1. UKRAINIAN AGRICULTURAL WEATHER RISK MANAGEMENT WORLD BANK COMMODITY RISK MANAGEMENT GROUP Ulrich Hess Joanna Syroka PhD January 20 2004 UKRAINIAN AGRICULTURAL WEATHER RISK MANAGEMENT WORLD BANK COMMODITY RISK MANAGEMENT GROUP IFC PEP Ukraine Ulrich Hess Joanna Syroka PhD January 22 2004 Developments in Flood Index Insurance COMMODITY RISK MANAGEMENT GROUP The World Bank December 2007 ERIN BRYLA Based on work by William Dick, Alex Lotsch, and Ornsaran Manuamorn

2. Flood is the major natural risk impacting GDP

3. Flood is a key risk in South East Asia

4. World Bank interest in flood  World Bank has a focus on disaster relief and reduction and one of the major issues is the risk of flood  The World Bank hotspot analysis identifies four major areas as flood prone including:  South/Central America  Southern/Eastern Europe  South East Asia  South Asia  Primary interest is in poverty reduction and agriculture is key  There is a need to expand applications of flood insurance from property to agriculture  WB is developing innovative instruments to help farmers and agricultural banks to manage flood risk  New products harness technology including flood modeling and remote sensing

5. Focus on flood modeling FLOOD MODELING FLOOD WARNING FLOOD MITIGATION INSURANCE FOR PROPERTY INSURANCE FOR AGRICULTURE FLOOD MANAGEMENT

6. Property catastrophe facility: example of Romania  WB assisting in development of Romanian Catastrophe Insurance Program Earthquake and flood National flood risk modeling and vulnerability/asset assessment Addition of cat perils to conventional property policies Source: RMSI

7. Clients for agricultural flood insurance  Micro level insurance product  Insured is the individual farmer, or a group of farmers, in homogenous risk areas  A micro product would identify flooded areas at high resolution and reduce basis risk  Challenge: can flood risk zones, and flood loss assessment, be developed to allow micro insurance product ?  Macro level insurance product  Insured is a holder of aggregate risk, e.g. agricultural bank, micro-finance organisation, or processor  Index based on wider indicator of flood, e.g. river gauge data  Aggregator sets rules for application of claims payouts

8. Example Flood Risk Management for Agriculture: Implementing Index Insurance  Challenge Design an alternative, efficient and cost-effective crop failure insurance program that facilitates risk transfer and is feasible for small farmers in low-income countries.  Index Insurance  Suited to some widespread catastrophe perils  Overcomes many problems of traditional insurance  Main shortcoming is “basis risk”  Index insurance experience to date  Mainly for drought risk (rainfall deficit index)  Micro applications - individual farmer contracts  Limited macro experience for aggregate risk transfers  Not developed yet for flood insurance

9. Index Based Flood - Work in Progress  Thailand – CRMG technical assistance - Pasco Study 2006 - Flood index – additional development in progress  Vietnam - ADB funded development project leading to macro product design for agricultural bank - CRMG collaborating for implementation of pilot  Bangladesh - CRMG - Feasibility study undertaken 2006  Technical study – CRMG and subcontractors - Product design, underwriting, loss assessment and technical requirements

10. Conceptual approach  Test parametric approach for flood  Design flood index to proxy crop losses  Harness technology to support insurance underwriting and operation  Flood Modeling (FM)  Earth Observation (EO)  Geographic Information Systems (GIS)  Design a flood index to proxy losses caused to crop  In Thailand rice has been chosen as the strategic crop most exposed to flood  Flood impact is dependent on variety, time of occurrence, depth and duration of flood water

11. Combining the Technology Components FM+EO+GISDefine flood risk zones and pricing, farm locations FM + AMM Design a flood index that proxies rice loss EO+ GISLoss adjustment for payout determination according to the index INDEX DESIGN OPERATIONAL PHASE

12. Steps in product design 1. Defining the Hazard  FM: define the flood risk zones  EO: validate FM output with archive EO imagery 2. Defining the Vulnerability  AMM: flood parameters and extent of yield loss according to crop growth phase and planting date 3. Design options for index phases and payouts  Design index thresholds, incremental payouts, limits  Economic data: Review the required insured values (production cost or output values) by crop phase 4. Pricing the index  FM: time series of flood extent and duration for each zone 5. Validating the index  Correlate against other known damage or yield data

13. Objectives of Flood Analysis  Support design of ‘mircro’ insurance scheme  Simulate historical floods  Define flood risk zones  Define critical rainfall levels  Agric. loss assessment with remote sensing

14. “High Risk” Pricing Zone “Medium Risk” Pricing Zone Pasak River LA4 LA2 LA3 LA1 LA5 “Low Risk” Pricing Zone Petchaboon Risk Zoning for Pricing and Loss Adjustment

15. Flood Detection using Satellites Land Use Stratification Surface water estimates Inundated Paddy

16. A PROTOTYPE FLOOD INDEX Payout Index Days of inundation of 60 cm. flood Yield Damage 3 daysNo damage 4 days20% loss 5 days60% loss 6 days80% loss 7 days100 % loss Claim Eligibility Trigger One time excess of “Bench Mark Level” at 115.89 cm. at the Pasak River Water Gauge station (ID: S4:B) OR 177 mm. from average 4 day rainfall at 3 stations (Upper: 379002; Middle: 379401; Lower: 379201)

17. Data Requirements: How Met Office Can Help DataNeeds (Source, years, integrity) Flood ModelingHistorical rainfall data, real time data feeds from gauges throughout catchments Agro-meteorological ModelingReliable crop models, including damage factors from flood Earth ObservationHistorical satellite data series (in order to validate the modeling), timely post event definition of extent and duration of flooding GISGeo-referencing databases of insured households to allocate to flood risk zones

18. CHALLENGES  Flood Risk Zoning --Using a flood model to zone flood risk for insurance pricing on agricultural land  Validation of Model -- Creating objective methods which are acceptable for international risk transfer to reinsurers (e.g. river gauges, or earth observation)  Modeling Flood Risk -- Complexity of flood risk and flood modeling (inundation flood, cyclone/coastal flood, flash flood…)  Digital Terrain Data  Hydro-meteorological and streamflow data  Computation and Model Choice  Meet needs for flood insurance for agriculture  Could provide insurance to smaller farmers and businesses  Could support objective disaster payments outside formal insurance  Settlement could made on an objective trigger (EO)  The cost of reaching farmers drops significantly BENEFITS