How Risk Identification is Linked to Early Warnings? Tana River Example A Collaborative Study USGS, WFP, IRI and FEWS NET Hussein Gadain Regional Hydrologist, U. S. Geological Survey Famine Early Warning Systems Network – Nairobi, Kenya Symposium on Multi-Hazard Early Warning Systems for Integrated Disaster Risk Management, WMO 23-24 May 2006 September 20, 2018September 20, 2018

Background Kenya is drought prone and reasonably prepared for drought emergencies. However, Kenya is also prone to very serious flood risks especially in the lowlands in northeastern Kenya particularly; Garissa, Ijara and Tana River districts, the areas surrounding Lake Victoria and Nairobi. El Niño - related floods of 1997/98 necessitated massive relief operation, catching Kenya unprepared, despite warning of strong El Niño event.   Symposium on Multi-Hazard Early Warning Systems for Integrated Disaster Risk Management, WMO 23-24 May 2006 September 20, 2018September 20, 2018

Gaps, Needs and Challenges Institutional Lack of institutional framework to monitor and manage flood disasters Weak/No flood disaster management policies Lack of political commitment Data Weak data collection, archiving, and management systems Lack of hydrometeorological data and up to date instruments (old systems, no telemetry technology) Financial Lack of funds and technical capacities for maintaining observational networks Data are still in paper form !! No suitable hardware for handling data?? Communication of EW and Risk Information No public awareness and education Research Symposium on Multi-Hazard Early Warning Systems for Integrated Disaster Risk Management, WMO 23-24 May 2006 September 20, 2018September 20, 2018

Challenges to be addressed Institutional challenges including inter-disciplinary and participatory approaches for risk and EW identification Financial & economic challenges Technical challenges Data (gaps, sharing, capacity building & training) Use of new technology for dissemination of Risk and EW information (Web, e-mail, SMS, etc.) Community participation (involvement of the people affected in risk identification and EWS development) Symposium on Multi-Hazard Early Warning Systems for Integrated Disaster Risk Management, WMO 23-24 May 2006 September 20, 2018September 20, 2018

Case study Once the water overflows the final dam in the Tana River reservoir series, Garisa, downstream, floods three days later. By monitoring the stream flow level before the dam, the lead time can be increased by running the model and forecasting the inundated area. Tana River source of 75% of Kenya’s power La Nina 1999-2000 droughts: water shortages severe cutbacks in power generation, power rationing and blackouts estimated losses $2 million/day Flooding in June 2003 displaced an estimated 10,000 people Symposium on Multi-Hazard Early Warning Systems for Integrated Disaster Risk Management, WMO 23-24 May 2006 September 20, 2018September 20, 2018

WFP provides relief to flood victims, so understanding how the flooding affects livelihoods will improve flood assistance programming In the longer term, WFP wants to know how yield losses will affect people 2-3 months into the future which could affect food aid needs The results are in press and will be published by the World Bank in the global natural disaster risk hotspots report, Vol II-case studies Chapter 6 (keep an eye) Gadain, H. M., Bidault, N., Stephen, L., Watkins, B., Dilley, M., and Mutunga, N,. Reducing the Impacts of Floods through Early Warning and Preparedness: A Pilot Study for Kenya, World Bank Report on Identification of Global Nature Disaster Risk Hotspots, Volume II – Case Studies (in press). Symposium on Multi-Hazard Early Warning Systems for Integrated Disaster Risk Management, WMO 23-24 May 2006 September 20, 2018September 20, 2018

Inputs and Assumptions Digital Elevation Model (USGS, RCMRD) Livelihood baselines (WFP, FEWS & GoK) Stream flow modeling Inundated areas (USGS) Contingency plans Assumptions: The worst floods (floods associated with El Niño in 1997/1998) come only once every 30 – 40 years; Moderate flooding, which can also have a severe impact occurs every 5 to 7 years. Symposium on Multi-Hazard Early Warning Systems for Integrated Disaster Risk Management, WMO 23-24 May 2006 September 20, 2018September 20, 2018

Moderate flood Villages People Area Km2 51 47,000 4,612 Severe flood 73 70,000 5,377 Symposium on Multi-Hazard Early Warning Systems for Integrated Disaster Risk Management, WMO 23-24 May 2006 September 20, 2018September 20, 2018

Impacts on livelihoods We assessed risks of worst case flood impacts on livelihoods using the El Niño flooding event of 1997–98, with an estimated 35-year return period, as a scenario. The impact of floods on populations differs depending on their livelihoods and wealth group. Among the different livelihood groups in both districts, the ones most exposed to flooding are pastoralists, agro-pastoralists, and the dry riverine and Tana Delta livelihood systems Symposium on Multi-Hazard Early Warning Systems for Integrated Disaster Risk Management, WMO 23-24 May 2006 September 20, 2018September 20, 2018

Impacts of prices increase and loss of livestock by district Livelihood zones affected: Pastoralists, Agropastoralists, dry riverine communities, Tana delta communities In the pastoralist, agro-pastoralist and Dry riverine areas, the main source of income is from livestock production Worst affected = pastoralists:68% of total cash income comes from livestock Second most affected = Dry Riverine: Maize accounts for up to 50% of total food consumption Loss of maize crops due to floods will have a major impact Limited availability because of access and cost. Substantial price increases after El Niño Symposium on Multi-Hazard Early Warning Systems for Integrated Disaster Risk Management, WMO 23-24 May 2006 September 20, 2018September 20, 2018

Contingency Plan -- worst case scenario WHO? Likely target groups and areas may be pastoralist and dry riverine communities WHAT? Type of emergency assistance El Nino flood impact assessments showed that relief food enabled pastoralists to save their remaining livestock and to start rebuilding herds and livelihoods. HOW? Air drops, boat, NGO coordination on the ground HOW MUCH? Costs to air drop and deliver food by boat to 70,000 people? Symposium on Multi-Hazard Early Warning Systems for Integrated Disaster Risk Management, WMO 23-24 May 2006 September 20, 2018September 20, 2018

Lessons Learnt Risk identification offers potential for improving early warning systems and consequently risk management (better contingency planning). Risk Identification involves Hydrometeorological, Physical and, Socio economic data and, Expertise Symposium on Multi-Hazard Early Warning Systems for Integrated Disaster Risk Management, WMO 23-24 May 2006 September 20, 2018September 20, 2018

Recommendations Maintain and extend our ability to collect, analyze, and disseminate data related to risk identification and early warning. In the case of extremes, such data has a demonstrable relationship to decision making. Continue efforts to develop risk identification tools and methods, including development and implementation of flood models at local, regional and global scales. To enhance the linkage between risk identification and early warning, support existing organizations and groups such as National Meteorological and Hydrological Services Departments, and Regional Centers. This linkage shall also includes activities such as this workshop and other activities which bring together scientists and all stakeholders. Symposium on Multi-Hazard Early Warning Systems for Integrated Disaster Risk Management, WMO 23-24 May 2006 September 20, 2018September 20, 2018