Enhancing the benefits of Remote Sensing Data and Flood Hazard Modeling in Index-based Flood Insurance (IBFI) for the rural farmers in South Asia

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Enhancing the benefits of Remote Sensing Data and Flood Hazard Modeling in Index-based Flood Insurance (IBFI) for the Rural Farmers in South Asia

Giriraj Amarnath, Ph.D.Senior Researcher and Project Lead - IBFIInternational Water Management Institute (IWMI), Colombo

PRESENTATION OUTLINEHuman cost of natural disasters and its impactFlood situation in BiharLinking Disaster Management and Index InsuranceIBFI Concept, Implementation StrategyProject Progress and Updates from team membersPilot area selectionFlood hazard modelingImplementation processBusiness model and institutional frameworkCommunication and IPPartnership for Implementation

HUMAN COST OF NATURAL DISASTERS AND ITS IMPACTS

Number of disasters and affected people reported per country (1994-2013)

IND819mPK50mBGL127mGlobal Assessment by natural disasters

Source: EMDAT (2015)3

BIG FACTS ON FLOODINGBihar is Indias most flood-prone state.73% of the total geographical area is annually flooded.76% of the population in North Bihar is at risk of flooding.Major flood events have occurred in 1987, 1995, 1998, 2002, 2004 and 2007.Approx. 15million people are affected by floodingApprox. 300,000 metric tons of rice production damaged by floodsFor example Muzaffarpur District, alone, incurred losses of over USD 3 million per year from 2001 to 2012 due to floods.Recent report by UNISDR about 800 million people are currently living in flood-prone areas, and 70 million are experiencing floods each year.Global flood losses in 2011 >$100 billion with major losses from Thailand, Australia and Hurricane IreneRecent Overseas Development Institute (ODI), UK estimates to over $450 billion by 2030In 2007, floods killed 3,200 people in India and Bangladesh alone. In 2010, flooding killed 2,200 people in Pakistan and another 1,900 in China, while in 2013, an exceptionally high number of 6,500 people died due to floods in India.BiharGlobal to Regional

HISTORICAL FLOOD TRENDS IN GANGES BASIN

Flood FrequencyFlood DurationFlood Seasonality

Flood Extent

RECENT FLOOD DISASTERS IN BIHAR

Photo credit: Amit Kumar

OBSERVING FLOOD DISASTERS FROM SPACE1988 TO 2014 HISTORICAL IMAGES

THE ROLE OF DISASTER MANAGEMENT

Short-term emergency assistanceEmergencyResponseRecoveryReconstruction &RehabilitationLong-term infrastructure & sustainable development assistance

Liquidity GapXCatastropheTimehttp://www.wmo.int/pages/prog/drr/events/Barbados/Pres/4-CCRIF.pdf

PROMOTING PREPAREDNESS AND INCREASING RESILIENCE

Funds for emergency aidFunds for resilience building

The TradeoffSignificant amount of money locked in disaster relief funds

Funds for emergency aid insurance premium Funds for resilience building

The TradeoffInsurance payoutInsurance can help unlock the money that is kept for relief and use it for climate change adaptation and mitigationRole of Insurance

REDUCING THE FLOOD RISKSource: Jha et al, (2011) adapted from Ranger and Garbett-Shiels (2011)

Cost-Benefit-RatioRobustness to Uncertainties

StructuralNon-Structural

PORTFOLIO OF FLOOD MANAGEMENT OPTIONS

InvestmentEffectiveness

Acceptable risk level

IBFILevee

Storages

DiversionsEarly warningReseroir Opn.MULTIPLE BARRIER PROTECTION

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20132013-142011-12SHORT HISTORY OF IBFI2015-18

CCAFS CRP Refresh Phase Flood Risk SA

ACTIVITY

IBFI Concept Initiated and Approved

Multi-hazard Mapping SA

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PROJECT SUMMARYCCAFS Project ID: P41-F2-SA-IWMIPeriod: 2015 to 2018Budget: USD 1.0 Million (approx.)Target Countries: India & BangladeshResearch Partners: CGIAR: IWMI (HQ, ND), IFPRI-New DelhiInternational: UoB, MCII, GlobalAgRisk, UNOOSA ++India: ICAR-IIWM, NIH, FMISC, CWC + +Bangladesh: IWM, MoDM, BWDB, UoD + +Implementing/Co-sponsoring Partners:AIC, eeMausam, BajajAllianz, SwissRe, DOA-BH, NABARD ++Knowledge Sharing Partners:FMISC, BSDMA, CWC, DoA-Bihar ++

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Partners

CGIAR Partners + DonorsKnowledge PartnerGovt. + Technical Partner + InsurerCommunication, Media Partner

INDIABANGLADESH

RESEARCH OBJECTIVES

Setting up pilot-scale trials to demonstrate that positive verifiable impacts emerge from IBFI in terms of agriculture resilience and improving productivity, and household incomes, locally and at the broader scaleDeveloping tools and strategies that support IBFI development and upscaling, integrated with existing and future flood control measures.

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Goals are essentially:Develop a strong proof of concept of UTFI: technical, economic & institutionalFacilitate opportunities for scaling up in the most prospective regions14

Flood index designFlood hazard moduleFlood loss module

CropYieldlossEconomic lossWater levelFlood ExtentFlood DurationCrop DamageRainfall Insurance payout Structure/SchemeGovernment Insurance agenciesDevelopment banks

Farmers(from 50,000 to 1 million farmers would be benefitted by the scheme)

OutputInput, Modeling and analysisUsersFinal beneficiariesRemote Sensing Data for Inundated Crop AreaIBFI CONCEPT

CONCEPT: INDEX BASED FLOOD INSURANCE

Peoples Participation

Flood map

Scaled for Depth

Scaled for Duration

Final IndexMapFlood Indexing Concept

Flood Hazard ModelFlood Loss Model Flood Insurance PolicyPartner: IWM

Proof-of-concept on IBFI coupled with the flood hazard model and remote sensing (RS) data in selected districts of South Asian countries.Digital flood mapping tool to monitor and quantify the impact of floods on crops, and its application in insurance schemes.Design and pilot test a set of farmer-friendly flood insurance contracts for at least three districts with a considerable number of marginalized female farmers/poor people to ensure contracts are not gender biased.Obtaining feedback and develop community of practice from officials/staff of insurance regulatory authorities in countries, operating insurance companies, meteorological agencies, agricultural research institutions, micro-finance institutions or NGOs, and relevant government agencies (e.g., ministries involved with disaster management, water resources, and agriculture). Policy and institutional guidelines translated into business models for the implementation of flood insurance product.Comparative analysis of the cost-effectiveness of RS-based index insurance compared to traditional methods, and estimating the potential in other parts of the target countries.Research papers and reports, planning guidelines, policy/investment briefs and other communications material including websites, brochures and videos.

MAJOR DELIVERABLES

Inception Workshop on Enhancing the benefits of Remote Sensing Data and Flood Hazard Modeling in Index-based Flood Insurance (IBFI)1 2 August 2015, Hotel Gargee Grand Patna, Bihar (India)

COMPONENTSMETHODSIBFI system planning, design, implementation and evaluationsite characterization, design, pilot-scale implementation, baseline data, performance monitoring and testing, hydrologic/hydraulic modeling, flood parameters, and scenario analysis/ forecasting, training & capacity buildingInstitutional, economic and gender analysis baseline socio-economic data, gender/social disaggregated analysis, social/institutional/ policy arrangements, cost-benefit analysisTechnical guidelines and business case developmentsynthesis; cross-country comparisons, IBFI vs alternative mitigation approachesStrategy development and disseminationknowledge exchange meetings/dialogues/ regional workshops for key stakeholders and potential investors, investment support tools; risk management framework

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Site Prioritization Analysis: Hydrological Data

Flood frequencyAnalyzed long-term historical rainfall data from IMD in different districts of Bihar Prepared inundation map from MODIS and calculated flood extent, duration, frequency and compared with data from FMIS, BDMA and other reportsRiver flow information from altimeter and in-situ measurements

Rainfall

20

Exposure of Floods

Potential Insurance Demand

Potential Insurance Demand

Identification of Pilot Location

PILOT STUDY AREA

MUZAFFARPUR DISTRICT(BIHAR, INDIA)SIRAJGANJ DISTRICT(BANGLADESH)

To provide operational rainfall-runoff model from MISDc and hydraulic model using MIKE 11 in the process of flood forecasting;To integrate past, current and future weather information to provide probability of flood levels in early warning process and its application in flood insurance;To provide geospatial products including flood extent, flood depth and flood duration and possible provide composite flood index map;To provide tools and models with detail technical report and possibly a research paper in 2016.DELIVERABLESOperational flood forecasting system

Input observationsRainfall-Runoff Modelling (MISDc)Rainfall and temperature time series, in near real-rime, by in situ stations, remote sensing, numerical weather prediction modelling (for future prediction, 2-5 days ahead)Soil (geological) and land use mapsHydrodynamic modelling (MIKE 11)Cross section geometryInfrastructure (bridge, reservoir, storaging structure)Digital Elevation Model (DEM)Field information (pictures)Calibration and testing of modelling systemDischarge/flow data (by in situ stations)Flood maps (by field surveys or remote sensing)

The system needs hydrometeorological data (rainfall and air temperature) as input. The soil/land use/topographic information are used for the determination of hydrologic/hydraulic model parameter values.Finally, discharge data and flood maps are required for model testing and validation.

Operational In-situ Water level for Pilot Districts

Data Provided by Central Water Commission Daily water level, rainfall35 stations are operational for 2015 flood seasonsBuxar Station

Hayaghat Station

Flood mapping

Every day, the flood forecasting system will provide flood inundation maps for the region of interest from which the water depth, flow velocity, and flood duration for each location (e.g., village) will be automatically extracted.

Index Based Flood Insurance

Design Index Based Flood Insurance

Framework of Designing IBFI

Implementation Process

IBFI Impact PathwayInnovation IBFI productValidation-DisseminationAdoption-AdaptationIMPACTFarmers and their familiesMarket, Private Insurance firmsGovernment (Disaster mgmt, Agri, water )

IndicatorsPotential effectiveness of flood thresholds and loss estimationAdoption Rate Communication and Uptake Activities Engage

Educate

Exemplify

Actual effectiveness of flood thresholds and loss estimationAnd Change in knowledge, attitude, skills and practice in usersBaseline analysis of the context and issues

Pilot implementationAnd Strength of Intermediaries

IBFI Pilot Implementation Increasing Agriculture Resilience Reducing Risk to Livelihoods

ACTORS

Business models &Scaling Up Gender DimensionFlood Index DesignScale up to other flood prone states and adopted by more playersFeed into National and State Policy dialoguesIBFI The Big PicturePolicy Analysis & Investment options Contextual FactorsSocio-economic, political, technologicalExisting policies, practices, beliefsInfluencing networks, policy & practice, power relationsCapacity of target groups to respond and use research

Thank You !!