Background Paper on
Assessment of the Economics of Early Warning Systems
for Disaster Risk Reduction[1]
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
Facilitated by the AsianDisasterPreparednessCenter
1 December 2008
Background Paper on Assessment of the Economics of Early Warning Systems for Disaster Risk Reduction
Contents
Executive Summary...... v
1.Introduction and Methodology
1.1 Introduction
1.2 Methodology for Quantification of Benefits of EWS
2.Case Studies on Cost-Benefits of EWS
Case Study 1: Sidr Cyclone, November 2007, Bangladesh
2.1 Group 1:
Case Study 2: 2003 Floods, Sri Lanka
2.2 Group 2:
Case Study 3: Bangladesh Floods
2.3 Group 3:
Case Study 6: 2006 Floods (July – September) Thailand
2.4 Group 4:
Case Study 5: Climate Forecast Applications- Philippines (2002-2003 El Niño)
Case Study 6: India Drought 2002
2.5 Category 2: Geological Hazards (e.g. Tsunami)
Case Study 7: Regional Integrated Multi-Hazard Early Warning System (RIMES)
3.Non-Market Factors
3.1Factors Influencing Adoption of EWS at Government or Institutional Levels
3.1.1 At policy level
3.1.2 At political level
3.1.3 At technical institutions...... 45
3.1.4 At the community level...... 47
3.2Incentives for EWS...... 48
Annex A: Methods of Calculating Flood Damage Reduction due to Early Warning
Annex B: Basic Services vs. Value-Added Services
Annex C: Avoidable Damage for Various Sectors – Perception of Small Farmers in Bangladesh
Annex D: Additional Case Studies
Annex E: Climate Field Schools in Indonesia...... 64
Annex F: List of References
Annex G: Terms of Reference for the Paper
1
Background Paper on Assessment of the Economics of Early Warning Systems for Disaster Risk Reduction
Figures
1.Flood affected areas – Sri Lanka, May 2003
2.Historical flood event: extent and crop damage
3.Area under production: major crops
4.Cereal production (1972-2001)
5.Improvement in forecast lead time due to CFAB technology, Bangladesh
6.June-July rainfall (1993-2002)
7.RIMES Member Countries
8.Integration of tsunami and hydro-meteorological subsystems
9. Integration of tsunami and hydro-meteorological subsystems: common elements
10. Integration of tsunami and hydro-meteorological subsystems: human resource
11. Integration of tsunami and hydro-meteorological subsystems: human resource
12. Addressing various gaps in an end-to-end early warning framework
13. Central Water Commission (CWC) of Government of India...... 46
Boxes
1.Benefits of adopting early warning systems
2.Benefits of fostering community and institutional involvement
3.Climate forecast applications in Bangladesh,flood forecasting technology
4.Institutional responses to the July 2007 flood forecasts in Bangladesh
5.Forecasting technology options & avoidable damages
6.Possible measures that could have led to reduction of impacts of 2002 drought
7.Agro-meteorological station in Dumangas Municipality, Iloilo Province...... 43
8.Bird flu claims first Thai victim...... 44
9.August 2003 heat wave in France...... 44
Tables
1.Case study findings on cost-benefits of EWS
2.Application of lead time for agriculture
3.Decision table- probabilistic forecast information
4.Damage reduction due to early warning of different lead times
5.Summary of damage and losses – Cyclone Sidr
6.EWS costs for Bangladesh Sidr Cyclone
7.Identifying EWS benefits for Bangladesh Sidr Cyclone
8.Quantifying EWS benefits for Bangladesh Sidr Cyclone
9.EWS costs for Sri Lanka
10.Avoidable damage in two of the five districts affected – 2003 floods, Sri Lanka
11.Estimated avoidable damage fromfloods in Sri Lanka, last 3 decades
12.Return period of floods
13.Major floods affecting Bangladesh in last five decades
14.Quantifying benefits: July-Aug 2007 Floods
15.Estimated avoidable damage for floods in Bangladesh, last 3 decades
16.Potential impacts in food and agriculture sector due to various floods
17.Actions for utilizing improved flood forecast information
18.Agricultural risk management options in case of 10 to 15 days early warning
19.2006 Thailand Floods –summary of damages and losses
20.Estimates of cumulative coverage under rice, Orissa 2002
21.Crop damages as per state report, Orissa 2002
22.Crop production losses due to drought, India 2002-2003
23.Impacts of some severe cyclones (1977 to 2006) in Andhra Pradesh
Abbreviations
ADB / Asian Development BankADPC / AsianDisasterPreparednessCenter
BDT / Bangladesh Taka
BMG / Meteorological and Geophysical Agency, Indonesia
CBO / Community-Based Organization
CFA / Climate Forecast Applications
CFAB / Climate Forecast Applications in Bangladesh
CWC / Central Water Commission
DAE / Department of Agricultural Extension
DITLIN / Directorate for Crop Protection, Indonesia
DoM / Department of Meteorology, Sri Lanka
ECMWF / European Centre For MediumRange Weather Forecasting
EDRR / Economics of Disaster Risk Reduction
ENSO / El Niño Southern Oscillation
EWS / Early Warning System
FFWC / Flood Forecasting and Warning Centre
GDP / Gross Domestic Product
GFDRR / Global Facility for Disaster Reduction and Recovery
IMD / India Meteorological Department
INR / Indian Rupee
IOC / Intergovernmental Oceanographic Commission
ICG / Intergovernmental Coordination Group
IOTWS / Indian Ocean Tsunami Warning and Mitigation System
IPB / Bogor Agricultural University, Indonesia
IRI / International Research Institute for Climate and Society
MAO / Municipal Agriculture Office
MM5 / Meso-scale Model 5
MT / Metric ton
NIA / National Irrigation Administration
NLM / Northern limit of monsoon
NMHS / National Meteorological and Hydrological Services
NWMP / National Water Management Plan
NWP / Numerical Weather Prediction
NWRB / National Water Resources Board
OFDA / Office of U.S. Foreign Disaster Assistance
PAGASA / Philippine Atmospheric, Geophysical and Astronomical Services Administration
PAO / Provincial Agriculture Office
RIMES / Regional Integrated Multi-Hazard Early Warning System
SLR / Sri Lankan Rupee
TMD / Thailand Meteorological Department
UNESCO / United Nations Educational, Scientific, and Cultural Organization
UNISDR / United Nations International Strategy for Disaster Reduction
USAID / United States Agency for International Development
USD / United States Dollar
VND / Vietnamese Dong
WRF / 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 ina) 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.
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 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 benefitsover a ten-year period may be realized.
Sri Lanka,
May 2003 floods case study /
- 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 thatfor 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).
Vietnam,
2001-2007 hydro-meteorological hazards case study /
- Increased lead time as well as accuracy due to incorporation of the advanced Weather Research Forecasting (WRF) model run at much higher resolutions could helpreduce 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 realizea return of USD 10.4 in benefits.t
Bangladesh,
2007 Flood case study /
- 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 WarningCenter 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.
Thailand,
2007 Flood case study /
- 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.
Indonesia,
Seasonal forecasting case study /
- 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.
Philippines,
Seasonal forecasting case study /
- 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.
Sri Lanka,
Seasonal forecasting case study /
- 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.
India,
2002 Drought case study /
- 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.
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 communicationand processing facilities and human resources). An integrated or end-to-end approach, addressing downscaling of forecast informationand 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 southernThailand 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, 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, IloiloProvince 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.