Elements for Life

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analyzed by remote sensing data, Adv. Space Res., 33(3), 268-273, 2004
6. Ouzounov D., N. Bryant, T. Logan, S. Pulinets, P.Taylor Satellite thermal IR phenomena
associated with some of the major earthquakes in 1999-2004, Physics and Chemistry of
the Earth, 31,154-163, 2006
7. Pulinets et al., 2005b
8. Pulinets et al, 2006.
Satellite remote sensing for early warning of food security crises
1. Skole, D. L. (2004) ‘Geography as a great intellectual melting pot and the preeminent
interdisciplinary environmental discipline’. Annals of the Association of American
Geographers, 94, 739-743
2. FEWS (2005) Famine Early Warning System Network Home Page. USAID FEWS NET
3. Dilley, M. (2000) Warning and intervention: What kind of information does the response
community need from the early warning community? Washington DC,
USAID, Office of US Foreign Disaster Assistance 2000
4. FEWS (2000) Framework for food crisis contingency planning and response. Arlington, VA,
FEWS-ARD
5. Ibid; Dilley, M. and Boudreau, T. E. (2001) ‘Coming to terms with vulnerability: a
critique of the food security definition’. Food Policy, 26, 229-247
6. Waymire, E. (1985) ‘Scaling limits and self-similarity in precipitation fields’. Water
Resources Research, 21, 1271-1281; Xie, P. & Arkin, P. A. (1997)
‘Global Precipitation: A 17-year monthly analysis based on gauge observations, satellite
estimates, and numerical model outputs’. Bulletin American Meteorological Society, 78,
2539-2558
7. Tucker, C. J., Newcomb, W. W., Los, S. O. and Prince, S. D. (1991) ‘Mean and Inter-
Annual Variation of Growing-Season Normalized Difference Vegetation Index for the
Sahel 1981-1989’. International Journal of Remote Sensing, 12, 1133-1135
8.
9. Mathys, E. (2005) FEWS NET’s Approach to Livelihoods-Based Food Security Analysis.
Washington DC, FEWS NET USAID.
Unjust waters: climate change, flooding and the protection of poor
urban communities in Africa
1. This article is an edited version of the research report ‘Unjust Waters’ (ActionAid, 2006.
Download at ), and highlights findings from three cities from the study,
Accra in Ghana, Maputo in Mozambique and Kampala in Uganda. The other cities in the
study are Freetown (Sierra Leone), Maputo (Mozambique) and Nairobi (Kenya). Policy
analysis was also carried out to understand whether there is a gap between poor urban
people’s experiences of climate change impacts and current disaster management
policies. ActionAid International works with more than 13 million individuals across
Africa, Asia, Latin America and the Caribbean through some 2,000 civil society
partners. For more information, visit or contact
2. IPCC Working Group II, 2001, Impacts, Adaptation and Vulnerability: Chapter 10 ‘Africa’,
Cambridge University Press, Cambridge.
3. Nicholls R, Hoozemans F and Marchand M, 1999, Increasing flood risk and wetland
losses due to global sea-level rise: regional and global analyses. Global Environmental
Change 9, S69-S87
4. Jallow B, Toure S, Barrow M and Mathieu A, 1999, ‘Coastal zone of the Gambia and
the Abidjian region in Cote d’Ivoire: sea level rise vulnerability, response strategies
and adaptation options’. Climate Research Special Issue 6, 137-143
5. IPCC Working Group II, 2001
6. Signed by 168 countries at the World Conference for Disaster Reduction, Kobe, Jan
2005. See
Adapting to climate change through resilience to natural disasters
1. Ministry of Environment and Forests, Government of India, (2004) India’s Initial
National Communication to the United Nations Framework Convention on Climate
Change, pp 266. )
2. Goswami, B. N., V. Venugopal, D. Sengupta, M. S. Madhusoodanan, Prince K. Xavier,
(2006) Increasing Trend of Extreme Rain Events Over India in a Warming Environment,
Science, 314, 1442-1445.
3. Kelkar, R. R., (2005) Understanding the Extreme Weather Events, IWRS Newsletter,
November 2005.
Climate, man and forest fires
1. Goldammer, J.G. and Mutch, R. W. (2001) FRA 2000. Global Forest Fire Assessment 1990-
2000. FAO Forestry Department. Forest Resources Assessment Programme. Working
Paper 55. Rome 2001.
2. Chandler, C., Cheney P., Thomas P., Trabaud L., Williams D. (1983) Fire in Forestry
(Vol.I) John Wiley & Sons, New York.
3. Viegas, D.X. (2005) ‘A Mathematical Model for Forest Fires Blow-up’, Combustion Science
and Technology , 177: 27-51.
4. Pyne, S. J., P. Andrews and R. D. Laven (1996) Introduction to Wildland Fire (Second
Edition) - Ed. John Wiley & Sons, New York.
IV
ENVIRONMENT
The African Monitoring of the Environment for Sustainable Development Initiative: a
timely initiative to save an endangered continent
1. About EUMETSAT: EUMETSAT is responsible for operating Europe’s weather satellites
in geostationary and polar orbits, and for delivering satellite data, services and products
to the European National Meteorological Services, research and training institutions and
other end users. EUMETSAT currently has 20 Member States and 10 Cooperating States
and works in close collaboration with the World Meteorological Organization (WMO),
the European Union, the European Space Agency and other European and international
partners, industrial companies and space agencies. In addition to AMESD and PUMA,
EUMETSAT contributes to other strategically important projects such as the European
Global Monitoring for Environment and Security (GMES) initiative, the Global Earth
Observation System of Systems (GEOSS) and the WMO’s World Weather Watch and
Global Observing System.
Climate information applications for sustainable development in Africa
1. FEWS 1998: USAID-financed Famine Early Warning System. January 28, 1998, AFR/98-
01, 8 pp
2. Hammer, G. L., 2000: ‘A general system approach to applying seasonal climate forecasts’.
Applications of seasonal climate forecasting in agricultural and natural ecosystems: the Australian
experience. Atmospheric and Oceanographic Sciences Library, vol. 21
3. ICPAC, 2005: ‘Media Workshop Report’. Fifteenth Greater Horn of Africa Climate Outlook
Forum (GHA-COF15), Mombasa, Kenya. 19pp
4. Linthicum K.J., Anyamba, A, Tucker C J., Kelly P. W., Myers M. F., Peters C. J., 1999:
‘Climate and satellite indicators to forecast Rift Valley Fever’. Science 285:397-400
5. ICPAC, Op. Cit.
V
ASSESSMENT METHODOLOGIES
Methodologies for assessing the economic benefits of National Meteorological and
Hydrological Services
1. Lazo, J.K. and L.G. Chestnut. 2002. Economic Value of Current and Improved Weather
Forecasts in the U.S. Household Sector. Prepared for Office of Policy and Strategic Planning,
NOAA. November 22.
2. Teisberg, Thomas J., Rodney F. Weiher, and Alireza Khotanzad. 2005. ‘The Economic
Value of Temperature Forecasts in Electricity Generation’, Bulletin of the American
Meteorological Society 86, 12, 1765–1771.
3. Ebi, Kristie L., et al., 2004. ‘Heat Watch/Warning Systems Save Lives: Estimated Costs
and Benefits for Philadelphia 1995–1998’, Bulletin of the American Meteorological Society,
85, 8, 1067–1073.
4. The authors thank Charles S. Colgan for helpful discussions.
All economic sectors, regions, and individuals on Earth are affected by weather.
In any application areas, a better understanding of these interactions could enhance
personal safety, reduce property damage, and increase economic efficiency, saving
multiple lives and millions of dollars each year. If we are to realize these potential
benefits, we need to thoroughly understand how individuals and socioeconomic
sectors do and could use different types of weather information.
To learn more about work toward this goal, visit NOAA’s Economics & Social Science
(NESS) Web site at www.economics.noaa.gov. As an agency, NOAA is focused on the
earth’s physical sciences, but recognizes that interactions between earth science and
social science are vital to its ultimate goal – giving users what they need. The NESS
programme and Web site is part of NOAA’s Office of Program Planning and Integration
(PPI). Another valuable resource can be found at NCAR, with
funding from the US Weather Research Program, established the Collaborative
Program on the Societal Impacts and Economic Benefits of Weather Information (SIP)
to create a dedicated focal point for assembling, coordinating, developing, and
synthesizing research and information on the societal impacts and economic benefits
of weather information.
Moving from hindsight to foresight: a challenge in the application of valuation research
1. This is acknowledged, among other places, in Shapiro, M A and A J Thorpe,
THORPEX International Science Plan Version III, International Science Steering
Committee, 2004.
2. Mills, B. Decision Support Systems: Considerations for THORPEX SERA. Background theme
paper for the workshop on North American THORPEX Societal and Economic Research
and Applications, Boulder, CO, August. 2006. 14-16.
3. Katz, R.W. and A.H. Murphy. Economic Value of Weather and Climate Forecasts. Cambridge
University Press, New York. 1997. 222 pp.
4. See, for example, Jochec, K.G., J.W. Mjelde, A.C. Lee, and J.R. Conner. ‘Use of seasonal
climate forecasts in rangeland-based livestock operations in West Texas’, Journal of
Applied Meteorology, 40(9). 2001.1629–1639; Fox, G., J. Turner, and T. Gillespie. ‘he
value of precipitation forecast information in winter wheat production’ Agricultural and
Forest Meteorology, 95. 1999. 99-111.
5. Roulston, M.S., D.T. Kaplan, J. Hardenberg and L.A. Smith, ‘Using medium-range
weather forecasts to improve the value of wind energy production’, Renewable Energy , 28.
2002. 585-602.
6. For example: Ebi, K.L., T.J. Teisberg, L.S. Kalkstein, L. Robinson and R.F. Weiher. ‘Heat
watch/warning systems save lives: Estimated costs and benefits for Philadelphia 1995–98’,
Bulletin of the American Meteorological Society, 85(8). 2004. 1067–1073.
7. Gunasekera, D., G. Mills, N. Plumier, T. Bannister, L. Anderson-Berry, M. Williams, A.
González-Cabán, and J. Handmer. Economic Value of Fire Weather Services . BMRC Research
Report No. 112, Bureau of Meteorology Research Centre, Melbourne. 2005. 61pp.
8. Keith, R. ‘Optimization of value of aerodrome forecasts’, Weather and Forecasting , 18(5)
2003. 808–824; Smith, K. and S.D. Vick. Valuing weather radar benefits for winter road
maintenance: a practical case example, Meteorological Applications,1. 1994. 103-115;
Stewart, T.R., R. Pielke Jr., and R. Nath. ‘Understanding user decision making and the
value of improved precipitation forecasts’, Bulletin of the American Meteorological Society ,
85(2). 2004. 223-235.
9. Hamlet, A.F., D. Huppert, and D.P. Lettenmaier. ‘Economic value of long-lead streamflow
forecasts for Columbia River hydropower’, Journal of Water Resources Planning and
Management, 128. 2002. 91-101.
10. Rollins, and J. Shaykewich. ‘Using willingness-to-pay to assess the economic value of
weather forecasts for multiple commercial sectors’, Meteorological Applications, 10.
2003. 31-38; Lazo, J.K. and L. Chestnut. Economic value of current and improved weather
forecasts in the US household sector. Stratus Consulting, SC10050. 2002. 21 pp; Brown,
J.S. Valuation of weather forecast services: discrete choice and CVM approaches. MSc. thesis,
University of Guelph, Guelph, Canada. 2003.
11. Vodden, K. and D.A. Smith. Valuing Meteorological Products and Services: Case Study of the
National Radar Project. Report prepared for the Adaptation and Impacts Research Group,
Meteorological Service of Canada. Applied Research Consultants, Ottawa, Canada.
2003. 20 pp.
12. Cavlovic, A., J. Forkes and K. Rollins. The Economic Value of Environment Canada’s
Weatheradio Service for Users in Maritime Communities of Atlantic Canada. Department of
Agricultural Economics and Business, University of Guelph. 1997.
13. As noted in Morss, R.E., J.K. Lazo, H.E. Brooks, B.G. Brown, P.T. Ganderton and B.N.
Mills. ‘Societal and Economic Research and Application Priorities for the North American
THORPEX Program’, Bulletin of the American Meteorological Society. Submitted. 2007.
14. E.g., Dempsey and Fisher, 2005; Cohen et al., 2006
15. For example, Environment Canada’s Climate Change Impacts and Adaptation Research
Network consist of federal scientists working in academic institutes and faculties; also
Environment Canada is in the process of developing an Environmental Prediction
Strategy
16.