Abstract. The urgency of accelerating disaster risk resilience also promotes preferred systematic reviews of the methods for design and evaluation of risk transfer tools. This paper aims to provide a state-of-art weather index insurance design, thereby including methods for natural hazards’ indices calculation, vulnerability assessment and risk pricing. We applied the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) using the Scopus database. First, 364 peer-reviewed articles from 2010 to present were screened for a bibliometric analysis and then, the 34 most cited articles from the past five years were systematically analyzed. Our results demonstrate that despite a great research effort on index insurance, the majority of them focused on food insecurity through agricultural and crop insurance. Also, climate change and basis risks were found highly relevant for weather index insurance, but weakly developed, suggesting challenges around food insecurity. Special focus was given to drought hazards, while other hazards such as temperature variation, excessive rainfall and wildfires were slightly covered. Emerging areas, namely agricultural, hydrological, and sustainable index insurance found promissory for insurance. Also, current state-of-the-art lacks methods for incorporating multi-hazard risk evaluation in vulnerability assessment and risk pricing. Most studies considered only single-hazard risk, and the multi-hazard risk studies assumed independence between hazards. Thus, we summarized the most common methods for calculating indices, estimating losses using indices, pricing risks, and evaluating insurance index policies. This review promotes a starting point in weather index insurance design towards a multi-hazard resilient society.

<p>Water is a critical resource for food production. Climate change has shown that shifts in precipitation regimes and increases in atmospheric temperature threaten food production worldwide. Therefore, it plays an essential role as a driver in the water-environment-food nexus. Strategies to cope with impacts of climate risk require understanding the relationship between hydro-meteorological extremes and crop yield shortfalls to guide decision-making. The purpose of this paper is to propose a fully data-driven model for predicting the impacts of hydrological extremes on food production for decision-making at the municipal level. We use the Support Vector Machine (SVM) model considering a variety of kernels for predicting crop yields using reanalysis data of water storage (WS) from 0 to 28 cm of soil during the soybean growing season. We used ERA5 WS data for Paraná state in Brazil and official annual soybean crop yields (SBY) at the municipal level. We tuned a SVM radial basis function kernel with sigma and cost parameters for municipalities with significant production of soybean. The SVM model predicted crop yields accurately with R² ranging from 0.12 to 0.85. The use of soil moisture data increased model accuracy from 30 to 95% and reduced error from 5 to 58% in relation to using only SBY, except for one location. These results indicate that the model is better able to depict SBY during drier conditions, reducing prediction accuracy in years with average or above average yields. The model we proposed is useful for estimating crop losses due to water shortage at the municipal level. Our results suggest that using WS data from ERA5 as an additional input to past SBY adds relevant information for several applications such as risk transfer, irrigation planning and farm-level management with data that are made available for most countries. Our model has potential for climate impact studies coupled with projections from Global Circulation Models forced by Shared Socioeconomic Pathways (SPP).</p>

Abstract. Ensuring food security against climate risks has been a growing challenge recently. Weather index insurance has been pointed out as a tool for increasing the financial resilience of food production. However, the multi-hazard insurance design needs to be better understood. This paper aims to review weather index insurance design for food security resilience, including the methodology for calculating natural hazards' indices, vulnerability assessment, and risk pricing. We searched for relevant research papers in the Scopus database using the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) protocol. Initially, 364 peer-reviewed papers from 1 January 2010 to 19 February 2022 were screened for bibliometric analysis. Then, the 26 most relevant papers from the last 5 years were systematically analyzed. Our results demonstrate that despite a significant research effort on index insurance, most papers focused on food production. However, research considering other aspects of food security, such as transportation, storage, and distribution, is lacking. Most research focuses on droughts. Other hazards, such as extreme temperature variation, excessive rainfall, and wildfires, were poorly covered. Most studies considered only single-hazard risk, and the multi-hazard risk studies assumed independence between hazards, neglecting the synergy hypothesis between hazards. Lastly, we proposed a conceptual framework that illustrates design paths for a generalized weather index insurance design and evaluation. Solutions for addressing multi-hazard problems are considered. An illustrative example demonstrates the importance of testing the multi-hazard risk hypothesis for weather-based index insurance design for soybean production in Brazil.

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