Articles | Volume 23, issue 4
https://doi.org/10.5194/nhess-23-1335-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-23-1335-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Review article: Design and evaluation of weather index insurance for multi-hazard resilience and food insecurity
Marcos Roberto Benso
CORRESPONDING AUTHOR
WADI Lab, São Carlos School of Engineering, University of São Paulo, São
Carlos, SP, Brazil
National Institute of Science and Technology for the Fight Against Hunger, São Paulo, SP, Brazil
National Institute of Science and Technology for Climate Change Phase 2 (INCT-II), São Paulo, SP, Brazil
Gabriela Chiquito Gesualdo
WADI Lab, São Carlos School of Engineering, University of São Paulo, São
Carlos, SP, Brazil
Roberto Fray Silva
Institute of Advanced Studies, University of São Paulo, São Paulo, SP, Brazil
Center for Artificial Intelligence (C4AI), São Paulo, SP, Brazil
National Institute of Science and Technology for the Fight Against Hunger, São Paulo, SP, Brazil
Greicelene Jesus Silva
WADI Lab, São Carlos School of Engineering, University of São Paulo, São
Carlos, SP, Brazil
National Institute of Science and Technology for Climate Change Phase 2 (INCT-II), São Paulo, SP, Brazil
Luis Miguel Castillo Rápalo
WADI Lab, São Carlos School of Engineering, University of São Paulo, São
Carlos, SP, Brazil
Fabricio Alonso Richmond Navarro
WADI Lab, São Carlos School of Engineering, University of São Paulo, São
Carlos, SP, Brazil
Patricia Angélica Alves Marques
Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba, SP, Brazil
Center for Artificial Intelligence (C4AI), São Paulo, SP, Brazil
National Institute of Science and Technology for Climate Change Phase 2 (INCT-II), São Paulo, SP, Brazil
José Antônio Marengo
Brazilian National Center for Monitoring and Early Warning of Natural Disasters (CEMADEN), São José dos Campos, SP, Brazil
National Institute of Science and Technology for Climate Change Phase 2 (INCT-II), São Paulo, SP, Brazil
Eduardo Mario Mendiondo
WADI Lab, São Carlos School of Engineering, University of São Paulo, São
Carlos, SP, Brazil
Center for Artificial Intelligence (C4AI), São Paulo, SP, Brazil
National Institute of Science and Technology for the Fight Against Hunger, São Paulo, SP, Brazil
National Institute of Science and Technology for Climate Change Phase 2 (INCT-II), São Paulo, SP, Brazil
Related authors
Marcos Roberto Benso, Roberto Fray Silva, Gabriela Chiquito Gesualdo, Antonio Mauro Saraiva, Alexandre Cláudio Botazzo Delbem, Patricia Angélica Alves Marques, José Antonio Marengo, and Eduardo Mario Mendiondo
Nat. Hazards Earth Syst. Sci., 25, 1387–1404, https://doi.org/10.5194/nhess-25-1387-2025, https://doi.org/10.5194/nhess-25-1387-2025, 2025
Short summary
Short summary
This study applies climate extreme indices to assess climate risks to food security. Using an explainable machine learning analysis, key climate indices affecting maize and soybean yields in Brazil were identified. Results reveal the temporal sensitivity of these indices and critical yield loss thresholds, informing policy and adaptation strategies.
Marcos Roberto Benso, Roberto Fray Silva, Gabriela Chiquito Gesualdo, Antonio Mauro Saraiva, Alexandre Cláudio Botazzo Delbem, Patricia Angélica Alves Marques, José Antonio Marengo, and Eduardo Mario Mendiondo
Nat. Hazards Earth Syst. Sci., 25, 1387–1404, https://doi.org/10.5194/nhess-25-1387-2025, https://doi.org/10.5194/nhess-25-1387-2025, 2025
Short summary
Short summary
This study applies climate extreme indices to assess climate risks to food security. Using an explainable machine learning analysis, key climate indices affecting maize and soybean yields in Brazil were identified. Results reveal the temporal sensitivity of these indices and critical yield loss thresholds, informing policy and adaptation strategies.
Marina Batalini de Macedo, Marcos Roberto Benso, Karina Simone Sass, Eduardo Mario Mendiondo, Greicelene Jesus da Silva, Pedro Gustavo Câmara da Silva, Elisabeth Shrimpton, Tanaya Sarmah, Da Huo, Michael Jacobson, Abdullah Konak, Nazmiye Balta-Ozkan, and Adelaide Cassia Nardocci
Nat. Hazards Earth Syst. Sci., 24, 2165–2173, https://doi.org/10.5194/nhess-24-2165-2024, https://doi.org/10.5194/nhess-24-2165-2024, 2024
Short summary
Short summary
With climate change, societies increasingly need to adapt to deal with more severe droughts and the impacts they can have on food production. To make better adaptation decisions, drought resilience indicators can be used. To build these indicators, surveys with experts can be done. However, designing surveys is a costly process that can influence how experts respond. In this communication, we aim to deal with the challenges encountered in the development of surveys to help further research.
Marina Batalini de Macedo, Nikunj K. Mangukiya, Maria Clara Fava, Ashutosh Sharma, Roberto Fray da Silva, Ankit Agarwal, Maria Tereza Razzolini, Eduardo Mario Mendiondo, Narendra K. Goel, Mathew Kurian, and Adelaide Cássia Nardocci
Proc. IAHS, 386, 41–46, https://doi.org/10.5194/piahs-386-41-2024, https://doi.org/10.5194/piahs-386-41-2024, 2024
Short summary
Short summary
More and more extreme rainfall causes flooding problems in cities and communities, affecting the health and well-being of the population, as well as causing damage to the economy. To help design actions aiming at reducing the impacts of these floods, computational models can be used to simulate their extent. However, there are different types of models currently available. In this study, we evaluated three different models, for a city in Brazil and a region in India, to guide the best use of it.
Gabriela C. Gesualdo, Marcos R. Benso, Fabrício A. R. Navarro, Luis M. Castillo, and Eduardo M. Mendiondo
Proc. IAHS, 385, 117–120, https://doi.org/10.5194/piahs-385-117-2024, https://doi.org/10.5194/piahs-385-117-2024, 2024
Short summary
Short summary
We simulated indexed insurance for a water utility responsible for providing water to 7.2 million people in a metropolitan region. According to our findings, an annual amount (premium) of USD 0.43, 0.87, and 1.73 should be charged per person to obtain drought coverage for three, six, and twelve months. The premium fee can be implemented in the water bills as a new strategy to pool the risk between the supplied users and the utility, to prevent them from being exposed to surcharge fluctuations.
Heidi Kreibich, Kai Schröter, Giuliano Di Baldassarre, Anne F. Van Loon, Maurizio Mazzoleni, Guta Wakbulcho Abeshu, Svetlana Agafonova, Amir AghaKouchak, Hafzullah Aksoy, Camila Alvarez-Garreton, Blanca Aznar, Laila Balkhi, Marlies H. Barendrecht, Sylvain Biancamaria, Liduin Bos-Burgering, Chris Bradley, Yus Budiyono, Wouter Buytaert, Lucinda Capewell, Hayley Carlson, Yonca Cavus, Anaïs Couasnon, Gemma Coxon, Ioannis Daliakopoulos, Marleen C. de Ruiter, Claire Delus, Mathilde Erfurt, Giuseppe Esposito, Didier François, Frédéric Frappart, Jim Freer, Natalia Frolova, Animesh K. Gain, Manolis Grillakis, Jordi Oriol Grima, Diego A. Guzmán, Laurie S. Huning, Monica Ionita, Maxim Kharlamov, Dao Nguyen Khoi, Natalie Kieboom, Maria Kireeva, Aristeidis Koutroulis, Waldo Lavado-Casimiro, Hong-Yi Li, Maria Carmen LLasat, David Macdonald, Johanna Mård, Hannah Mathew-Richards, Andrew McKenzie, Alfonso Mejia, Eduardo Mario Mendiondo, Marjolein Mens, Shifteh Mobini, Guilherme Samprogna Mohor, Viorica Nagavciuc, Thanh Ngo-Duc, Huynh Thi Thao Nguyen, Pham Thi Thao Nhi, Olga Petrucci, Nguyen Hong Quan, Pere Quintana-Seguí, Saman Razavi, Elena Ridolfi, Jannik Riegel, Md Shibly Sadik, Nivedita Sairam, Elisa Savelli, Alexey Sazonov, Sanjib Sharma, Johanna Sörensen, Felipe Augusto Arguello Souza, Kerstin Stahl, Max Steinhausen, Michael Stoelzle, Wiwiana Szalińska, Qiuhong Tang, Fuqiang Tian, Tamara Tokarczyk, Carolina Tovar, Thi Van Thu Tran, Marjolein H. J. van Huijgevoort, Michelle T. H. van Vliet, Sergiy Vorogushyn, Thorsten Wagener, Yueling Wang, Doris E. Wendt, Elliot Wickham, Long Yang, Mauricio Zambrano-Bigiarini, and Philip J. Ward
Earth Syst. Sci. Data, 15, 2009–2023, https://doi.org/10.5194/essd-15-2009-2023, https://doi.org/10.5194/essd-15-2009-2023, 2023
Short summary
Short summary
As the adverse impacts of hydrological extremes increase in many regions of the world, a better understanding of the drivers of changes in risk and impacts is essential for effective flood and drought risk management. We present a dataset containing data of paired events, i.e. two floods or two droughts that occurred in the same area. The dataset enables comparative analyses and allows detailed context-specific assessments. Additionally, it supports the testing of socio-hydrological models.
Enner Alcântara, José A. Marengo, José Mantovani, Luciana R. Londe, Rachel Lau Yu San, Edward Park, Yunung Nina Lin, Jingyu Wang, Tatiana Mendes, Ana Paula Cunha, Luana Pampuch, Marcelo Seluchi, Silvio Simões, Luz Adriana Cuartas, Demerval Goncalves, Klécia Massi, Regina Alvalá, Osvaldo Moraes, Carlos Souza Filho, Rodolfo Mendes, and Carlos Nobre
Nat. Hazards Earth Syst. Sci., 23, 1157–1175, https://doi.org/10.5194/nhess-23-1157-2023, https://doi.org/10.5194/nhess-23-1157-2023, 2023
Short summary
Short summary
The municipality of Petrópolis (approximately 305 687 inhabitants) is nestled in the mountains 68 km outside the city of Rio de Janeiro. On 15 February 2022, the city of Petrópolis in Rio de Janeiro, Brazil, received an unusually high volume of rain within 3 h (258 mm). This resulted in flash floods and subsequent landslides that caused 231 fatalities, the deadliest landslide disaster recorded in Petrópolis. This work shows how the disaster was triggered.
Ashish Shrestha, Felipe Augusto Arguello Souza, Samuel Park, Charlotte Cherry, Margaret Garcia, David J. Yu, and Eduardo Mario Mendiondo
Hydrol. Earth Syst. Sci., 26, 4893–4917, https://doi.org/10.5194/hess-26-4893-2022, https://doi.org/10.5194/hess-26-4893-2022, 2022
Short summary
Short summary
Equitable sharing of benefits is key to successful cooperation in transboundary water resource management. However, external changes can shift the split of benefits and shifts in the preferences regarding how an actor’s benefits compare to the other’s benefits. To understand how these changes can impact the robustness of cooperative agreements, we develop a socio-hydrological system dynamics model of the benefit sharing provision of the Columbia River Treaty and assess a series of scenarios.
Cited articles
Abdi, M. J., Raffar, N., Zulkafli, Z., Nurulhuda, K., Rehan, B. M., Muharam, F. M., Khosim, N. A., and Tangang, F.: Index-based insurance and hydroclimatic risk management in agriculture: A systematic review of index selection and yield-index modelling methods, Int. J. Disast. Risk Re., 67, 102653, https://doi.org/10.1016/j.ijdrr.2021.102653, 2022. a, b, c, d
Akter, S.: The Role of Microinsurance as a Safety Net Against Environmental Risks in Bangladesh, J. Environ. Dev., 21, 263–280, https://doi.org/10.1177/1070496512442505, 2012. a
Aria, M. and Cuccurullo, C.: bibliometrix: An R-tool for comprehensive science mapping analysis, J. Informetr., 11, 959–975, 2017. a
Awondo, S. N.: Efficiency of region-wide catastrophic weather risk pools: Implications for African Risk Capacity insurance program, J. Dev. Econ., 136, 111–118, https://doi.org/10.1016/j.jdeveco.2018.10.004, 2019. a, b
Baldos, U., Haqiqi, I., Hertel, T., Horridge, M., and Liu, J.: SIMPLE-G: A multiscale framework for integration of economic and biophysical determinants of sustainability, Environ. Modell. Softw., 133, 104805, https://doi.org/10.1016/j.envsoft.2020.104805, 2020. a
Barnett, B. J., Barrett, C. B., and Skees, J. R.: Poverty Traps and Index-Based Risk Transfer Products, World Dev., 36, 1766–1785, https://doi.org/10.1016/j.worlddev.2007.10.016, 2008. a
Benso, M. R., Fray da Silva, R., Gesualdo, G.,
Jesus da Silva, G., Castillo, L., Richmond Navarro, F. A., Marques,
P. A. A., Marengo, J., Mendiondo, and Eduardo, M.: Multi-hazard
risk index insurance for soybean in Parana, Brazil, V1, Mendeley Data [data set],
https://doi.org/10.17632/3xjshm9n5w.1, 2023. a
Bhering, S. B., Dos Santos, H. G., Bognola, I., Cúrcio, G., Carvalho Junior, W. D., Chagas,
C. D. S., Manzatto, C., Áglio, M., and Silva, J. D. S.: Mapa de solos do Estado do Paraná,
legenda atualizada, in: 32th CONGRESSO BRASILEIRO DE CIÊNCIA DO SOLO, O solo e
a produção de bioenergia: perspectivas e desafios: anais, SBCS, Viçosa, MG; UFC,
Fortaleza, 2009. a
Blier-Wong, C., Cossette, H., Lamontagne, L., and Marceau, E.: Machine learning in P&C insurance: A review for pricing and reserving, Risks, 9, 4, https://doi.org/10.3390/risks9010004, 2020. a, b, c
Boyle, C. F., Haas, J., and Kern, J. D.: Development of an irradiance-based
weather derivative to hedge cloud risk for solar energy systems, Renew.
Energ., 164, 1230–1243, https://doi.org/10.1016/j.renene.2020.10.091, 2021. a, b, c, d
Brandão, A. D. and Sodek, L.: Nitrate uptake and metabolism by roots of soybean plants under oxygen deficiency, Brazilian Journal of Plant Physiology, 21, 13–23, 2009. a
Breiman, L.: Random forests, Mach. Learn., 45, 5–32, 2001. a
Brunner, M. I., Slater, L., Tallaksen, L. M., and Clark, M.: Challenges in modeling and predicting floods and droughts: A review, WIREs Water, 8, e1520, https://doi.org/10.1002/WAT2.1520, 2021. a
Cobo, M., López-Herrera, A., Herrera-Viedma, E., and Herrera, F.: An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the Fuzzy Sets Theory field, J. Informetr., 5, 146–166, https://doi.org/10.1016/j.joi.2010.10.002, 2011. a
Cremades, R., Surminski, S., Máñez Costa, M., Hudson, P., Shrivastava, P., and Gascoigne, J.: Using the adaptive cycle in climate-risk insurance to design resilient futures, Nat. Clim. Change, 8, 4–7, 2018. a
da Silva, E. H. F. M., Antolin, L. A. S., Zanon, A. J., Junior, A. S. A., de Souza, H. A., dos Santos Carvalho, K., Junior, N. A. V., and Marin, F. R.: Impact assessment of soybean yield and water productivity in Brazil due to climate change, Eur. J. Agron., 129, 126329, https://doi.org/10.1016/j.eja.2021.126329, 2021. a
Denaro, S., Castelletti, A., Giuliani, M., and Characklis, G.: Fostering cooperation in power asymmetrical water systems by the use of direct release rules and index-based insurance schemes, Adv. Water Resour., 115, 301–314, https://doi.org/10.1016/j.advwatres.2017.09.021, 2018. a, b
Denaro, S., Castelletti, A., Giuliani, M., and Characklis, G.: Insurance Portfolio Diversification Through Bundling for Competing Agents Exposed to Uncorrelated Drought and Flood Risks, Water Resour. Res., 56, e2019WR026443, https://doi.org/10.1029/2019WR026443, 2020. a, b
Ejiyi, C. J., Qin, Z., Salako, A. A., Happy, M. N., Nneji, G. U., Ukwuoma, C. C., Chikwendu, I. A., and Gen, J.: Comparative Analysis of Building Insurance Prediction Using Some Machine Learning Algorithms, International Journal of Interactive Multimedia and Artificial Intelligence, 7, 75–85, https://doi.org/10.9781/ijimai.2022.02.005, 2022. a
FAO: Chapter 2. Food security: concepts and measurement, Trade Reforms and Food Security, Conceptualizing the Linkages, Food and Agriculture Organization, United Nations, Rome, https://www.fao.org/3/y4671e/y4671e06.htm (last access: 8 March 2023), 2003. a
FAO: The Water-Energy-Food Nexus A new approach in support of food security and sustainable agriculture, Food and Agriculture Organization, United Nations, Rome, https://www.fao.org/3/bl496e/bl496e.pdf (last access: 8 March 2023), 2014. a
Figueiredo, R., Martina, M. L., Stephenson, D. B., and Youngman, B. D.: A probabilistic paradigm for the parametric insurance of natural hazards, Risk Anal., 38, 2400–2414, 2018. a
Foster, B., Kern, J., and Characklis, G.: Mitigating hydrologic financial risk in hydropower generation using index-based financial instruments, Water Resources and Economics, 10, 45–67, https://doi.org/10.1016/j.wre.2015.04.001, 2015. a
Frees, E. W., Derrig, R. A., and Meyers, G.: Predictive modeling applications in actuarial science, vol. 1, Cambridge University Press, https://doi.org/10.1017/CBO9781139342674, 2014. a
Furuya, J., Mar, S. S., Hirano, A., and Sakurai, T.: Optimum insurance contract of flood damage index insurance for rice farmers in Myanmar, Paddy Water Environ., 19, 319–330, https://doi.org/10.1007/s10333-021-00859-2, 2021. a, b, c
Ghosh, R. K., Gupta, S., Singh, V., and Ward, P. S.: Demand for Crop Insurance in Developing Countries: New Evidence from India, J. Agr. Econ., 72, 293–320, https://doi.org/10.1111/1477-9552.12403, 2021. a, b
Gill, J. C. and Malamud, B. D.: Reviewing and visualizing the interactions of natural hazards, Rev. Geophys., 52, 680–722, https://doi.org/10.1002/2013RG000445, 2014. a, b, c
Gómez-Limón, J. A.: Hydrological drought insurance for irrigated agriculture in southern Spain, Agr. Water Manage., 240, 106271, https://doi.org/10.1016/j.agwat.2020.106271, 2020. a, b, c, d
Guerrero-Baena, M. and Gómez-Limón, J.: Insuring Water Supply in Irrigated Agriculture: A Proposal for Hydrological Drought Index-Based Insurance in Spain, Water, 11, 686, https://doi.org/10.3390/w11040686, 2019. a, b, c
Guzmán, D. A., Mohor, G. S., and Mendiondo, E. M.: Multi-Year Index-Based Insurance for Adapting Water Utility Companies to Hydrological Drought: Case Study of a Water Supply System of the Sao Paulo Metropolitan Region, Brazil, Water, 12, 2954, https://doi.org/10.3390/w12112954, 2020. a, b
Halcrow, H. G.: Actuarial Structures for Crop Insurance, Am. J. Agr. Econ., 31, 418, https://doi.org/10.2307/1232330, 1949. a
Hillier, J. K., Matthews, T., Wilby, R. L., and Murphy, C.: Multi-hazard dependencies can increase or decrease risk, Nat. Clim. Change, 10, 595–598, https://doi.org/10.1038/s41558-020-0832-y, 2020. a
Hoek van Dijke, A. J., Herold, M., Mallick, K., Benedict, I., Machwitz, M., Schlerf, M., Pranindita, A., Theeuwen, J. J., Bastin, J.-F., and Teuling, A. J.: Shifts in regional water availability due to global tree restoration, Nat. Geosci., 15, 363–368, 2022. a
Hohl, R., Jiang, Z., Vu, M. T., Vijayaraghavan, S., and Liong, S.-Y.: Using a regional climate model to develop index-based drought insurance for sovereign disaster risk transfer, Agricultural Finance Review, 81, 151–168, https://doi.org/10.1108/AFR-02-2020-0020, 2021. a, b, c
Hudson, P., Botzen, W. W., Feyen, L., and Aerts, J. C.: Incentivising flood risk adaptation through risk based insurance premiums: Trade-offs between affordability and risk reduction, Ecol. Econ., 125, 1–13, 2016. a
IPCC: Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge Press University, ISBN 978-92-9169-160-9, 2022. a
Kahneman, D. and Tversky, A.: Prospect Theory: An Analysis of Decision under Risk, Econometrica, 47, 263–292, https://doi.org/10.2307/1914185, 1979. a
Kath, J., Mushtaq, S., Henry, R., Adeyinka, A., and Stone, R.: Index insurance benefits agricultural producers exposed to excessive rainfall risk, Weather and Climate Extremes, 22, 1–9, https://doi.org/10.1016/J.WACE.2018.10.003, 2018. a, b
Kath, J., Mushtaq, S., Henry, R., Adeyinka, A. A., Stone, R., Marcussen, T., and Kouadio, L.: Spatial variability in regional scale drought index insurance viability across Australia's wheat growing regions, Climate Risk Management, 24, 13–29, https://doi.org/10.1016/j.crm.2019.04.002, 2019. a, b, c, d
Keskitalo, E. C. H., Vulturius, G., and Scholten, P.: Adaptation to climate change in the insurance sector: examples from the UK, Germany and the Netherlands, Nat. Hazards, 71, 315–334, 2014. a
Kim, W., Iizumi, T., and Nishimori, M.: Global patterns of crop production losses associated with droughts from 1983 to 2009, J. Appl. Meteorol. Clim., 58, 1233–1244, https://doi.org/10.1175/JAMC-D-18-0174.1, 2019. a
Komendantova, N., Mrzyglocki, R., Mignan, A., Khazai, B., Wenzel, F., Patt, A., and Fleming, K.: Multi-hazard and multi-risk decision-support tools as a part of participatory risk governance: Feedback from civil protection stakeholders, Int. J. Disast. Risk Re., 8, 50–67, https://doi.org/10.1016/j.ijdrr.2013.12.006, 2014. a
Kraehnert, K., Osberghaus, D., Hott, C., Habtemariam, L. T., Wätzold, F., Hecker, L. P., and Fluhrer, S.: Insurance Against Extreme Weather Events: An Overview, Review of Economics, 72, 71–95, https://doi.org/10.1515/roe-2021-0024, 2021. a
Leblois, A., Quirion, P., and Sultan, B.: Price vs. weather shock hedging for cash crops: Ex ante evaluation for cotton producers in Cameroon, Ecol. Econ., 101, 67–80, https://doi.org/10.1016/j.ecolecon.2014.02.021, 2014. a
Lesk, C., Coffel, E., Winter, J., Ray, D., Zscheischler, J., Seneviratne, S. I., and Horton, R.: Stronger temperature–moisture couplings exacerbate the impact of climate warming on global crop yields, Nature Food, 2, 683–691, 2021. a
Liaw, A. and Wiener, M.: Classification and Regression by randomForest, R News, 2, 18–22, https://CRAN.R-project.org/doc/Rnews/ (last access: 8 March 2023), 2002. a
Liberati, A., Altman, D. G., Tetzlaff, J., Mulrow, C., Gøtzsche, P. C., Ioannidis, J. P., Clarke, M., Devereaux, P. J., Kleijnen, J., and Moher, D.: The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration, J. Clin. Epidemiol., 62, e1–e34, 2009. a, b
Lyubchich, V., Newlands, N. K., Ghahari, A., Mahdi, T., and Gel, Y. R.: Insurance risk assessment in the face of climate change: Integrating data science and statistics, WIREs Comput. Stat., 11, e1462, https://doi.org/10.1002/wics.1462, 2019. a
Machado, M. L., Nascimento, N., Baptista, M., Gonçalves, M., Silva, A., Lima, J. d., Dias, R., Silva, A., Machado, E., and Fernandes, W.: Curvas de danos de inundação versus profundidade de submersão: desenvolvimento de metodologia, Revista de Gestão de Água da América Latina, 2, 35–52, 2005. a
MacQueen, J.: Classification and analysis of multivariate observations, in: 5th Berkeley Symp. Math. Statist. Probability, 1966, Barkley, University of
California Press, 281–297, 1967. a
Miranda, M. J.: Area‐Yield Crop Insurance Reconsidered, Am. J. Agr. Econ., 73, 233–242, https://doi.org/10.2307/1242708, 1991. a, b
Monteleone, B., Borzí, I., Bonaccorso, B., and Martina, M.: Quantifying crop vulnerability to weather-related extreme events and climate change through vulnerability curves, Nat. Hazards, 1–36, online first, https://doi.org/10.1007/s11069-022-05791-0, 2022. a
Mortensen, E. and Block, P.: ENSO Index-Based Insurance for Agricultural Protection in Southern Peru, Geosciences, 8, 64, https://doi.org/10.3390/geosciences8020064, 2018. a, b, c
Müller, A. and Grandi, M.: Weather Derivatives: A Risk Management Tool for Weather-sensitive Industries, Geneva Pap. R. I.-Iss. P., 25, 273–287, https://doi.org/10.1111/1468-0440.00065, 2000. a, b
Mußhoff, O., Hirschauer, N., Grüner, S., and Pielsticker, S.: Bounded rationality and the adoption of weather index insurance, Agricultural Finance Review, 78, 116–134, https://doi.org/10.1108/AFR-02-2017-0008, 2018. a
Nelsen, R. B.: An Introduction to Copulas, in: Springer Series in Statistics, Springer New York, New York, NY, https://doi.org/10.1007/0-387-28678-0, 2006. a
Norton, M. T., Turvey, C., and Osgood, D.: Quantifying spatial basis risk for weather index insurance, The Journal of Risk Finance, 14, 20–34, https://doi.org/10.1108/15265941311288086, 2013. a, b, c
Odening, M. and Shen, Z.: Challenges of insuring weather risk in agriculture, Agricultural Finance Review, 74, 188–199, https://doi.org/10.1108/AFR-11-2013-0039, 2014. a
Parana: Levantamento da Produção Agropecuária, https://www.agricultura.pr.gov.br/deral/ProducaoAnual, last access: 8 March 2023. a
Paudel, Y., Botzen, W. J., and Aerts, J. C.: Influence of climate change and socio-economic development on catastrophe insurance: a case study of flood risk scenarios in the Netherlands, Reg. Environ. Change, 15, 1717–1729, 2015. a
Pereira, D. I., Pereira, P., Brilha, J., and Santos, L.: Geodiversity assessment of Paraná State (Brazil): an innovative approach, Environ. Manage., 52, 541–552, 2013. a
Peterson, T., Folland, C., Gruza, G., Hogg, W., Mokssit, A., and Plummer, N.: Report on the activities of the working group on climate change detection and related rapporteurs, World Meteorological Organization Geneva, http://etccdi.pacificclimate.org/docs/wgccd.2001.pdf (last access: 8 March 2023), 2001. a
Pislyakov, V. and Shukshina, E.: Measuring excellence in Russia: Highly cited papers, leading institutions, patterns of national and international
collaboration, J. Assoc. Inf. Sci. Tech., 65, 2321–2330, https://doi.org/10.1002/asi.23093, 2014. a
Platteau, J.-P., De Bock, O., and Gelade, W.: The demand for microinsurance: A literature review, World Dev., 94, 139–156, 2017. a
Porth, L., Boyd, M., and Pai, J.: Reducing risk through pooling and selective reinsurance using simulated annealing: An example from crop insurance, Geneva Risk Ins. Rev., 41, 163–191, 2016. a
R Core Team: R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, https://www.r-project.org/ (last access: 8 March 2023), 2022. a
Raucci, G. L., Lanna, R., da Silveira, F., and Capitani, D. H. D.: Development of weather derivatives: evidence from the Brazilian soybean market, Italian Review of Agricultural Economics, 74, 17–28, https://doi.org/10.13128/REA-10850, 2019. a
Riahi, K., Van Vuuren, D. P., Kriegler, E., Edmonds, J., O'Neill, B. C., Fujimori, S., Bauer, N., Calvin, K., Dellink, R., Fricko, O., Lutz, W., Popp, A., Cuaresma, J. C., KC, S., Leimbach, M., Jiang, L., Kram, T., Rao, S., Emmerling, J., Ebi, K., Hasegawa, T., Havlik, P., Humpenöder, F., Da Silva, L. A., Smith, S., Stehfest, E., Bosetti, V., Eom, J., Gernaat, D., Masui, T., Rogelj, J., Strefler, J., Drouet, L., Krey, V., Luderer, G., Harmsen, M.,
Takahashi, K., Baumstark, L., Doelman, J. C., Kainuma, M., Klimont, Z., Marangoni, G., Lotze-Campen, H., Obersteiner, M., Tabeau, A., and Tavoni, M.: The shared socioeconomic pathways and their energy, land use, and greenhouse gas emissions implications: an overview, Global Environ. Change, 42, 153–168, 2017. a
Ricome, A., Affholder, F., Gérard, F., Muller, B., Poeydebat, C., Quirion, P., and Sall, M.: Are subsidies to weather-index insurance the best use of public funds? A bio-economic farm model applied to the Senegalese groundnut basin, Agr. Syst., 156, 149–176, https://doi.org/10.1016/j.agsy.2017.05.015, 2017. a
Righetto, J., Mendiondo, E., and Righetto, A.: Modelo de Seguro para Riscos Hidrológicos, Revista Brasileira de Recursos, 12, 107–113, https://doi.org/10.21168/rbrh.v12n2.p107-113, 2007. a
Rodríguez, Y. E., Pérez-Uribe, M. A., and Contreras, J.: Wind put barrier options pricing based on the Nordix index, Energies, 14, 1177, https://doi.org/10.3390/en14041177, 2021. a, b
Roznik, M., Brock Porth, C., Porth, L., Boyd, M., and Roznik, K.: Improving agricultural microinsurance by applying universal kriging and generalised additive models for interpolation of mean daily temperature, Geneva Pap. R. I.-Iss. P., 44, 446–480, https://doi.org/10.1057/s41288-019-00127-9, 2019. a
Sant, D. T.: Estimating Expected Losses in Auto Insurance, J. Risk Insur., 47, 133–151, https://doi.org/10.2307/252686, 1980. a
Sarris, A.: Weather index insurance for agricultural development: Introduction and overview, Agr. Econ., 44, 381–384, https://doi.org/10.1111/agec.12022, 2013. a
Saxton, K. E. and Rawls, W. J.: Soil water characteristic estimates by texture and organic matter for hydrologic solutions, Soil Sci. Soc. Am. J., 70, 1569–1578, 2006. a
Scopus: What is Scopus about?, https://service.elsevier.com/app/answers/detail/a_id/15100/supporthub/scopus/ (last access: 8 March 2023), 2022. a
Sekhri, S., Kumar, P., Fürst, C., and Pandey, R.: Mountain specific multi-hazard risk management framework (MSMRMF): Assessment and mitigation of multi-hazard and climate change risk in the Indian Himalayan Region, Ecol. Indic., 118, 106700, https://doi.org/10.1016/j.ecolind.2020.106700, 2020. a
Skees, J. R.: Challenges for use of index‐based weather insurance in lower
income countries, Agricultural Finance Review, 68, 197–217,
https://doi.org/10.1108/00214660880001226, 2008. a, b, c
Smith, A. B. and Matthews, J. L.: Quantifying uncertainty and variable sensitivity within the US billion-dollar weather and climate disaster cost estimates, Nat. Hazards, 77, 1829–1851, https://doi.org/10.1007/s11069-015-1678-x, 2015. a
Tilloy, A., Malamud, B. D., Winter, H., and Joly-Laugel, A.: A review of quantification methodologies for multi-hazard interrelationships, Earth-Sci. Rev., 196, 102881, https://doi.org/10.1016/J.EARSCIREV.2019.102881, 2019. a, b
Turvey, C., Shee, A., and Marr, A.: Addressing fractional dimensionality in the application of weather index insurance and climate risk financing in agricultural development: A dynamic triggering approach, Weather Clim. Soci., 11, 901–915, https://doi.org/10.1175/WCAS-D-19-0014.1, 2019. a, b
UNDRR: 2009 unisdr terminology on disaster risk reduction, https://www.unisdr.org/files/7817_UNISDRTerminologyEnglish.pdf (last access: 8 March 2023), 2009. a
UNEP FI: Principles for sustainable insurance, UN Environment Programme Finance Initiative, Geneva, Switzerland, https://www.unepfi.org/insurance/insurance/ (last access: 8 March 2023), 2012. a
Van Vuuren, D. P., Edmonds, J., Kainuma, M., Riahi, K., Thomson, A., Hibbard, K., Hurtt, G. C., Kram, T., Krey, V., Lamarque, J.-F., Masui, T., Meinshausen, M., Nakicenovic, N., Smith, S. J., and Rose, S. K.: The representative concentration pathways: an overview, Climatic Change, 109, 5–31, 2011. a
Vroege, W., Bucheli, J., Dalhaus, T., Hirschi, M., and Finger, R.: Insuring crops from space: the potential of satellite-retrieved soil moisture to reduce farmers' drought risk exposure, Eur. Rev. Agric. Econ., 48, 266–314, https://doi.org/10.1093/erae/jbab010, 2021.
a, b, c
Wang, S. S.: A Universal Framework for Pricing Financial and Insurance Risks, ASTIN Bull., 32, 213–234, https://doi.org/10.2143/AST.32.2.1027, 2002. a
Yoshida, K., Srisutham, M., Sritumboon, S., Suanburi, D., Janjirauttikul, N., and Suanpaga, W.: Evaluation of economic damages on rice production under extreme climate and agricultural insurance for adaptation measures in Northeast Thailand, Engineering Journal, 23, 451–460, https://doi.org/10.4186/ej.2019.23.6.451, 2019. a
Zara, C.: Weather derivatives in the wine industry, International Journal of Wine Business Research, 22, 222–237, https://doi.org/10.1108/17511061011075365, 2010. a
Zscheischler, J., Martius, O., Westra, S., Bevacqua, E., Raymond, C., Horton, R. M., van den Hurk, B., AghaKouchak, A., Jézéquel, A., Mahecha, M. D., Maraun, D., Ramos, A. M., Ridder, N. N., Thiery, W., and Vignotto, E.: A typology of compound weather and climate events, Nature Reviews Earth & Environment, 1, 333–347, 2020. a, b
Short summary
This article is about how farmers can better protect themselves from disasters like droughts, extreme temperatures, and floods. The authors suggest that one way to do this is by offering insurance contracts that cover these different types of disasters. By having this insurance, farmers can receive financial support and recover more quickly. The article elicits different ideas about how to design this type of insurance and suggests ways to make it better.
This article is about how farmers can better protect themselves from disasters like droughts,...
Altmetrics
Final-revised paper
Preprint