La marcha de Jesús sobre las aguas:

Examples of policies, practices and tools experimented in other economies may become the source of inspiration for initiatives to be undertaken within APEC, depending on the specific circumstances of the economy. To this end, this section presents two very different initiatives: the Africa RiskView (ARV), an IT platform providing a transparent system to estimate drought-related crop losses and the impact on populations’ food security in sub-Saharan African economies (see Box 11) and the newly established French National Observatory of Natural Hazards (Observatoire national des risques naturels), a public- private collaboration in disaster risk assessment.

Box 11 Africa RiskView (ARV)

Developed by the World Food Programme (WFP), Africa RiskView (ARV) is an IT platform providing a transparent system to estimate drought-related crop losses and the impact on populations’ food security in sub-Saharan African economies. This tool also converts the anticipated adverse impacts into monetary and financial terms, with a view to providing accurate estimates of the required response costs. It provides the technical foundation and basic infrastructure for the Africa Risk Capacity (ARC), an African-owned, continental index-based weather risk insurance pool and early response mechanism.

As a flexible tool that can be used to select accurate proxies for drought related losses and response costs needs, ARV allows ARC participating members to quantify critical financial components of their drought risk and, consequently, to determine the appropriate amount of risk to transfer to the ARC risk pool.

Several tasks can be performed by the software application, including: early warning, risk mapping, vulnerability assessment and financial planning. Rainfall and crop monitoring data are provided by reliable sources36 to anticipate high-level food security needs of affected populations and budget for response costs. Using ARV, ARC participating economies37 can determine how changes in rainfall would affect crop performance – measured by the reference water stress indicator called “Water Requirement Satisfaction Index” (WRSI) – and, ultimately, vulnerable populations. Water stress indicators measure crop performance based on the availability of water to the crop during a growing season. They assume that crop yields are proportional to the satisfaction of crop needs for water resource. The WRSI, developed by the Food and Agriculture Organisation (FAO)38 and currently used in different parametric insurance schemes across the world, is defined as the ratio of actual evapotranspiration (ETa) to maximum evapotranspiration (ETc). In simple terms, this index captures the impact of timing, quantity, and distribution of rainfall, comparing the amount of water available throughout the season to how much a plant needs in its different stages of growth.39

36

Including: WFP, Food and Agriculture Organisation, Famine Early Warning Systems Network, and the National Oceanic and Atmospheric Administration.

37

ARC participating countries are: Burkina Faso, Burundi, Central African Republic, Chad, Comoros, Republic of Congo, Côte d'Ivoire, Djibouti, Gambia, Guinea, Kenya, Liberia, Libya, Malawi, Mauritania, Mozambique, Niger, Rwanda, Sahrawi Arab Democratic Republic, Senegal, Togo and Zimbabwe.

38

See: Doorenbos J. and W.O. Pruitt, (1977) FAO Crop Water Requirements, Pruitt. FAO, Rome, Italy; Frère, M. and Popov, G.F. (1979) FAO: Agrometeorological crop monitoring and forecasting. FAO Plant Production and Protection p) Paper, FAO Working Paper, Rome, Italy; Frère, M. and Popov, G.F. (1986) FAO, Early Agrometeorological Crop Yield Forecasting. FAO Plant Production and Protection Paper, Working Paper No. 73FAO, Rome, Italy.

39

“ETa corresponds to an estimation of the quantity of water actually evaporated while ETc corresponds to the quantity of water that would evaporate if the water requirements of the plant were fully satisfied (…) Since crop sensibility to water stress depends on its growth phase, most of the insurance contracts consider those phases and take in account different references values of WRSI as triggers, depending on the phase considered (…)” Rainfall level of each (phase) is compared to the crop requirement for this particular growth stage and included in the weighted sum in order to compute the index corresponding for the whole

A second component of ARV aims to estimate the population affected by overlaying drought index information with sub-national data about the percentage of vulnerable population. This component tries to identify how drought may impact the needs of individuals and households. Exposure and resiliency are employed as basic variables in the exercise. Exposure to drought risk is defined by the weight of agricultural activities in the household’s total annual income. Resiliency is measured in terms of household’s distance from the poverty line. The percentages of the population vulnerable to droughts of various degrees of severity can be modified by the user at different administrative levels.

As a third step, ARV seeks to determine a monetary approximation of response costs, based on the index- based estimate of seasonal crop performance and the impact of such performance on affected populations vulnerable to food insecurity. This is crucial for policymakers in finance and disaster planning and preparedness. Using ARV, response costs can be estimated at different aggregation levels, including the local and regional levels, by multiplying the population affected by a fixed cost per beneficiary. Such cost can be adjusted by the user to reflect the cost of various types of responses to food insecure populations (e.g., food aid, cash vouchers), assessing financial needs for drought events of varying magnitudes. The tool can also be used to study historical data and learn from past events, with a view to improving contingency planning and emergency preparedness for future shocks.

The risk quantification capabilities developed by ARV can assist governments in anticipating the financial consequences of future potential drought events of different magnitudes, helping them to set up appropriate contingency plans and to budget for emergency response costs. Based on such estimates, ARC participating members can also determine the desired retention level beyond which drought risks are transferred to the risk pool, the ceding percentage (or co-insurance proportion), as well as the coverage limits to be purchased. These parameters determine the cost of risk transfer. ARV can also be used on an on-going basis to monitor, in near real-time,40 satellite-based rainfall and crop information through the season, thereby improving their emergency response planning and crisis management capabilities.

In short, ARV constitutes a flexible tool41 that can be used to select accurate proxies for drought related losses and response costs needs, supporting multiple applications: from quantitative risk assessment to early warning and financial planning. In this regard, it is noteworthy how risk assessment is linked here with emergency preparedness.

In France, following the devastating effects of windstorm Xynthia in 2010, causing storm surges in the Bay of Biscay and in Brittany, the French government, the Caisse centrale de réassurance (CCR), the state-owned reinsurance company, and the Mission des sociétés d’assurances pour la connaissance et la

prévention des risques naturels (MRN), established in 2012 a National Observatory of Natural Hazards

(Observatoire national des risques naturels, or “ONRN”), a non-profit entity involving two French insurance industry associations. The establishment of the ONRN will allow for the sharing of information and data collected and elaborated by different stakeholders, at central and local levels, and for the presentation of such information and data in a reliable, harmonised, updated and consistent manner. Data providers are affiliated to the ONRN by means of special contractual agreements in order to ensure consistency of approach and interoperability of data. Within the ONRN, confidential data can also be exchanged by participants and a common, holistic approach to natural hazards can be adopted.

period.” Leblois, A., Quirion, P., (2013) “Agricultural insurances based on Meteorological Indices: Realizations, Methods and Research Challenges”, Meteorol. Appl. Vol. 20, pp.1–9, 3.

40

Rainfall data in ARV is updated every ten days. 41

ARV was designed to be highly transparent and the different parameters can be changed and customised based on country, regional and local conditions.

Until the ONRN was established, experimental indicators and studies on natural hazards, including their financial and economic impacts, had been produced independently by various stakeholders. For instance, the CCR elaborated an indicator of insured losses due to floods per municipality and per inhabitant in France during the period 2005-2008; at the same time, the MNR evaluated the adequacy of the scope of coverage of Flood Risk Prevention Plans (Plan de prevention des risques d’inondations or “PPRI”), at the national level in France.42

The French Ministry of Sustainable Development and its DGPR have started a preliminary evaluation of flood risks for the purposes of implementing the EU Flood Directive to its full extent. This exercise, to be carried forward under the auspices of the ONRN, is expected to lead to a comprehensive assessment of flood risks in France, as well as to the design and adoption of targeted flood risk mitigation measures.

The ONRN is noteworthy as a collaborative effort among the State, local authorities, specialised agencies, the academia and the private insurance and reinsurance sector focusing on the improvement of consistency and interoperability of data on natural hazards for a full range of different applications, including risk assessment, risk mitigation, emergency preparedness and financial planning. End users of the data have a role in the governance of the ONRN, and can thus provide comments and input into ONRN work. This case illustrates the potential benefits that can be derived from foresight, planning and understanding the costs that have been incurred from past disaster events as well as being able to think about the likely costs that may arise in the future.

42

According to the analysis conducted by MNR, the scope of coverage of PPRIs can be considered adequate if at least 80% of the insured assets exposed to flood risk are located in municipalities that are covered by a regular PPRI and at least 80% of the municipalities included in the priority list (higher flood risk) are covered by a regular PPRI. In the context of the ONRN, the analysis will be expanded to cover not only insured assets, but all “adverse consequences” of floods mentioned by the European Directive 2007/60/EC on the assessment and management of flood risks (“EU Flood Directive”).