compelling reason for countries to
collaborate as in the Pacific Ocean.
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Background Paper on Assessment of the Economics of Early Warning Systems for Disaster Risk Reduction


Further, many being developing economies, there is national pride and rivalry involved,
which makes it very difficult for any one nation to be unanimously acceptable to all
countries as a regional power, though there may not be any doubt over capabilities of
many countries to don this mantle.
Many countries in the region have no history of mutual dependence or collaboration on
any major issue. Rather, there have been many skirmishes and full-fledged wars
between countries and, until recently, few have had a history of working together.
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Background Paper on Assessment of the Economics of Early Warning Systems for Disaster Risk Reduction
3. Non-Market Factors
3.1
Factors Influencing Adoption of EWS at Government or Institutional Levels
Governments with good governance are responsive to the needs and aspirations of its people,
and would have a motivation to establish early warning systems that protect its people and their
livelihoods. This has been demonstrated in Dumangas, Iloilo Province in the Philippines and
Indramayu, West Java in Indonesia (refer to Case Study 5 and Annex D). In most locations/
countries, however, investment in early warning systems is constrained by several factors,
notwithstanding the benefits that may be derived from the EWS.
3.1.1 At policy level
Perception
There is still a lingering perception that natural disasters are ‘Acts of God’, i.e., governments/
institutions/ communities cannot do anything, but have to live with disasters. So if a disaster
occurs, the government cannot do anything to avoid its impacts, or is not blamed for not doing
much about it. Recent assessments indicate that communities in the Nargis Cyclone-affected
areas in Myanmar, Sidr cyclone-affected areas in Bangladesh, and earthquake- affected areas in
Pakistan hold that perception. Hence, there is no desirable level of pressure on governments to
invest in EWS.
Establishing a robust early warning system would entail an investment, and that, too, for events
which would happen infrequently, or cannot be prevented in the eyes of policymakers; hence
resources are spent on more compelling priorities, such as poverty alleviation, infrastructure
development, etc. Becker and Posner6 opine: “Politicians with limited terms of office and, thus,
foreshortened political horizons are likely to discount low-risk disaster possibilities, since the
risk of damage to their careers from failing to take precautionary measures is truncated.”
Hard evidence, based on a systematic study of the cost and benefits of EWS for the country, can
convince politicians to invest in EWS. Consistent efforts to engage movers and shakers in the
country would also be needed. Demonstrations should consider areas with high economic stake
to engage communities and local institutions, and create a demand for EWS (the experience of
Indramayu, West Java in Indonesia (Annex D) provides an example).
Not tangible enough?
The benefits from an effective early warning system are not tangible enough for policy makers,
as compared to that from an essential early warning system (saving lives), to divert public
finance towards it. While it is easy to survey and estimate the damage and losses post-disaster,
it is still not easy for responsible agencies to convince decision-makers about the ‘preventable or
avoidable damages’ that an effective early warning system can bring about. This is due to lack
of experience in countries in Asia, except in the case of the Philippines, to a limited extent in the
agricultural sector, to undertake potential pre-event impact assessments due to EWS to convince
policymakers about the benefits of EWS.
6
http://www.becker-posner-blog.com/archives/2005/01/the_tsunami_and.html; Blog ‘ The tsunami and economics of catastrophic risk’.
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Background Paper on Assessment of the Economics of Early Warning Systems for Disaster Risk Reduction
Creating and demonstrating tools for measuring intangible benefits, engaging the media, and
creating awareness among policy- and decision-makers may be undertaken to make the benefits
of EWS visible. In Indramayu, Indonesia, the local media, having been exposed to the
application of seasonal climate information and its economic benefits, and having interacted
with forecasters, agriculture extension workers and farmers, is now a partner of the local
government in highlighting the benefits of the EWS, particularly at the end of an “abnormal”
(e.g. drier than the usual dry) season. This has sparked interest for replication from neighboring
provinces (refer to Annex E).
Unwelcome harbinger?
Public awareness on disasters and, by association, early warning systems are considered as
unwelcome in some cases where it could hurt economic potential of the area. Anecdotal
information reveals that in areas of Padang, West Sumatra, hotels were averse to display tsunami
evacuation routes even after the devastating December 2004 tsunami due to fear of hurting
occupancy rates. Local governors in southern Thailand discouraged tsunami EWS based on
probabilistic conjecture-based forecasts, for fear of losing tourists.
Awareness-raising and education of hotel operators, tourist service providers, and communities
would be required. Similar to Thailand’s Ministry of Health’s certification program on clean
and safe food for food establishments, which foreign tourists appreciate, a certification process
may also be initiated, adapting the U.S. National Oceanic and Atmospheric Administration’s
(NOAA) certification for hazard-ready communities. This certification process is currently
being piloted in select high-risk sites in Indonesia, Philippines, Sri Lanka, and Vietnam.
Trans-boundary hazards?
In case of trans-boundary hazards such as tsunami, or even a cyclone or typhoon, there is even
less incentive to establish an EWS since there is an opportunity to free-ride, as explained by
Becker and Posner7 “…….where risks are regional or global, rather than local, many national
governments, especially in the poorer and smaller countries, may drag their heels in the hope of
taking a free ride on the larger and richer countries..” Some countries in the Indian Ocean
region exhibit these tendencies with respect to tsunami EWS.
Further, where the source of hazard risk lies in one country and impact is experienced in
another, there is no effort to establish joint bilateral collaborative EWS, e.g., trans-boundary
flood risk in Himalayan Rivers. Even within countries, trans-jurisdictional issues act as
disincentive for investment in EWS, for instance, the different provinces in Panay Island in the
Philippines.
High frequency, high impact hazards lead to essential early warning services in a country, but
low frequency, low impact hazards are largely ignored, since its low impact means only a small
area is affected and responsibilities remain largely with the immediate district or provincial
authorities, and rarely get national attention, though many areas may be prone.
Damage and loss assessments to blame?
Though all recent post-disaster assessments, with pressure from donors, have started to
incorporate both direct damages and indirect losses, government decision-making still does not
7
http://www.becker-posner-blog.com/archives/2005/01/the_tsunami_and.html; Blog ‘ The tsunami and economics of catastrophic risk’.
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Background Paper on Assessment of the Economics of Early Warning Systems for Disaster Risk Reduction
fully comprehend and incorporate the magnitude of indirect losses, and only aspects of direct
damage due to disasters are still considered when taking crucial decisions. Investments for
improving EWS are often ignored for this reason. But where governments have absorbed the
enormity of losses in addition to the damages, there has been some concrete action. For
instance, the Government of India commissioned a detailed study on the 2002 drought, which
highlighted the huge losses, much of which could have been avoided had there been a pro-active
early warning system. As a result, it has funded improvement of drought forecasting, as well as
setting up of a comprehensive drought management system covering the entire nation.
Essential EWS vs. effective EWS?
Stagnation with essential early warning services, i.e., systems which reduce loss of lives, is one
of the reasons that hinder further improvement of early warning systems. Mobilizing public
finance for the transition to the next level of an effective EWS (saving lives and reducing
damages, impacts, and disruptions) is very difficult compared to developing an essential early
warning service. Some possible explanations for this are also considered in following two cases
from India.
Cyclones in Andhra Pradesh
The table below illustrates the varying impacts caused by some severe cyclones affecting the
state of Andhra Pradesh in the east coast of India. While loss of lives has been reduced to a
great extent, estimated losses have been steadily increasing. The technology and efforts from
the state and central governments have been more focused on saving lives, rather than reducing
damages and losses.
Table23: Impacts of some severe cyclones (1977 to 2006) in Andhra Pradesh
Cyclone
events
No. of
districts
affected
Population
affected
(million)
Lives lost
Livestock
loss
(no.)
Houses
damaged
Crop area
Estimated
damaged
loss
(ha)
(million INR)
Nov 77
8
3.40
10,000
250,000
1,014,800
1,351,000
1,720
May 90
14
7.78
817
27,625
1,439,659
563,000
21,370
Nov 96
4
8.06
1,077
19,856
61,6553
511,000
61,290
Oct-Nov 06
5
1.39
41
350,000
95,218
384,550
71,730
Source: http://disastermanagement.ap.gov.in/website/history.htm (Department of Disaster Management, Government of Andhra Pradesh)
Similarly, there is an annual loss of around 100 to 500 lives due to typhoon-associated hazards
in Vietnam and Philippines, and up to 5,000 lives in Bangladesh due to severe cyclonic storms.
These are accepted as tolerable disaster thresholds. Public policy is somewhat insensitive to
invest in improvements in EWS, unless unwritten disaster threshold tolerances are breached.
Droughts in India
Absence of a pro-active drought early warning system, despite recurrence of droughts across a
large part of the country, is surprising, considering its severe impacts (a case in point is the 2002
drought). While there are several institutions at national and state levels approaching different
issues relating to droughts from various perspectives, there is not yet one collective system that
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Background Paper on Assessment of the Economics of Early Warning Systems for Disaster Risk Reduction
is able to provide efficient drought early warning services to the national or state governments
which leads to appropriate impact reduction actions. After the 1967 drought, which led to over
1.5 million deaths, the Government of India took several measures to address food security
concerns, and thereby minimized or prevented drought-related deaths. This was a major
achievement, but was not followed by similar large-scale initiatives to reduce drought-related
damages and losses which, in case of the 2002 drought, amounted to a staggering INR 200
billion or USD 4.4 billion, impacting nearly 300 million people in 16 states of India. This
drought convinced policy makers to improve drought EWS.
Emotive Factor?
Preventing or minimizing loss of lives eliminates the emotive factor which arouses public
attention. Thereby, once an essential EWS is in place, it becomes more difficult to attract
priority government investment for further improvement. With significant reduction in the loss
of lives due to natural disasters as a result of various factors, such as improved accuracy of
forecasting, better understanding of hazards, better response, and improved awareness, the
emotive factors associated with disasters are reduced. Thus, associated damages brought about
by disasters are treated as unavoidable, or institutions try to justify that early warning systems
cannot save all lives at all times, and that there would always be some unavoidable loss of lives
or damages. This threshold for unavoidable loss or damage varies from country to country, and
may be a reflection of the accountability of the governance system, size of countries, economic
status, and severity of hazards.
In some countries, there is a greater tolerance of disaster thresholds, which limits the impetus to
establish warning and appropriate response systems. In a country with a huge population like
India, this threshold could well be a few hundreds, while in the neighboring country of Bhutan,
even one casualty would be treated as a disaster. Hence, it is only a very big event that can
precipitate changes in the system so that a new, emerging early warning technology would be
experimented with and adopted.
3.1.2 At political levels
Political disincentives – lack of continuity?
In some cases, an early warning system established by a previous political administration does
not receive due backing and financial support from the next administration, as demonstrated in
the case of Dumangas municipality, Iloilo Province in the Philippines (see Box 9). The new
Mayor, who inherited this well-functioning system for providing essential forecast information
benefiting several hundred farmers in the province from his pro-active predecessor who had
established it, was not interested in sustaining its operations since it had the stamp of his
predecessor. However, the intervention of the Governor of Iloilo Province ensured that the
system was kept alive, inspiring other municipalities to emulate it.
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Background Paper on Assessment of the Economics of Early Warning Systems for Disaster Risk Reduction
Box 7: Agro-meteorological station in Dumangas Municipality, Iloilo Province, Philippines
The Dumangas municipal government was instrumental in establishing in 2002 a scientific agro-meteorological
(agro-met) station, in cooperation with ADPC and PAGASA. The first Climate Field School in the Philippines
was also established in Dumangas, Iloilo.
The agro-met station conducts daily observations of weather and climate parameters. The data collected is
interpreted by PAGASA main office in Manila and sent back to the center for dissemination to farmers, fishpond
operators, government units and other stakeholders. Farmers get their daily weather advisories to guide them in
their farming activities, and are immediately informed of impending natural disasters so they can prepare and
minimize the impact on fishery and agriculture industries. The Dumangas disaster program is a Hall of Fame
Awardee of the National Disaster Coordinating Council’s (NDCC) Gawad Kalasag, an annual search for best
practices in disaster management.
Political system?
Cuba and Vietnam have managed to reduce loss of lives considerably, despite the high
frequency of hurricanes and typhoons, respectively. There are interesting studies on Cuba (Ben
Wisner, Lessons from Cuba? Hurricane Michele, November, 2001; Lino Naranjo Diaz,
Hurricane Early Warning in Cuba: An Uncommon Experience, MeteoGalicia, University of
Santiago de Compostela), which highlight several possible reasons for Cuba’s success, despite
the sanctions and its isolation. It is quite provoking to attribute the success to the socialist model
in place in Cuba. However, more likely reasons are that as a command state with a highly
educated and disciplined professional class, Cuba can easily organize large evacuations and
coordinate action among water, power, gas, health, and other sectors. This can be supported by
its effective neighborhood organization. Successful responses to forecast information also
highlight the historical memory of past disasters, actively encouraged by the authorities, and
trust on the part of the general population. Many developing Asian countries, save for one or
two like Vietnam, cannot claim to be in a similar position. (Vietnam is under a similar political
system, with the government able to organize large-scale evacuations and coordinate action
among sectors, with its mass-based organizations involved and having responsibilities before,
during, and after an emergency (ADPC, 2003)).
Despite a long culture of multi-party political system, the administration and political systems in
many countries are not so accountable to the public, for public opinion to force them to invest on
costly technology. India, for example, still does not have a robust drought early warning system,
despite periodic, massive losses due to drought.
Relief and rehabilitation offers more visibility?
Post-disaster relief and rehabilitation provides an opportunity for the government to increase its
visibility and be seen as responsive. However, public, as well as media, attention is focused on
the response, and not on underlying causes which result in such increasing losses and damages.
The issue of focusing on the most recent disaster is also worthy of being highlighted.
Investment on EWS, on the contrary, would be a hard sell as it is abstract and lacks the visibility
of expenditure for post-disaster response and relief.
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Background Paper on Assessment of the Economics of Early Warning Systems for Disaster Risk Reduction
Lack of accountability?
Boxes 8 and 9 illustrate the issues of lack of accountability to the public, by concealing or
censoring relevant information. In Thailand, bird flu information was not shared as it might
have hurt the tourism potential, while in France the information on high casualties in the heat
wave was restricted to prevent ‘alarm’. In India, in case of the Gujarat cyclone of 1998, over
1,000 people died in Kandla Port as warning information did not reach them in time.
Box 8: Bird flu claims first Thai victim
The Thai government only confirmed an outbreak of bird flu -- a strain of H5N1 avian influenza -- on Friday after
days of denying accusations from farmers and opposition legislators that the nation had been hit by the dangerous
disease. The Thai Prime Minister conceded on the weekend that his government suspected for "a couple of
weeks" that the country was facing an outbreak of bird flu, but decided not to reveal the outbreak until Friday in
order to avoid mass panic. The Tai Prime Minister's admission comes as his government faces increasing
criticism over its handling of the outbreak amid claims of a cover up.
Source: http://www.cnn.com/2004/WORLD/asiapcf/01/25/bird.flu/
Box 9: August 2003 heat wave in France
During the first fortnight of August 2003, a severe heat wave affected most of Europe, with a number of
consequences on water availability, energy supply (in Italy, for instance), a significant increase in forest fires
(Portugal), and atmospheric pollution (Belgium). But nowhere was the impact as dramatic as in France where the
mortality increased 55% nationwide, and as much as 221% in the area of Paris. More than 80% of the affected
people were older than 75, and 64% were women. About half of the deaths occurred in homes for the elderly in a
country that spends 9.5% of its GNP on public health.
……. The National Assembly established the Commission d’Enquête on 7 October 2003 to inquire into the causes
of the disaster caused by the heat wave. It appears that not only had warning systems failed, but on 8 August the
Prefect of Police, Paris, instructed the Fire Brigades “not to be alarmist and not to disclose the number of deaths”
in testimony by Jacques Kerdoncuff, Commander of the Paris Fire Brigade, before the Commission on 5
November……….
(by Rene Gommes, Jacques du Guerny, and Michele Bernardi)
The poor has no voice?
In the Jakarta city floods, Dhaka urban floods, and Mumbai floods, majority of the people
affected are the marginal population who, though numerous, do not have a ‘loud’ voice. In
Shanghai, a city which experienced a spurt in economic growth in recent years, the Shanghai
Multi-Hazard Early Warning Systems project has been initiated recently for many reasons. One
of which is that Shanghai is now ‘important’ and ‘valuable’ to deserve the investment (as
compared to a decade ago), as more and more assets are exposed to disaster risks. There are
larger proportions of populations at risk in the hinterlands who would still not have access to
such warning facilities.
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Background Paper on Assessment of the Economics of Early Warning Systems for Disaster Risk Reduction
3.1.3 At technical institutions
Uncertainty of science
In the operational forecasting agency, there is lack of incentive for identifying, experimenting,
and operationalizing technologies. The system is amenable only towards technology which has
been proven and demonstrated. In Bangladesh, when the long-lead flood forecast technology
was experimental, there was little interest. Use of longer-lead time forecast, which is
probabilistic and with inherent uncertainties, requires whole-hearted acceptance from users and
commitment from the NMHS to connect and engage with users. This culture is not in vogue
among the countries of this region. Hence, this is a disincentive for the adoption of such
probabilistic longer-lead time forecast technologies.
Bureaucratic psyche towards uncertainty of information?
Uncertainty of science in generating accurate forecasts is often a disincentive. While
bureaucrats deal with uncertainties in financial forecasts, budget planning process, and in many
other ways, they uncharacteristically insist on a high degree of certainty in weather and climate
forecasting, which is not possible even with the best technology, limiting the resource allocation
for forecasting. For every proposal identifying strength and opportunities, bureaucracies are
adept in posing weaknesses and constraints to derail it. A case in point is the Technical
Assistance Project Proforma (TAPP) for continuing a successfully demonstrated forecast
technology which, despite the approval of the Government of Bangladesh, did not pass muster
with a donor agency and remains under active consideration since 2006.
Multi-disciplinary?
First order early warning services that save lives are straightforward to implement through the
disaster management machinery. In comparison, the next level of services reduce damages or
impacts using longer-lead time probabilistic forecast information whose utility encompasses
multiple sectors, demanding greater coordination, cooperation, and a multi-disciplinary
approach, but are more complex in implementation. For a developing country, this multisectoral cooperation around an effective early warning is difficult to accomplish, and hence does
not take off as rapidly as an essential early warning.
Lack of accountability?
Another aspect of lack of accountability is within the early warning system itself. A method
commonly adopted by an early warning agency to judge its accuracy is to compare the observed
parameters with forecast parameters, e.g. measured wind speed of the cyclone against the
forecast wind speed. Forecasters consider it a success if the forecast figures are close to 70% of
the observed figures, irrespective of the damages that occur despite the ‘accurate’ forecast. The
Central Water Commission of the Government of India, in its annual report (2006-07) observed,
“During the flood season 2006 (May to Oct), 6,655 flood forecasts (5,070 level forecasts and
1,585 inflow forecasts) were issued, out of which 6,370 (95.7%) forecasts were within accuracy
limit. Similarly, out of 1,585 inflow forecasts issued, 1,543 (97.4%) at 26 stations were within
permissible limits of accuracy….”
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Background Paper on Assessment of the Economics of Early Warning Systems for Disaster Risk Reduction
Figure 13: Central Water Commission (CWC) of Government of India,
Flood Forecasting Performance (1997- 2006)
No early warning for surprises
The points above discuss cases of recurring hazards, and not surprises, such as the Indian Ocean
tsunami of December 2004 (most of the countries had not faced a tsunami in living memory),
the Myanmar Nargis severe topical cyclone of May 2008 (no cyclone in living memory had
crossed Ayerwaddy delta), the recent Kosi floods in India due to structural failure upstream in
Nepal (which was unprecedented in recent memory), and the typhoon Frank of June 2008 in
Philippines which crossed central Philippines while typhoons cross only the northern part of
Philippines at that time of the year. It is quite acceptable for institutions to defend their failure
to forewarn by arguing that the hazard event was a ‘surprise’ for which early warning was not
quite possible. However, institutions and systems could be sensitive to risk knowledge as there
were cases in the past – 1881 Indian Ocean wide tsunami, 1941 Andaman tsunami, 1945
Pakistan tsunami – which meant that these ‘surprise’ events were not actually surprises.
Disconnect of early warning with response
Even if early warning information is issued only one hour ahead, the national institution
generating early warning information considers that its job is done, for it is the responsibility of
notified institutions and communities to respond. Evaluation of early warning is still connected
to the dissemination, not to the response that can be attributed to it. Ideally, the response should
be a measure of the effectiveness of early warning. (Refer to the example above on the Central
Water Commission (CWC).)
A set of performance criteria that includes forecast accuracy, rapid notification, userfriendliness, and recipient responses, among others, may be used to evaluate EWS. Results of
the evaluation will be provided as feedback to the NHMS, as well as intermediary institutions,
through the pre-monsoon dialogues between forecasters and users of information, to motivate
improvement in outcomes.
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Background Paper on Assessment of the Economics of Early Warning Systems for Disaster Risk Reduction
3.1.4 At the community level
Community responses guided by recent experiences
Community responses are influenced by their recent experiences – if there has been a major
event, such as a cyclone in the last few years, then a cyclone early warning results in overresponse and panic. If the last known event was beyond recent memory (varies from place to
place), then it results in under-response. However, some communities can keep alive their
experiences and pass memories on from one generation to another as in the case of the Simeulue
Island. In less prone areas, a major hazard event is treated as a surprise resulting in ineffectual
response. False alarms of previous events could result in lukewarm response to early warnings
to subsequent real events (in Bangladesh, false tsunami alarm led to poor community response
for cyclone Sidr).
Education on the nature of hazards (not all events are the same), uncertainties in predicting
them, and the importance of (preparedness) vigilance is important. Warnings should be
delivered within a risk communication framework, informing receivers about risks associated,
not only with the hazard, but with possible responses.
User-friendliness of early warning
Early warning, for scientific institutions, comprises of data such as amount of rainfall, or wind
speed and direction. However, response is determined not by data, nor by information in the
warning messages, e.g., trees may be uprooted, but only when the information is personalized
into knowledge specific to ones’ context – such as what the wind speed means for his or her
agricultural crops, livestock, or poultry.
The Orissa Super Cyclone of 1999 illustrates that though the coastal population was aware of the
cyclone, they did not personalize the storm surge intensity, which meant more people were at
risk even in places far away from the coast.
Channel is as important as warning content
Early warning information for Cyclone Nargis was disseminated up to 48 hours in advance in
Myanmar through official channels, including state-run television media.
Anecdotal
information suggests that communities were informed verbally by military personnel based in
the area. However, there is a general mistrust among the public of both the media and the armed
forces, and hence this did not elicit an appropriate response from the public. The political
environment was also one of disinterest and mistrust, with a referendum being unilaterally
scheduled around the same time, so there was even less cognizance of this warning information.
For action to be predicated, ‘It is not enough to believe the message, but also important to trust
the messenger.’
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Background Paper on Assessment of the Economics of Early Warning Systems for Disaster Risk Reduction
3.2
Incentives for EWS
Public awareness
A big push for adoption of early warning could come from empowered civil society or massbased organizations. They are mostly unaware of the advances and potential benefits of
technology, but once empowered with the knowledge that many of the events which have
claimed lives or damage to property could be anticipated and impacts mitigated, they would be
able to influence communities and governments to adopt technologies for improved early
warning.
Accountability
If institutions and governments are held accountable for the loss of even a single human life due
to the hazard event, there is definitely a great scope and incentive for improvement of early
warning systems.
Economic sense
While reducing loss of lives definitely reduces public and government interest in improving
early warning, economic damages may continue to remain high. Hence, there is a need to
ensure a continuous, informed assessment of economic losses due to disasters. If the public and
government are convinced that a large percentage of these damages and losses could have been
avoided through improved early warning at a fraction of the cost, it might be an incentive to
invest on improving technologies. Emphasizing the linkages to development by sensationalizing
the avoidable economic damages and losses through the argument that the amount spent on
recovering from avoidable damages or losses could be better utilized for other pressing
development concerns, would also act as an incentive to strengthen early warning systems.
Removal of barriers
One of the ways to remove some of the barriers is for early warning institutional systems to
incorporate economic and social aspects of EWS, and for early warning to evolve into a multidisciplinary field by incorporating pre-impact assessment or potential damage assessment,
including avoidable damages, and identify appropriate response options to avoid these damages.
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Background Paper on Assessment of the Economics of Early Warning Systems for Disaster Risk Reduction
Financial instruments
Innovative financial instruments to support proven, but untested, technologies, and capacitybuilding of institutions to accept and make use of probabilistic forecasts in a risk management
framework could also be an incentive. As demonstrated by CFAB, technical research and
development capabilities of scientific institutions can be harnessed to tackle priority hazards,
such as floods in Bangladesh, through financial support from willing donors to develop
innovative, emerging technology-based solutions for pilot testing and improvement through
government institutional involvement. Once successfully demonstrated, the same can be
operationalized and integrated within existing EWS institutional structure of the government,
with necessary financial support from interested donors. This model holds great promise for
wider replication in other country and regional contexts too.
Avoidance of free–rider syndrome
UN technical agencies encourage resource–rich ‘big-brother’ countries to provide free early
warning services to neighboring resource-poor countries. These arrangements, though in most
cases provided EW information to some extent, also led to a lot of dissatisfaction among early
warning recipient countries. This is due to several factors, such as not up to expected level of
services in terms of lead-time and inadequate inter-personal communication during hazard
situations, and other factors, such as national pride involving provider and receiver, superior and
inferior complexes, and other political factors. These non-market factors, coupled with
economic advantages provided by recent advances in science and technology and information
technology revolution, encouraged resource-poor countries to look for alternatives to
collectively own and manage EWS by themselves in the context of increasing frequency and
intensity of natural hazards due to climatic and non-climatic factors. During UNESCO/ IOC
IOTWS meeting in Kuala Lumpur in April 2008, resource-poor countries expressed a desire to
establish by themselves a collectively-owned and managed EWS. A catalytic investment of
USD 4.5 million by UNESCAP has successfully encouraged this process for Indian Ocean and
South East Asia for establishing the Regional Integrated Multi-Hazard Early Warning System
(RIMES). This kind of strategic, small investments could act as incentive to establish a regional
EWS not only for low-frequency, high impact hazards such as tsunami, but also for high
frequency, but low impact hazards.
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Background Paper on Assessment of the Economics of Early Warning Systems for Disaster Risk Reduction
Annex A
Methods of Calculating Flood Damage Reduction due to Early Warning
Day’s Method
Day (1970) proposed that the tangible
benefit of a Flood Warning System
could be estimated as a function of
warning time due to the system. By
considering the value and spatial
distribution of property in the
Susquehanna River basin and the
historical response of property owners,
he developed what is commonly
referred to as the Day curve, shown in
Figure 18. This predicts damage
reduction in terms of percentage of
Figure A1: Day curve – damage reduction
maximum potential inundation damage
as a function of the mitigation time. If the warning time is 0 h, the curve predicts that the flood
warning system will provide no tangible benefit. If the warning time is 12 h, the Day curve
predicts that the damage will decrease by 23%. For example, if the damage without warning is
$1,000,000, and a flood warning system increases the mitigation time to 12 h, the damage
reduction will be $230,000. The Day curve also suggests that no matter how great the warning
time, the maximum possible reduction is about 35% of the total damage due to the flood. This is
logical, as some property, including most structures, simply cannot be moved.
Institute for Water Resources (IWR) Methods
A report by the Corps’ IWR
(USACE 1994) proposes two
methods for estimating the
benefit of a flood warning
system:
• Using the concept of the Day
curve: The IWR report
suggests
that
Day’s
“...methodology is perfectly
applicable today,” but notes
that the actual Day curve
should not be used. Instead,
the report suggests that the
Figure A2: Depth-damage curve
Day curve should be calibrated
to account for the differences
in the contents of residential structures of 1970 and the present and for other regional and system
differences.
• Shifting the depth-damage curve: The report suggests a 0.3 or 0.6 m parallel shift in the stagedamage curve to account for actions taken as the mitigation time available is greater. However,
the duration of mitigation time with which this shift corresponds is not reported. The report
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Background Paper on Assessment of the Economics of Early Warning Systems for Disaster Risk Reduction
further suggests, “The simplest way to adjust the stage damage curve is to assume some
percentage in reduction in damages at each stage. The extent of the assumed reduction in
damages used in the model can be determined based on explicit knowledge of the floodplain
community, results from similar studies, the literature, a Delphi or other consensus building
approach, or professional judgment.”
Flood Hazard Research Center (FHRC) Methods
The FHRC of Middlesex University, United Kingdom, has researched flood warning system
performance in the United Kingdom and published reports on the benefits of those systems.
Methods proposed for benefit evaluation are similar to those by Day and the IWR.
Based upon analysis of historical flood damage and simulation, Chatterton and Farrell (1977)
concluded, “...eventual depth of water in the building is an important factor influencing the
effectiveness of a flood warning. The damage-reducing effects of flood warning are likely to be
greater for high rather than for low flood stages.” They propose a relationship in which damage
reduction is a function of both depth and mitigation time.
Figure A-3 shows an example of the relationship for residential structures and contents due to
flooding at various depths; similar relationships are proposed for commercial and industrial
structures.
This
shows,
for
example, that with 4 h
of mitigation time, the
damage due to a flood
depth of 1.5 m could
be reduced by 72%. If
this result is combined
with the depth-damage
relationship of Fig. A2,
it can be concluded
then that the damage at
this depth would be
reduced by 72%: from
the originally predicted
40% of total value to
Figure A3: Damage reduction – function of depth and mitigation time
11.2% of total value.
If the total value of the
content of a structure is $100,000, with warning the damage is now reduced from $40,000 to
$11,200, a savings of $28,800 for the structure. This savings is attributable to the components of
the flood warning system. The damage reduction for other flood depths and warning times can
be estimated in a similar manner.
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Background Paper on Assessment of the Economics of Early Warning Systems for Disaster Risk Reduction
Annex B
Basic Services vs. Value-Added Services
Box B1: Investments for adopting early warning systems
The investment required for providing value-added services in addition to basic services already available is
calculated by breaking it down into the investments for:
1.
Generation of forecast information (in some cases, it is only an added cost for new services in addition to
the existing basic services)
+
2.
Management of information, i.e., interfacing the early warning information into stakeholder institutions
and user systems, e.g., for contingency planning, logistical support or preparedness costs (at both
institutional and community levels), and the community level response system, i.e., investment required
to enable communities to be aware of the hazard, understand the warning information, and respond
appropriately
Box B2: Basic services vs. value-added services
Recurrent disasters caused by hydro-meteorological hazards across the world reveal weaknesses at national
(institutional level) and local levels, especially so in their early warning and response capabilities. These incidents
result in a huge relief and rehabilitation cost and, year after year, great losses are borne by all developing and least
developed countries due to extreme events and natural disasters. The primary warning providers in many
countries are the National Meteorological and Hydrological Services (NMHSs), and this chapter examines the
distinction between basic services and value-added services of the NMHS.
Basic Services
Basic services refer to the first-order services from NMHSs, which give the basic weather and climate
information. The lead time is less than 3-days at best, and is quite inadequate for purposes of early warning, and
meeting user needs, beyond saving lives.
Several developing or under-developed countries are only able to provide these basic services. A case in point
would be the meteorology departments in Cambodia and Lao PDR. Their forecasts are limited to three days, with
only temperature being quantified; rainfall, for example is indicated as nil, moderate, heavy or very heavy.
Such information does not encourage users (government agencies such as agriculture, irrigation, or power; private
sector such as construction industry, transportation industry) to take risk reduction or preventive measures. For
example, during critical agricultural phases, the irrigation department in Cambodia requires at least one week
rainfall forecast to take preparedness measures, and to procure water pumps and keep on stand by for distribution
to farmers associations.
Value-Added Services
Value-added services on the other hand, refer to special services and products from NMHSs tailored to meet user
needs and requirements. High-resolution precipitation forecasts, with actual intensity of rainfall and duration and
spatial extent of occurrence, are an example of a special service with multiple uses for various users. Very
specific early warning products that are actionable leading to appropriate response measures, which in turn result
in reduction of losses is another example of a value-added service.
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Background Paper on Assessment of the Economics of Early Warning Systems for Disaster Risk Reduction
Value addition by IMD: District-level dynamical forecast for monsoon depressions and storms
Prediction of rainfall associated with monsoon depressions and storms formed during summer monsoon
season is a very challenging task for the India Meteorological Department (IMD). Numerical Weather
Prediction (NWP) models have limitations in predicting rainfall at a very small spatial scale, such as district
level. However, the value-added district level dynamical-synoptic system for rainfall, utilizing several inputs
such as different model outputs other than rainfall such as circulation features, sea level pressure, vertical
velocity etc., along with synoptic charts, climatology, and satellite, results in a considerable improvement of
forecast skill.
This system can be used to forecast, at a high resolution, the possibility of rainfall along the preferred track
(monsoon trough) passing from Orissa, parts of Madhya Pradesh, Uttar Pradesh and Delhi. This is especially
useful with potential benefits for agriculture and other related sectors. In the 2002 drought, for example, using
such a technique could have led to identification of the fact that there were no tropical depressions 8 in the Bay
of Bengal, hence very low-probability of monsoon rainfall along this preferred track. Hence, instead of
planting crops at the pre-fixed time (based on climatology), the planting could have been delayed or not
undertaken till alternative arrangements are made, resulting in enormous savings (refer case study on 2002
India drought for details of savings). This is an illustration of a special service that could be provided by a
NMHS, with marginal input cost – in this case, additional man-hours and cost of additional information
(negligible) with manifold benefits.
Value-added services by TMD: ocean state forecasts
The Thailand Meteorological Department (TMD) provides 24-hour ahead ocean state forecasts, including sea
state along shipping routes.
“….In the Gulf of Thailand; Sea is light breeze, have small wavelets and crests of glassy appearance, but do
not break. The significant wave height is 0.1 m and it's tendency maintain poise. Yuan;Sea is light breeze have
small wavelets and crests of glassy appearance but do not break. The significant wave height is 0.3 m and its
tendency maintain poise.”
Value-added services by Bureau of Meteorology: forecast and warning services for agricultural
purposes
Sheep grazier alerts
Provides warnings of wet and windy conditions to enable sheep graziers to take action to reduce losses among
new-born lambs and newly shorn sheep due to hypothermia.
Frost risk forecasts and warnings
Provides forecasts and warnings of frost, primarily to assist in reducing damage to frost-sensitive plants and
crops, as well as machinery.
Brown rot warnings
Provide warnings to fruit growers of conditions conducive to the development of brown rot on fruit.
Downy mildew forecasts
Provide forecasts of wet and mild weather conditions likely to lead to an infection of Downy Mildew in grapegrowing areas.
Warnings for barley growers
Provides warnings of windy conditions, during key flowering times so Barley growers can take action to
prevent damage to the flowering head of the Barley plant.
8
In meteorology, it is another name for an area of low pressure, a low, or trough.
53
Background Paper on Assessment of the Economics of Early Warning Systems for Disaster Risk Reduction
Improving basic services to provide value-added services
There is an additional investment required to upgrade basic services to provide value-added services. This
additional investment is often marginal compared to the benefits that accrue from it, as several case studies in
subsequent sections illustrate.
For example, incorporation of NWP techniques into a meteorological service can help improve lead time of
forecasts up to a week, as well as enable it to provide quantitative precipitation forecasts which can help in
agriculture and water management, among other sectors. The cost of additional computing equipment would
range from USD 200,000 (for cluster computers) to USD 1 million. Training and capacity building of existing
human resources could be estimated to be USD 50,000, to build-in NWP capabilities into a NMHS, with an annual
recurring cost of less than 10% of total costs (mainly for computing system maintenance).
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Background Paper on Assessment of the Economics of Early Warning Systems for Disaster Risk Reduction
Annex C
Avoidable Damage for Various Sectors:
Perception of Small Farmers in Bangladesh
Damage
category
Structural
damage
Content
damage
Total
value
(BDT)
55,000
47,125
Outside
property
damage
Livestock
damage
67,500
Agricultural
loss
25,050
46,500
Fishery loss
(cultured)
26,500
Fishery loss
(Open water)
6,500
Lead
time
24 hrs
48 hrs
Damage
reduction
(BDT)
4,800
10,300
Damage
reduction
(%)
9
19
7 days
35,300
64
24 hrs
10,580
22
48 hrs
44,080
94
7 days
44,680
95
24 hrs
48 hrs
7 days
24 hrs
48 hrs
7 days
24 hrs
5,000
35,000
45,000
300
20,300
20,300
2,400
7
52
67
1
44
44
10
48 hrs
8,400
34
7 days
19,400
77
24 hrs
48 hrs
7 days
24 hrs
48 hrs
7 days
8,700
11,200
19,500
650
1,000
33
42
74
10
15
Description of saving
Kitchen, ghol ghor
House wall protect with bamboo,
elevated platform made by bamboo,
kitchen, ghol ghor
House wall, roof, house wall protect with
bamboo, elevated platform made by
bamboo, kitchen, ghol ghor
Jewellery, TV, radio, clothes & kitchen
items, chair, table, mattress, chatai, dola,
tripol.
Stored crops, almira, jewelry, TV, radio,
clothes & kitchen items, chair, table,
mattress, chatai, dola, tripol
Dheki, stored crops, almira, jewelry, TV,
radio, clothes & kitchen items, chair,
table, mattress, chatai, dola, tripol
Fences
Trees, fences
Trees, fences, access roads
Chicken, ducks
Cow, goat, lamb, chicken, ducks
Cow, goat, lamb, chicken, ducks
Ladder, spade, plough, axe, leveler,
weeder
50% crop harvest from field, ladder,
spade, plough, axe, leveler, weeder
Orchard trees, total crop harvest from
field, ladder, spade, plough, axe, leveler,
weeder
Fish loss
Fish loss
Fish loss
Fishing net, boat damage
Fishing net, boat damage
Notes: (USD 1 = approx. BDT 70)
Based on study conducted at Baggar Dona River Catchment Area, in two unions – Char Jabbar, Char Jubilee, in Suborno Char Upazila
in Naokhali district of Bangladesh.
Source: ADB Early Warning System Study
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Background Paper on Assessment of the Economics of Early Warning Systems for Disaster Risk Reduction
Annex D
Additional Case Studies
Case Study D1 : Natural Disasters in Vietnam
Vietnam uses the Meso-scale Model 5 (MM5) in weather forecasting. Lead time, as well as
accuracy, could be substantially improved by utilizing more advanced technologies. The WRF
model, which runs at much higher resolutions, could provide greater accuracy, so losses could
be reduced and avoidable damages could also be minimized. By virtue of its accuracy in
predicting landfall point, as well as associated parameters such as wind speed and rainfall, this
also has the benefit of reducing avoidable responses including evacuation across hundreds of
kilometers along the coast, and disruption of fishing and other marine activities. Thus even the
cost of avoidable responses, in the form of opportunity costs for fishermen who avoid fishing for
at least a week due to each typhoon, could be reduced.
a) Observed
b) Simulated
Figure D1: WRF results for Vietnam: Typhoon Lekima
Table D1: EWS costs for Vietnam
Item
Fixed costs
(million USD)
Scientific component
High Performance Computing System
Additional training for human resources to generate forecast
information
Institutional component
Capacity building of national and sub-national (district)
institutions for translation, interpretation and communication
of probabilistic forecast information
Community component
Training of Trainers at local levels to work with ground level
users: farmers, fishermen, small business, households
Total (million USD)
56
Yearly variable costs
(million USD)
1.0
0.1
0.10
0.01
-
0.20
-
0.10
1.1
0.41
Background Paper on Assessment of the Economics of Early Warning Systems for Disaster Risk Reduction
EWS costs for 10 years
Fixed costs remain @ USD 1.1 million:
Variable costs @ USD 0.41 million per year for 10 years:
USD 1.1 million
USD 4.1 million
Total costs for 10 years (C):
USD 5.2 million
Table D2: Direct damages due to hydro-meteorological disasters in Vietnam - agriculture,
livestock and fisheries (2001- 2007)
Sector, items
Unit
2001
2002
2003
2004
2005
2006
2007
Agriculture
Paddy
Inundated
Ha
132,755 46,490 209,764 263,874 504,098
139,231
173,830
Destroyed
Ha
6,678
2,696 41,076
367
0
5,370
4,710
Lost
Ha
15,848
2,182 50,118 82,328 30,372
21,348
33,064
Farm produce
Submerged
Ha
85,528
0
5,925
4,720 160,780
122,460
215,059
Damaged
Ha
4,600 43,698
195
11
749
951
Lost
Ha
3,027 10,233
1,572
1,710
23,488
37,768
Seed beds
Submerged
Ha
3,159
6
5,252
1
2,115
Lost
Ha
302
3
0
1
Damaged
Ton
288
724
0
1,128
2,565
8,569
Food spoiled
Ton
17,237 42,064
9
13,346
79,118
Sugar-cane
Ha
17,296
0 11,639
248
1,829
3,064
33,769
damaged
Forest damaged Ha
5,328
0 467,063
293 23,524
34,028
5,404
Trees collapse
Unit
786,995
0
3,975 2,014,390 27,549,424 3,100,042
Orchard
Ha
51,221
0
3,755
65
86,433
30,647
damaged
Orchard lost
Ha
7
0
0
3,000
1,761
Livestock
Cattle killed
Unit
2,096
8,465
288
151
1,629
427
1,931
Pigs killed
Unit
53,604 27,723
2,535
14
6,708
619
246,553
Poultry killed
Unit
70,015 219,456 93,885
1,051 131,747
79,766 2,868,985
Sub-total VND M
79,485 198,268 1,921,045 316,894 193,862
954,690
432,615
Sub-total USD M
4.97
12.39 120.07
19.81
12.12
59.67
27.04
Fisheries and Aquaculture
Feeding area
Ha
16,615
0 14,490
7,805 55,691
9,819
19,765
damaged
Fish cages
Unit
3,298
310
51
446
124
329
1,308
drifted
Shrimp, fish
Ton
1,002
26 10,581
403
3,663
566
3,308
lost
Ships/boats
Unit
2,033
0
183
44
381
1,151
266
sunk, lost
Ships/ boats
Unit
344
0
1
42
1,095
163
sunk,damaged
100,650
194 131,116 33,073 235,536
258,500 111,224
Sub-total VND M
6.29
0.01
8.19
2.07
14.72
16.16
6.95
Sub-total USD M
57
2001 to
2007
1,470,042
60,897
235,260
594,472
50,204
77,798
10,533
306
13,274
151,774
67,845
535,640
33,454,826
172,121
4,768
14,987
337,756
3,464,905
4,096,859
256.05
124,185
5,866
19,549
4,058
1,645
870,293
54.39
Background Paper on Assessment of the Economics of Early Warning Systems for Disaster Risk Reduction
Total estimated
economic loss – all
sectors (million VND)
Total estimated
economic loss – all
sectors (million USD)
3,370,222 1,958,378 1,589,728 108,479 5,809,334 18,565,661 11,513,916
210.64
122.40
99.36
6.78
363.08
1,160.35
719.62
42,915,718
2,682.23
Notes: Financial figures for agriculture are estimated (2002 – 2005)
Financial figures for fisheries and aquaculture are estimated (2002 – 2005 and 2006)
USD 1 = VND 15,990
Source: Natural Disaster Mitigation Partnership, Ministry of Agriculture & Rural Development (MARD), Vietnam; Website:
www.ccfsc.org.vn/ndm-p
Table D3: Quantifying EWS benefits for hydro-meteorological disasters in Vietnam - agriculture,
livestock and fisheries (2001- 2007)
Damage without additional EWS
(as in
Table )
Sector, items
Ha or ton
Paddy
Destroyed
Lost
Total Paddy
Farm Produce
Damaged
Lost
Total Farm Produce
Seedbeds
Lost
Damaged (ton)
Total Seedbeds (ha)
Sugarcane
Orchards
Pigs (no.)
Poultry (no.)
Shrimp, fish (ton)
Total (million USD)
MT or no.
Total
(million USD)
Damage reduction with
EWS
Amount
(%)
(million USD)
60,897
235,260
296,157
592,314
189.54
10%
18.95
50,204
77,798
128,002
1,152,017
57.60
10%
5.76
337,756
3,464,905
19,549
6.72
9.50
0.29
6.76
3.46
18.57
30%
30%
30%
45%
45%
70%
2.02
2.85
0.09
3.04
1.56
13.00
47.27
306
13,274
10,839
67,845
4,768
Notes: Paddy: 1 ha = 2 MT; 1 MT = USD 320; Farm Produce: 1 ha = 9 MT; 1 MT = USD 50;
Seedbeds: 1 ha = USD 620; Sugarcane: 1 ha = USD 140; Orchards: 1 ha = USD 60;
1 Pig = USD 20; 1 poultry bird = USD 1; Shrimp, fish: 1 MT = USD 950 (average)
Total benefit considering probabilistic forecasting (90%): 47.27 x 0.8
Total benefit for 10 years: (37.81/ 7) x 10
USD 37.81 million
USD 54.02 million
Cost-benefit analysis for 10 years
Total costs for 10 years (C):
Total benefits for 10 years:
USD 5.2 million
USD 54.02 million
Total Benefit = 54.02
Total Costs
5.2
10.4
In other words, for every USD 1 invested in this EWS, there is a return of USD 10.4 in
benefits.
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Background Paper on Assessment of the Economics of Early Warning Systems for Disaster Risk Reduction
Case Study D2: 2000 Floods in Mozambique
Mozambique is one of the poorest countries in the world, with more than 50% of its 19.7 million
people living in extreme poverty. Civil war, conflict and extreme climate events have
negatively affected its development. The last three decades have seen seven major droughts and
seven major floods. Mozambique’s frequent flooding could be due to the tropical cyclones that
form in the southwestern Indian Ocean, causing heavy rainfall, though not many actually strike
its coasts. Another reason is the fact that Mozambique is a lower riparian country with nine
major river systems draining through it: 50% of the water in its rivers is due to rainfall outside
of the country. These events (and droughts) have the potential to impact about 80% of
Mozambique’s population, since they work mostly in agriculture and fisheries. Currently,
forecasts result in response-oriented actions, not pro-active, anticipatory actions. As a result,
there are often significant damages, as in the case of the 2000 floods, which were the worst in
living memory for Mozambique.
In earlier years, scientists at the National Institute of Meteorology had noted a correlation
between La Niña activity and high rainfall in southern Mozambique, conditions which now
appeared to be repeating more forcefully. They also noted that 1999–2000 coincided with the
cyclic peak of sunspot activity, which had, over the past 100 years, correlated with periods of
exceptionally heavy rainfall. On this basis, the Mozambican weather services warned, in the last
quarter of 1999, that there was a high probability of floods the following year.
The government took the warning seriously and mobilized accordingly. The disaster committee,
which normally meets just four times a year, started meeting fortnightly. In November, the
committee released a national contingency plan for rains and cyclones during the 1999–2000
season. Provincial and local structures developed their own plans and conducted preparatory
exercises.
Between January and March, the worst floods in over 100 years affected three major river basins
– the Incomati, Limpopo, and Save. The flooding was not the result of a single weather event,
but rather the cumulative effect of a succession of events. While each event was predicted and
monitored with some success, their interaction was complex and its combined impact was not
well foreseen.
There were heavy rains in southern Mozambique and adjacent countries (South Africa,
Botswana, Zimbabwe, and Swaziland) between October and December. Around the beginning
of February, a cyclone over the Indian Ocean, cyclone Connie, caused further heavy rain in the
Maputo area. The Limpopo, Incomati and Umbeluzi rivers were all affected by this time, with
water levels at their highest since records began. Three weeks later, cyclone Eline made
landfall, moving inland and causing serious flooding of the Save and Buzi rivers in the center of
the country, and aggravating the flooding of the Limpopo River in the south. At the beginning
of March, a third cyclone out at sea, Gloria, contributed to further flooding of the Limpopo,
Incomati, Save, and Buzi rivers. And finally, cyclone Hudah followed Eline and made landfall
in April.
At least 700 people died as a direct result of the floods. An estimated 350,000 livestock also
perished, and vast areas of agricultural land were devastated, with soils as well as crops lost.
Some 6,000 fisherpeople lost 50% of their boats and gear. Schools and hospitals were among
the many buildings destroyed. In all, economic damage was estimated at US$ 3 billion, or 20%
of the gross domestic product (GDP). A number of scientific advances may benefit flood early
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Background Paper on Assessment of the Economics of Early Warning Systems for Disaster Risk Reduction
warning in the future. These include improved capacity for predicting tropical cyclones.
Box D1: Lessons learned: possible causes for severe impacts despite some early warning
Pre-existing vulnerabilities
 Pre-existing vulnerabilities – low level of development due to various reasons, large-scale dependence on
agriculture and fisheries and other climate-sensitive sectors – placed the populations at great risk.
 The magnitude of the floods was overwhelming, and poverty of the majority of Mozambique’s people added
to their vulnerability.
Improvement of technical systems/ complexity of events
 Mozambique had policies and structures in place for domestic flood management, but it could not address its
water-related climate challenges alone, since weather events outside the country often largely determine the
internal situation. Regional cooperation is therefore critical, particularly for flood prediction.
 Further, the 2000 flooding was not the result of a single weather event, but rather the cumulative effect of a
succession of events. While each event was predicted and monitored with some success, their interaction
was complex and the combined impact of the events was not as well foreseen.
 Absence of risk assessments. An effective flood early warning system depends not only on the technical and
institutional capacity to produce a good risk assessment, but also on the communication of that risk to
vulnerable groups and to authorities charged with response.
 The river basin authorities and meteorological services lacked the capacity and equipment to carry out shortrange real-time modeling and forecasting.
Resource constraints
 Even in the case of availability of advance information, which sets off attempts to mobilize resources, few
resources could be spared, bearing in mind that a disaster was still merely a probability. For example, of the
20 boats requested, only 1 had been provided when disaster struck in an area.
Communication and community considerations
 Further, links between the media and the weather services were weak or non-existent. There was certainly no
media coverage of the risk during this period. Mass media were unaware of the flood prediction in the
months and weeks immediately before the floods, so there was low level of awareness among the
communities to prepare themselves.
 Flood warnings issued as the flooding escalated were not always accurate, and were not always properly
understood or heeded.
 Differing information came from different sources, which caused some confusion. The government relied on
government institutions, but NGOs, aid organizations, and others received forecasts from the USA or other
global sources. There is a need for a single voice to provide information to all stakeholder groups.
 Communication of flood warnings to the general public was even more challenging. The media did not have
a defined role, and did not begin to report until the disaster was happening. It seems that the risk was not
fully understood by many people, who chose not to leave their homes. Some died as a result, while others
had to be rescued as the waters rose.
Source: Hellmuth, M.E., Moorhead, A., Thomson, M.C., and Williams, J. (eds) 2007. Climate Risk Management in Africa: Learning from
Practice. International Research Institute for Climate and Society (IRI), Columbia, University, New York, USA.
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Background Paper on Assessment of the Economics of Early Warning Systems for Disaster Risk Reduction
Case Study D3: Climate Forecast Applications - Indonesia (2002-2003 El Niño)
The Asian Disaster Preparedness Center (ADPC), in collaboration with the Meteorological and
Geophysical Agency (BMG), Directorate for Crop Protection (DITLIN), Indramayu Agriculture
Office, and Bogor Agricultural University (IPB), with support from USAID/OFDA, implements
the CFA program in Indramayu (West Java) and Kupang (Nusa Tenggara Timur). BMG
provides the demonstration sites with localized seasonal climate forecasts at least a month before
the onset of the dry and wet seasons. This is demanded by farmers and other local users, such as
seed distributors, fertilizer traders, and other farming support institutions. Trained in risk and
potential impact assessments, the district level DITLIN and the Indramayu Agriculture Office
assess the potential impact of the rainfall forecast for the incoming season, prepare response
options, and communicate these to farmers through agricultural extension workers and farmers’
group representatives.
As required by farmers, information on season onset, rainfall characteristics, and length of dry
spell in the wet season are provided. The local government provides institutional support to
farmers through a revolving fund (of USD 30,000 for Indramayu district) that farmers can access
without interest (with a pay-back period of 2 to 4 seasons), seed supply, and mobilizing
agriculture input distributors to provide enough fertilizers, seed stocks, etc. to enable farmers to
respond to crop management options in response to the forecast. The local government also
established an agreement with a local cooperative to provide a market for the farmers’ products.
Farmers were trained in Climate Field Schools to understand forecasts and their constraints, crop
management practices appropriate for the climate outlook, and receive information on new
cropping practices and support mechanisms, such as establishing farmers’ cooperatives. The
Climate Field School, which meets once a week, is an important institutional mechanism that
allows regular interaction between BMG, DITLIN, Indramayu Agricultural Office, IPB and
farmers.
The Bhupati (head of local government) is willing to invest local resources (USD 30,0000) to
enable farmers to adopt alternate crop management practices, so that farmers can earn a profit,
despite the El Niño impact, and be in a position to repay their loans. Hence the indicative value
(conservative) of the CFA forecast could be estimated at at least USD 30,000 in one district.
This model has been replicated in over 50 districts by the national government (and is being
replicated in other districts). A rough estimate (at USD 30,000 per district) would yield the
indicative value of each seasonal forecast (currently in 50 districts) as USD 1.5 million, and
potentially (for 250 districts) as USD 7.5 million per season. The actual one-time investment to
produce this forecast would not be more than USD 0.25 million, with a marginal recurring cost
of USD 0.05 million per year.
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Background Paper on Assessment of the Economics of Early Warning Systems for Disaster Risk Reduction
Case Study D4: Sri Lanka - Drought Monitoring and Prediction in Sri Lanka
Failure of the northeast monsoon or low seasonal rainfall leads to droughts in the southeastern,
north-central, and northwestern parts of Sri Lanka (Figure 7). Droughts in the past 50 years
occur about every 3-4 years, with severe drought episodes almost every 10 years. Severe
droughts were experienced in the past 50 years in 1953-1956, 1965, 1974-1977, 1981-1983,
1985, 1993-1994, 2000-2001 and 2004. The incidence of drought in the second half of the 20th
century (1950-2000) is much greater than in the 1st half (1900-2000). The Department of
Meteorology (DoM) has noted a significant reduction in the annual average rainfall from 2,005
mm for the period 1931-1960 to 1,861 mm for the period 1961-1990, and an increase in rainfall
variability from 12% to 14% in the same period. Severe droughts impact on both agricultural
productivity and hydropower generation, which supplies about 70% of the country’s power
needs. Drought conditions affect the Maha crop, 1/3 of which is rain-fed, and the Yala crop,
which is entirely dependent on irrigation. The Maha crop accounts for about 2/3 of the yearly
crop production; the remaining 1/3 is contributed by the Yala crop.
Zone
Wet
Intermediate
Dry
Annual rainfall (mm)
> 2,500
1,750 – 2,500
< 1,750
Figure D2: Drought-prone dry zone in Sri Lanka
Use of ENSO information in drought monitoring
Development of drought conditions is monitored by DoM, using parameters such as rainfall.
Other parameters, such as El Niño Southern Oscillation (ENSO), the main driver of climate
variability in the tropics, may also be used. ENSO has differing impacts on the seasonal rainfall
in the country (Figure 8). A La Niña reduces rainfall during the Maha cropping season by as
much as 14%, but has a positive impact on rainfall during the Yala cropping season (Table 23).
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Background Paper on Assessment of the Economics of Early Warning Systems for Disaster Risk Reduction
Figure D3: ENSO impact on rainfall, Sri Lanka
Table D4: ENSO impact on seasonal rainfall, 1952-1997
Crop
Maha (Oct – Mar)
Yala (Apr – Sep)
Normal
1,220
Seasonal rainfall (mm)
El Niño
1,290 (+6%)
862
832 (-4%)
La Niña
1,048 (-14%)
992 (+15%)
1992 Maha season drop of 10,000 MT (El Niño year)
548,000 ha were sown, against the total available land of 602,000 ha
Opportunity cost (lost)
Average yield (1992) x additional area that
could have been sown: 3,512 (kg/ha) x 54,000
Opportunity cost at USD 200/MT:
190,000 MT or
11.6% of total Maha
production of 1.63
million MT
USD 38 million
1997 Maha season drop of 100,000 MT (El Niño year)
574,000 ha were sown, against the total available land of 602,000 ha
Opportunity cost (lost)
Average yield (1997) x additional area that
could have been sown: 3,565 (kg/ha) x 28,000
Opportunity cost at USD 200/MT:
100,000 MT or
5.6% of total Maha
production of 1.78
million MT
USD 20 million
Considering the existing capacities of DoM and the available technologies elaborated below, the
additional one-time investment required will be less than USD 1 million.
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Generation and application of seasonal climate forecasts
DoM could have the capacity to generate seasonal forecasts. However, capacity to deliver
localized forecasts that meet user needs (e.g. to guide cropping decisions) is a major constraint.
Sri Lanka can draw from the experience of ADPC in collaboration with the International
Research Institute for Climate Prediction (IRI) in the generation and application of downscaled
seasonal climate information in agriculture and water resource management in the region,
particularly in Indonesia, Philippines and Vietnam. A preliminary downscaling of global
climate model seasonal precipitation forecast for Sri Lanka (Figure 9) undertaken by IRI is
presented below.
Figure D4: Skill of downscaled global climate model seasonal precipitation forecasts
Box D2: Application of seasonal forecast for rice production in Sri Lanka
Seasonal climate forecasts are needed by early March for Yala and early September for Maha. During this period,
the acreage of rice to be cultivated, the type of rice variety to be used, and the choice of crops are deliberated upon
by farmer groups, district cultivation managers, and water managers. For example, during seasons where El Niño
is predicted, farmers may choose flood-resistant varieties in Maha and drought resistant short-term varieties in
Yala. In addition, the planting date could be delayed. Irrigation managers may increase the carryover storage to
tide over water deficits in the January to April period.
ENSO-based forecasts will be far from perfect, and farmers, irrigation managers and others who could use it for
agricultural decision-making should be well aware of it. The challenge in the successful use of probabilistic
ENSO forecasts is the communication of the level of uncertainty to farmers and water managers, and the choice of
steps that will minimize financial losses in case the predictions are incorrect.
Lareef Zubair, El Niño Southern Oscillation influences on rice production in Sri Lanka
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Background Paper on Assessment of the Economics of Early Warning Systems for Disaster Risk Reduction
Annex E
Climate Field Schools in Indonesia
The local media (Indramayu, West Java, Indonesia), as an active partner in highlighting the
benefits in using seasonal climate forecasts in farmers’ decision-making during the 2004 dry
season, which was influenced by a weak El Niño.
Farmers of Kelompok Tani Makmur got
good harvest in dry season 2004, while
neighboring villages did not get anything as
they made a wrong decision not to plant.
Visiting guests from South
Kalimantan to learn how
Indramayu implements the Climate
Field School
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Background Paper on Assessment of the Economics of Early Warning Systems for Disaster Risk Reduction
Annex F
List of References
Asian Development Bank (2006, October). TA-4562(BAN): Technical assistance grant for an
Early Warning System study. Draft Final Report.
Asian Disaster Preparedness Center (2003). The role of local institutions in reducing
vulnerability to recurrent natural disasters and in sustainable livelihoods development in highrisk areas: Vietnam case study.
Becker, G., and Posner, R. (2005). Blog: The tsunami and economics of catastrophic risk
Available: http://www.becker-posner-blog.com/archives/2005/01/the_tsunami_and.html
Benfield Hazard Research Centre (2006, June). Disaster early warning systems: People, politics
and economics. Disaster Studies Working Paper 16.
Diaz, L.N. (2003, October). Hurricane Early Warning in Cuba: An Uncommon Experience.
MeteoGalicia. University of Santiago de Compostela.
Available: http://www.disasterdiplomacy.org/NaranjoDiazMichelle.rtf
Ebi, K., Teisberg, T., Kalkstein, L., Robinson, L., and Weiher, R. (2004, August). Heat watch/
warning systems save lives: Estimated costs and benefits for Philadelphia 1995–98. Bulletin of
the American Meteorological Society, 85 (8).
Glantz, M. (2004), Report of workshop: Usable science 8 – Early Warning Systems Dos and
Don’ts. Shanghai, China. Available: http:// www.esig.ucar.edu/warning/
Gunasekera, D. (2004, August). Economic value of meteorological services: a survey of recent
studies. Economic issues relating to meteorological services provision, Research Report No.102.
Australia: Bureau of Meteorology Research Centre (BMRC).
Available: http://www.bom.gov.au/bmrc/pubs/researchreports/RR102.pdf
Hellmuth, M., Moorhead, A., Thomson, M.C., and Williams, J. (eds) 2007. Climate Risk
Management in Africa: Learning from Practice. New York: International Research Institute for
Climate and Society (IRI), Columbia, University.
Kristof, N. (1991, May 11,). In Bangladesh's Storms: Poverty more than weather is the killer.
The New York Times. Available:
http://query.nytimes.com/gst/fullpage.html?res=9D0CE7DB1631F932A25756C0A967958260
LAL, O.P. Singh, and Prasad, O. (2007, January 1). Value addition in district level dynamical
forecast during monsoon depression and storms. Mausam (58). New Delhi: India Meteorological
Department.
Lassa, J. (2008, May) When Heaven (hardly) Meets the Earth: Towards Convergency in
Tsunami Early Warning Systems. Proceeding of Indonesian Students’ Scientific Meeting. Delft,
Netherlands.
Letson, D., Sutter, D., and Lazo, J. (2005). The economic value of hurricane forecasts: an
overview and research needs.
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Background Paper on Assessment of the Economics of Early Warning Systems for Disaster Risk Reduction
Available: http://www.sip.ucar.edu/pdf/05_Economic_Value_of_Hurricane_Forecasts.pdf
Ojo, S.O. (2003, October). Meteorological information and national development planning in
Africa: The need to interact with policy-makers and major users. WMO Bulletin, 52 (4).
Somayajulu, U.V. (2005, October 21). Cyclones in Andhra Pradesh: Damages and Response.
Paper presented at the National Seminar on Population Environment and Nexus, Population
ENVIS Project, IIPS, Mumbai.
United Nations. (2006). Global Survey of Early Warning System:, An assessment of capacities,
gaps and opportunities toward building a comprehensive global early warning system for all
natural hazards.
Venton, C., and Venton, P. (2004, November). Disaster preparedness programmes in India- a
cost benefit analysis. Network Paper 49. The Humanitarian Practice Network (HPN), Overseas
Development Institute (ODI).
Available: http://www.odihpn.org/documents/networkpaper049.pdf
Wisner, B. (2001, November). Lessons from Cuba? Hurricane Michele. London: Development
Studies Institute, London School of Economics.
Zhu, Y., Toth, Z., Wobus, R., Richardson, D., and Mylne, K. (2002, January). The Economic
Value of Ensemble-Based Weather Forecasts., Bulletin of the American Meteorological Society,
83 (1). American Meteorological Society.
Zubair, L. (2002). El Niño–Southern Oscillation influences on rice production in Sri Lanka,
International Journal Of Climatology 22, 249–260. Wiley InterScience. Available:
www.interscience.wiley.com
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Background Paper on Assessment of the Economics of Early Warning Systems for Disaster Risk Reduction
Annex G
Terms of Reference for the Paper
Consultancy Services for the Preparation of Background Papers for the
Assessment on the Economics of Disaster Risk Reduction
Global Facility for Disaster Reduction and Recovery (GFDRR)
Background
The Global Facility for Disaster Reduction and Recovery (GFDRR) /World Bank and the United
Nations International Strategy for Disaster Reduction (UNISDR) have jointly commissioned an
Assessment on the Economics of Disaster Risk Reduction (EDRR). This Assessment aims to
evaluate economic arguments related to disaster risk reduction through providing an analytical,
conceptual and empirical examination of the themes identified in the Project Concept Note. In
doing so, the findings of the Assessment are intended to influence the broader thinking related to
disaster risk and disaster occurrence, awareness of the potential to reduce the costs of disasters,
and guidance on the implementation of disaster risk-reducing interventions.
Scope of Work for a background paper on economics of early warning systems for disaster
risk reduction
Context
The 2004 Indian Ocean tsunami has highlighted the massive losses that can be incurred due to
low-frequency high-impact hazards. A similar event may have a return period of 50 to 100 years
and for each of the affected countries to put up an early warning system (EWS) to provide early
warning of such a rare event, it would be individually prohibitively costly. However, if several
countries come together, a collective system becomes economical due to the scale of operations.
If such a system also integrates warning services for high-frequency, low-impact hazards, in
other words – more common but lesser damaging events – such as heavy rainfall episodes,
floods, storms, etc. cumulatively the higher costs (relatively) would appear justifiable even more
so.
If the economic losses due to natural disasters over the last 30 years in any country are
calculated, and even by assuming that scale of the events remains the same for the next 30 years
as in the past period, due to the economic growth and accumulation of more wealth it implies
that there would be more elements at risk and greater chance of larger direct losses. So by
integrating early warning systems, the society stands to benefit.
Issues to be addressed:
The paper will address the following key issues:

Economy of Scale: What is the economy of scale, at which threshold, an early warning
system can be justified as economical- with benefits out-weighing the initial and
operational costs? Further how much would such a threshold be lowered by integrating
the more common but low impact events within such an early warning system.

Benefits of enhancing basic meteorological services: Most national meteorological and
hydrological services (NMHSs) have basic infrastructure and technical and human
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Background Paper on Assessment of the Economics of Early Warning Systems for Disaster Risk Reduction
resources to provide basic or first order services to stakeholders; some additional
marginal costs could enable the NMHSs to provide special services (such as long-lead
forecasts or location specific forecasts) resulting in several benefits. What would such
benefits be?

Institutional and community involvement: While the scientific and technical investment
is vital, a marginal investment on ensuring institutional and community involvement will
go a long way in ensuring further saving of lives and property and thus economic
benefits; while there is no doubt that this societal investment has a bearing on economic
benefits, the linkages need to be elaborated further.

Emerging and new technologies: Even in relatively advanced systems, incorporation of
emerging, new technologies, with a minimal investment that enables systems to use the
latest advances in science can result in maximizing benefits manifold. What are the new
technologies and what are the benefits that can accrue due to them?
The paper will dwell upon several case studies to illustrate and discuss the above issues.
The paper will also examine the disincentives behind countries and societies not adopting early
warning systems – ranging from unwritten thresholds (ex-India where an event with even 1,000
causalities would not merit a national disaster rating whereas even the 150 people presumed
dead in Philippines ferry tragedy has resulted in an uproar); perceptions; way of life. How could
the barriers that hinder adoption of EWS into the national frameworks be addressed?
Further more auditing of EWS in a province or a country which by itself is a very marginal
investment can help in identifying some critical gaps and how by addressing such the
constraints/ gaps, with a low investment, the returns could be very high, are also relevant topics
that would be addressed.
Outline of the paper
1. Introduction
2. Some Case Studies/ Boxes – to fit in relevant sections
3. Economy of Scale in EWS
4. Benefits of enhancing basic meteorological services
5. Benefits of fostering community and institutional involvement
6. Benefits of utilizing emerging, new technologies
7. Barriers, constraints in adoption of early warning systems
8. Benefits of EWS audit
9. Addressing gaps and barriers to derive the maximum potential benefits
10. Conclusion
Supervision
The Consultant will submit the finished products, i.e., the background papers to Apurva Sanghi,
Team Leader of the EDRR.
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