The role of National Meteorological and Hydrological Services (NMHS) in DRR
Julius N. Kabubi Kenya Meteorological Department Some recent observations on disasters • There is a Global increasing trend in the number of disasters and their total economic impacts
• 90% of these natural disasters are caused by severe weather and extreme climate events ¾A number of severe weather and extreme climate‐related events in recent years have led to disasters of devastating consequences to many societies, thus arousing even keener interest of the general public and policy makers The role of NMHS
• Weather Monitoring • Weather Forecasting • Climate prediction • Early warnings • Weather and climate Advisories • Food security • Climate Change Detection and attribution • Research and outreach programmes Other duties and responsibly NMHS
1. Establishment and maintenance of a national meteorological observation network mandatory for weather and climate observations 2. Monitoring, detection and prediction of weather and climate phenomena and dissemination of relevant products and early warnings; 3. Monitoring environmental pollution and Greenhouse Gases, including ozone 4. Exchange and transmission of meteorological data nationally, regional and internationally; 5. Carrying out meteorological training and research to improve the quality of meteorological services 6. Archival of long‐term reliable national climatologically records Regional and international • Fulfillment of the national, regional and international obligations of the Government under the Convention of the World Meteorological Organization (WMO)
• Fulfillment of the national, regional and international obligations of the Government under the Convention of the International Civil Aviation Organization (ICAO) and others
• Carrying out a Scientific assessment on Climate Agenda under the IPCC which supports country positions on the resolutions, protocols and conventions of the United Nations Framework Convention on Climate Change (UNFCCC)
• Fulfillment of such other climate and weather related national, regional and international obligations as may be directed. Other duties
Analysis of weather variables • Trends and patterns in wind regimes • Trends and patterns in Pressure systems • Intensities of rainfall and durations • Trends in temperature regimes • Local modification of weather systems • Regional influences by meso‐scale weather systems • Seasonality Generate weather products
• Nowcasting (6 hrs ahead) • Weather Forecasts • 24 hrs,4 days,7 days,14 days,1 month & Seasonal • Specialized forecasts; – Aviation – Marine – Agriculture – Food security – Water resources – Energy – Disaster management – Health and other sectors Some products6.00 –Long‐term distribution of RF
5.00
4.00
MANDERA 2000.00 MOYALE 281.80 1900.00 3.00 LODWAR 731.70 1800.00 201.20 1700.00 2.00 MARSABIT 1600.00 816.90 WAJIR 1500.00 329.10 1400.00 1.00 1300.00 ELDORET 1200.00 KAKAM 1071.40 0.00 MERU 1100.00 1962.20KISUMU NAKURU 1341.30 1000.00 1379.30KERICHO NYERI GARISSA KISII 919.30 -1.00 2005.50 959.60 393.40 900.00 2101.70 NAROK DAGORETTI 800.00 782.20 760.10 700.00 -2.00 600.00 MAKINDU LAMU 500.00 602.00 943.10 -3.00 400.00 VOI MALINDI 300.00 568.50 1074.20 200.00 -4.00 MOMBASA 1105.00
-5.00
33.00 34.00 35.00 36.00 37.00 38.00 39.00 40.00 41.00 42.00 43.00 Trends in Temperature at Dagoretti (Nairobi)
MINIMUM MAXIMUM TEMPERATURE RANGE AT DAGORETTI OBSERVED CLIMATE CHANGE SIGNALS IN KENYA : Increases in temperature
Annual Max Temp: Lodwar Annual Max Temp: Dagoretti
36.5 26.0
y = 0.0317x + 34.322 25.5 36.0 25.0 35.5 y = 0.0186x + 23.244 24.5 35.0 24.0
34.5 23.5 Temperature ( ( C) Temperature
Temperature ( ( C) Temperature 34.0 23.0 22.5 33.5 22.0 33.0 1960 1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005 Year 1960 1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 Year
Local maximum temperature trends for Lodwar(Left) and Dagoretti(Right) Observed Climate Change Signals in Kenya: Decreasing October –November – December (OND) Seasonal Rainfall Trends
Nyahururu OND rainfall trend
900.0 1200.0 800.0
700.0 Nyahururu 1000.0 Marsabit 600.0
500.0 800.0 y = -1.393x + 221.61 400.0 y = -1.3197x + 336.22
Rainfall (OND) Rainfall 300.0 600.0
200.0 400.0 100.0
0.0 200.0 1961 1964 1967 1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000 2003 2006 Year 0.0 1950 1953 1956 1959 1962 1965 1968 1971 1974 1977 1980 1983 1986 1989 1992 1995 1998 2001 2004
Narok OND rainfall trend 800.0
700.0
600.0 Narok
500.0
400.0 y = - 0.2835x + 187.34
300.0 Rainfall (mm) Rainfall 200.0
100.0
0.0 1950 1953 1956 1959 1962 1965 1968 1971 1974 1977 1980 1983 1986 1989 1992 1995 1998 2001 2004 Year Finally –Early warning Information for DRR y Weather warnings and alerts y Weather advisories y Climate forecasts and predictions y Climate advisories y Stream flow Modeling y Extreme Weather and severe climate events y Droughts Early warning (La‐nina conditions) y Floods Early warning (El Nino conditions ) y Real time flood forecasting y Extreme temperatures (heat waves) y Fog and frost
NB/ Early warning is applied throughout the disaster cycle (Preparedness, response, relief and reconstruction) Initiatives to improve on EWS for DRR
• A awareness campaign with partners (Nile IWRM‐Net, UNISDR, UNOCHA, MOSSP, NDOC and Universities) • Real time monitoring of hazards • Timely communication of information • Research work for thresh‐holding of hazards • Campaign to reach the politicians Main bottlenecks in NMHS??
• Little funding from national governments • Inadequate data observational networks and data gaps • Old equipments in some countries • Human capacity and succession management problems • Lack of awareness and poor perception by communities General Challenges of EWS y Climate change impacts 1. Immergence of disease like Rift Valley Fever (RVF) – floods 2. Highland malaria cases ‐ extreme temperatures y Lack of knowledge by public to interpret disaster indicators and thresholds y Changing societal demands and expectation of EWSs over time y Communication problems y Hazard characteristics that can change over time y Domestication of EW tools (Models that are home grown) y Lack of deterministic EW models rather than probabilistic ones y Low level of appreciation and response by community y Numerical Weather prediction and use of Radar systems is still low EWS will not work where there is;
• Lack of (poorly implemented) climate‐change policies (A national climate change master plan) • Lack of awareness/preparedness • A well developed disaster management systems and policies • Unsustainable exploitation (over‐reliance) of natural resources • Lack of relocation opportunities and procedures • Unfavourable Topography and climate • Land subdivision and poor land policies • Adverse socio‐economic attributes of risk prone communities (Primary?) – Poverty – Conflicts – High population densities – Poor traditions and customs – Unwillingness to live with risks (unwillingness to change with changing environment) Conclusions
• NMHS play major roles in DRR through provision of EW information throughout the disaster cycle
• There is need therefore to strength NMHS capacities in order to improve on their role in DRR activities
• National DRR platforms should incorporate NMHS in their planning and execution of their national agenda