Articles | Volume 25, issue 4
https://doi.org/10.5194/nhess-25-1387-2025
© Author(s) 2025. 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-25-1387-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
10 Apr 2025
Research article |
| 10 Apr 2025
| 10 Apr 2025
A data-driven framework for assessing climatic impact drivers in the context of food security
Marcos Roberto Benso
CORRESPONDING AUTHOR
São Carlos School of Engineering, University of São Paulo, São Carlos, SP, 13566-590, Brazil
Roberto Fray Silva
Institute of Advanced Studies, University of São Paulo, São Paulo, SP, 05508-050, Brazil
Gabriela Chiquito Gesualdo
São Carlos School of Engineering, University of São Paulo, São Carlos, SP, 13566-590, Brazil
Department of Geosciences, Pennsylvania State University, State College, PA 16801, USA
Antonio Mauro Saraiva
Institute of Advanced Studies, University of São Paulo, São Paulo, SP, 05508-050, Brazil
Alexandre Cláudio Botazzo Delbem
Institute of Mathematics and Computer Sciences, University of São Paulo, São Carlos, SP, 13566-590, Brazil
Patricia Angélica Alves Marques
Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba, SP, 13418-900, Brazil
José Antonio Marengo
National Center for Monitoring and Early Warning of Natural Disasters (Cemaden), São José dos Campos, SP, 12247-016, Brazil
Graduate Program in Natural Disasters, São Paulo State University (UNESP)/Cemaden, São José dos Campos, SP, 12245-000, Brazil
Graduate School of International Studies, Korea University, Seoul, South Korea
Eduardo Mario Mendiondo
São Carlos School of Engineering, University of São Paulo, São Carlos, SP, 13566-590, Brazil
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Marcos Roberto Benso, Gabriela Chiquito Gesualdo, Roberto Fray Silva, Greicelene Jesus Silva, Luis Miguel Castillo Rápalo, Fabricio Alonso Richmond Navarro, Patricia Angélica Alves Marques, José Antônio Marengo, and Eduardo Mario Mendiondo
Nat. Hazards Earth Syst. Sci., 23, 1335–1354, https://doi.org/10.5194/nhess-23-1335-2023, https://doi.org/10.5194/nhess-23-1335-2023, 2023
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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.
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
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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
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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
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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
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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.
Marcos Roberto Benso, Gabriela Chiquito Gesualdo, Roberto Fray Silva, Greicelene Jesus Silva, Luis Miguel Castillo Rápalo, Fabricio Alonso Richmond Navarro, Patricia Angélica Alves Marques, José Antônio Marengo, and Eduardo Mario Mendiondo
Nat. Hazards Earth Syst. Sci., 23, 1335–1354, https://doi.org/10.5194/nhess-23-1335-2023, https://doi.org/10.5194/nhess-23-1335-2023, 2023
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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.
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
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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
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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
Battisti, R. and Sentelhas, P. C.: New agroclimatic approach for soybean sowing dates recommendation: A case study, Rev. Bras. Eng. Agr. Amb., 18, 1149–1156, https://doi.org/10.1590/1807-1929/agriambi.v18n11p1149-1156, 2014. a
Benso, M. R.: Climatic impact-drivers in the context of food security, Zenodo [data set], https://doi.org/10.5281/zenodo.12612860, 2024. a
Bray, E. A.: Plant response to water-deficit stress, Encyclopedia of Life Sciences, https://doi.org/10.1002/9780470015902.a0001298.pub2, 2007. a
Brazilian Institute of Geography and Statistics (IBGE): PAM – Municipal Agricultural Production, https://www.ibge.gov.br/en/statistics/economic/agriculture-forestry-and-fishing/16773-municipal-agricultural-production-temporary-and-permanent-crops.html?edicao=31814 (last access: 3 April 2025), 2023. a, b
Breiman, L.: Random forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001. a
Chiang, F., Mazdiyasni, O., and AghaKouchak, A.: Evidence of anthropogenic impacts on global drought frequency, duration, and intensity, Nat. Commun., 12, 2754, https://doi.org/10.1038/s41467-021-22314-w, 2021. a
Cleveland, W. S., Grosse, E., and Shyu, W. M.: Local regression models, in: Statistical models in S, edited by: Chambers, J. M. and Hastie, T. J., Chapman & Hall, ISBN 0-412-05291-l, 309–376, 2017. a
Cunha, A. P. M. d. A., Marengo, J. A., Cuartas, L. A., Tomasella, J., and Leal, K. R. D.: Drought monitoring and impacts assessment in Brazil: The CEMADEN experience, ICHARM Newsletter, http://repositorio.ufc.br/handle/riufc/59873, 2018. a
Das, S., Das, J., and Umamahesh, N.: Copula-based drought risk analysis on rainfed agriculture under stationary and non-stationary settings, Hydrolog. Sci. J., 67, 1683–1701, https://doi.org/10.1080/02626667.2022.2079416, 2022. a
Deusdará-Leal, K., Cuartas, L., Zhang, R., Mohor, G., Carvalho, L., Nobre, C., Mendiondo, E., Broedel, E., Seluchi, M., and Alvalá, R.: Implication of the new operation rules for Cantareira System: Re-reading of the 2014/2015 water crisis, Journal of Water Resource and Protection, 12, no. 4, 261–274, https://doi.org/10.4236/jwarp.2020.124016, 2019. a
Droogers, P. and Allen, R. G.: Estimating reference evapotranspiration under inaccurate data conditions, Irrigation and Drainage Systems, 16, 33–45, 2002. a
FAO: Agricultural production statistics 2000–2021, https://www.fao.org/3/cc3751en/cc3751en.pdf (last access: 10 November 2023), 2025. a
Frich, P., Alexander, L. V., Della-Marta, P., Gleason, B., Haylock, M., Tank, A. K., and Peterson, T.: Observed coherent changes in climatic extremes during the second half of the twentieth century, Clim. Res., 19, 193–212, https://doi.org/10.3354/cr019193, 2002. a, b
Gelcer, E., Fraisse, C., Dzotsi, K., Hu, Z., Mendes, R., and Zotarelli, L.: Effects of El Niño Southern Oscillation on the space–time variability of Agricultural Reference Index for Drought in midlatitudes, Agr. Forest Meteorol., 174, 110–128, https://doi.org/10.1016/j.agrformet.2013.02.006, 2013. a
Guyon, I. and Elisseeff, A.: An introduction to variable and feature selection, J. Mach. Learn. Res., 3, 1157–1182, 2003. a
Hamed, R., Van Loon, A. F., Aerts, J., and Coumou, D.: Impacts of compound hot–dry extremes on US soybean yields, Earth Syst. Dynam., 12, 1371–1391, https://doi.org/10.5194/esd-12-1371-2021, 2021. a
Han, L., Yang, G., Dai, H., Xu, B., Yang, H., Feng, H., Li, Z., and Yang, X.: Modeling maize above-ground biomass based on machine learning approaches using UAV remote-sensing data, Plant Methods, 15, 1–19, https://doi.org/10.1186/s13007-019-0394-z, 2019. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., F., Geer, A., Haimberger, L., Healy, S.,Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Quarterly J. Roy. Meteorol. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a
Hijmans, R. J.: terra: Spatial Data Analysis, R package version 1.7-29, https://CRAN.R-project.org/package=terra (last access: 15 January 2024), 2023. a
Jeong, J. H., Resop, J. P., Mueller, N. D., Fleisher, D. H., Yun, K., Butler, E. E., Timlin, D. J., Shim, K.-M., Gerber, J. S., Reddy, V. R., and Kim, S.-H.: Random forests for global and regional crop yield predictions, PLOS ONE, 11, e0156571, https://doi.org/10.1371/journal.pone.0156571, 2016. 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
Komisarczyk, K., Kozminski, P., Maksymiuk, S., and Biecek, P.: treeshap: Compute SHAP Values for Your Tree-Based Models Using the 'TreeSHAP' Algorithm, R package version 0.3.1, https://CRAN.R-project.org/package=treeshap (last access: 15 January 2024), 2024. 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, https://doi.org/10.1038/s43016-021-00341-6, 2021. a, b
Li, Y., Guan, K., Schnitkey, G. D., DeLucia, E., and Peng, B.: Excessive rainfall leads to maize yield loss of a comparable magnitude to extreme drought in the United States, Glob. Change Biol., 25, 2325–2337, https://doi.org/10.1111/gcb.14628, 2019. a
Liu, Y. and Ker, A. P.: When less is more: on the use of historical yield data with application to rating area crop insurance contracts, Journal of Agricultural and Applied Economics, 52, 194–203, https://doi.org/10.1017/aae.2019.40, 2020. a
Lundberg, S. M. and Lee, S.-I.: A unified approach to interpreting model predictions, in: Proceedings of the 31st International Conference on Neural Information Processing Systems, NIPS'17, Long Beach, California, USA, Curran Associates Inc., Red Hook, NY, USA, 4768–4777, https://doi.org/10.5555/3295222.3295230, 2017. a
MAPA: Histórico de perdas na agricultura brasileira: 2000–2021, https://www.gov.br/agricultura/pt-br/assuntos/riscos-seguro/seguro-rural/publicacoes-seguro-rural/historico-de-perdas-na-agricultura-brasileira-2000-2021.pdf (last access: 28 October 2023), 2022. a
Marengo, J. A., Alves, L. M., Alvala, R., Cunha, A. P., Brito, S., and Moraes, O. L.: Climatic characteristics of the 2010-2016 drought in the semiarid Northeast Brazil region, An. Acad. Bras. Cienc., 90, 1973–1985, https://doi.org/10.1590/0001-3765201720170206, 2017. a
Marengo, J. A., Cunha, A. P., Cuartas, L. A., Deusdará Leal, K. R., Broedel, E., Seluchi, M. E., Michelin, C. M., De Praga Baião, C. F., Chuchón Angulo, E., Almeida, E. K., Kazmierczak, M. L., Mateus, N., Pedro, A., Silva, R. C., and Bender, F.: Extreme drought in the Brazilian Pantanal in 2019–2020: characterization, causes, and impacts, Frontiers in Water, 3, 639204, https://doi.org/10.3389/frwa.2021.639204, 2021. a
Mariadass, D. A., Moung, E. G., Sufian, M. M., and Farzamnia, A.: Extreme Gradient Boosting (XGBoost) Regressor and Shapley Additive Explanation for Crop Yield Prediction in Agriculture, in: 2022 12th International Conference on Computer and Knowledge Engineering (ICCKE), 17–18 November 2022, USA, IEEE, 219–224, https://doi.org/10.1109/ICCKE57176.2022.9960069, 2022. a
Mayer, M.: shapviz: SHAP Visualizations, R package version 0.9.1, https://CRAN.R-project.org/package=shapviz (last access: 3 April 2025), 2023. a
McKee, T. B., Doesken, N. J., and Kleist, J.: Drought Monitoring with Multiple Time Scales. In Proceedings of the Ninth Conference on Applied Climatology, Dallas, TX, American Meteorological Society, 233–236, 1995. a
Moriondo, M., Giannakopoulos, C., and Bindi, M.: Climate change impact assessment: the role of climate extremes in crop yield simulation, Climatic Change, 104, 679–701, https://doi.org/10.1007/s10584-010-9871-0, 2011. a
Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., Albergel, C., Arduini, G., Balsamo, G., Boussetta, S., Choulga, M., Harrigan, S., Hersbach, H., Martens, B., Miralles, D. G., Piles, M., Rodríguez-Fernández, N. J., Zsoter, E., Buontempo, C., and Thépaut, J.-N.: ERA5-Land: a state-of-the-art global reanalysis dataset for land applications, Earth Syst. Sci. Data, 13, 4349–4383, https://doi.org/10.5194/essd-13-4349-2021, 2021. a, b, c
Ozaki, V. A., Goodwin, B. K., and Shirota, R.: Parametric and nonparametric statistical modelling of crop yield: implications for pricing crop insurance contracts, Appl. Econ., 40, 1151–1164, https://doi.org/10.1080/00036840600749680, 2008. a, b
Potapov, P., Turubanova, S., Hansen, M. C., Tyukavina, A., Zalles, V., Khan, A., Song, X.-P., Pickens, A., Shen, Q., and Cortez, J.: Global maps of cropland extent and change show accelerated cropland expansion in the twenty-first century, Nature Food, 3, 19–28, https://doi.org/10.1038/s43016-021-00429-z, 2022. a
Proctor, J., Rigden, A., Chan, D., and Huybers, P.: More accurate specification of water supply shows its importance for global crop production, Nature Food, 3, 753–763, https://doi.org/10.1038/s43016-022-00592-x, 2022. a
Pullanagari, R. R., Kereszturi, G., and Yule, I.: Integrating airborne hyperspectral, topographic, and soil data for estimating pasture quality using recursive feature elimination with random forest regression, Remote Sens.-Basel, 10, 1117, https://doi.org/10.3390/rs10071117, 2018. a
Ranasinghe, R., Ruane, A. C., Vautard, R., Arnell, N., Coppola, E., Cruz, F. A., Dessai, S., Saiful Islam, A., Rahimi, M., Carrascal, D. R., Sillmann, J., Sylla, M. B., Tebaldi, C., Wang, W., and Zaaboul, R.: Climate Change Information for Regional Impact and for Risk Assessment, in Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1767–1926, https://doi.org/10.1017/9781009157896.014, 2021. a
Ray, D. K., Gerber, J. S., MacDonald, G. K., and West, P. C.: Climate variation explains a third of global crop yield variability, Nat. Commun., 6, 5989, https://doi.org/10.1038/ncomms6989, 2015. a, b, c
Rosenzweig, C., Elliott, J., Deryng, D., Ruane, A. C., Müller, C., Arneth, A., Boote, K. J., Folberth, C., Glotter, M., Khabarov, N., Neumann, K., Piontek, F., Pugh, T. A. M., Schmid, E., Stehfest, E., Yang, H., and Jones, J. W.: Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison, P. Natl. Acad. Sci. USA, 111, 3268–3273, https://doi.org/10.1073/pnas.1222463110, 2014. a
Ruane, A. C., Vautard, R., Ranasinghe, R., Sillmann, J., Coppola, E., Arnell, N., Cruz, F. A., Dessai, S., Iles, C. E., Saiful Islam, A. K. M., Jones, R. G., Rahimi, M., Carrascal, D. R., Seneviratne, S. I., Servonnat, J., Sörensson, A. A., Sylla, M. B., Tebaldi, C., Wang, W., and Zaaboul, R.: The Climatic Impact-Driver Framework for Assessment of Risk-Relevant Climate Information, Earths Future, 10, e2022EF002803, https://doi.org/10.1029/2022EF002803, 2022. a, b
Sacks, W. J., Deryng, D., Foley, J. A., and Ramankutty, N.: Crop planting dates: an analysis of global patterns, Global Ecol. Biogeogr., 19, 607–620, https://doi.org/10.1111/j.1466-8238.2010.00551.x, 2010. a, b
Sadok, W. and Jagadish, S. K.: The hidden costs of nighttime warming on yields, Trends Plant Sci., 25, 644–651, https://doi.org/10.1016/j.tplants.2020.02.003, 2020. a
Santini, M., Noce, S., Antonelli, M., and Caporaso, L.: Complex drought patterns robustly explain global yield loss for major crops, Sci. Rep.-UK, 12, 5792, https://doi.org/10.1038/s41598-022-09611-0, 2022. a
Sarhadi, A., Ausín, M. C., Wiper, M. P., Touma, D., and Diffenbaugh, N. S.: Multidimensional risk in a nonstationary climate: Joint probability of increasingly severe warm and dry conditions, Science Advances, 4, eaau3487, https://doi.org/10.1126/sciadv.aau3487, 2018. a
Schierhorn, F., Hofmann, M., Gagalyuk, T., Ostapchuk, I., and Müller, D.: Machine learning reveals complex effects of climatic means and weather extremes on wheat yields during different plant developmental stages, Climatic Change, 169, 39, https://doi.org/10.1007/s10584-021-03272-0, 2021. a, b, c
Schyns, J. F., Hoekstra, A. Y., and Booij, M. J.: Review and classification of indicators of green water availability and scarcity, Hydrol. Earth Syst. Sci., 19, 4581–4608, https://doi.org/10.5194/hess-19-4581-2015, 2015. a
Sidhu, B. S., Mehrabi, Z., Ramankutty, N., and Kandlikar, M.: How can machine learning help in understanding the impact of climate change on crop yields?, Environ. Res. Lett., 13, 345–359, https://doi.org/10.1088/1748-9326/acb164, 2023. a, b
Silva Fuzzo, D. F., Carlson, T. N., Kourgialas, N. N., and Petropoulos, G. P.: Coupling remote sensing with a water balance model for soybean yield predictions over large areas, Earth Sci. Inform., 13, 345–359, https://doi.org/10.1007/s12145-019-00424-w, 2020. a
Sinnathamby, S., Douglas-Mankin, K. R., and Craige, C.: Field-scale calibration of crop-yield parameters in the Soil and Water Assessment Tool (SWAT), Agr. Water Manage., 180, 61–69, https://doi.org/10.1016/j.agwat.2016.10.024, 2017. a
Strumbelj, E. and Kononenko, I.: An Efficient Explanation of Individual Classifications using Game Theory, J. Mach. Learn. Res., 11, 1–18, 2010. a
Štrumbelj, E. and Kononenko, I.: Explaining prediction models and individual predictions with feature contributions, Knowl. Inf. Syst., 41, 647–665, 2014. a
Svetnik, V., Liaw, A., Tong, C., and Wang, T.: Application of Breiman's random forest to modeling structure-activity relationships of pharmaceutical molecules, in: Multiple Classifier Systems: 5th International Workshop, MCS 2004, Cagliari, Italy, 9–11 June, 2004, Proceedings 5, Springer, 334–343, https://doi.org/10.1007/978-3-540-25966-4_33, 2004. a
UNDRR: What is the Sendai Framework for Disaster Risk Reduction?, undrr.org, https://www.undrr.org/implementing-sendai-framework/what-sendai-framework (last access: 24 October 2023), 2025. a
Vapnik, V. N.: An overview of statistical learning theory, IEEE T. Neural Networ., 10, 988–999, 1999. a
Viana, C. M., Santos, M., Freire, D., Abrantes, P., and Rocha, J.: Evaluation of the factors explaining the use of agricultural land: A machine learning and model-agnostic approach, Ecol. Indic., 131, 108200, https://doi.org/10.1016/j.ecolind.2021.108200, 2021. a
Vicente-Serrano, S. M., Beguería, S., and López-Moreno, J. I.: A multiscalar drought index sensitive to global warming: the standardized precipitation evapotranspiration index, J. Climate, 23, 1696–1718, 2010. a
von Bloh, M., Júnior, R. d. S. N., Wangerpohl, X., Saltık, A. O., Haller, V., Kaiser, L., and Asseng, S.: Machine learning for soybean yield forecasting in Brazil, Agr. Forest Meteorol., 341, 109670, https://doi.org/10.1016/j.agrformet.2023.109670, 2023. a
Wang, Y. and Li, Y.: Mapping the ratoon rice suitability region in China using random forest and recursive feature elimination modeling, Field Crop. Res., 301, 109016, https://doi.org/10.1016/j.fcr.2023.109016, 2023. a
Wikle, C. K., Datta, A., Hari, B. V., Boone, E. L., Sahoo, I., Kavila, I., Castruccio, S., Simmons, S. J., Burr, W. S., and Chang, W.: An illustration of model agnostic explainability methods applied to environmental data, Environmetrics, 34, e2772, https://doi.org/10.1002/env.2772, 2023. a
Wright, M. N. and Ziegler, A.: ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R, J. Stat. Softw., 77, 1–17, https://doi.org/10.18637/jss.v077.i01, 2017. a
Yang, S.-R., Koo, W. W., and Wilson, W. W.: Heteroskedasticity in crop yield models, J. Agr. Resour. Econ., 17, 103–109, 1992. a
Yu, L. and Liu, H.: Feature selection for high-dimensional data: A fast correlation-based filter solution, in: Proceedings of the 20th international conference on machine learning (ICML-03), Washington, DC, USA, 856–863, https://doi.org/10.5555/3041838.3041946, 2003. a
Zhu, Y., Goodwin, B. K., and Ghosh, S. K.: Modeling yield risk under technological change: Dynamic yield distributions and the US crop insurance program, J. Agr. Resour. Econ., 36, 192–210, 2011. a
Zscheischler, J., Westra, S., Van Den Hurk, B. J., Seneviratne, S. I., Ward, P. J., Pitman, A., AghaKouchak, A., Bresch, D. N., Leonard, M., Wahl, T., and Zhang, X.: Future climate risk from compound events, Nat. Clim. Change, 8, 469–477, 2018. a
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.
This study applies climate extreme indices to assess climate risks to food security. Using an...
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