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Threat of low-frequency high-intensity floods to global cropland and crop yields

Nature Sustainability (2024)Cite this article

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Abstract

Flood hazards pose a significant threat to agricultural production. Agricultural adaptations tend to be prevalent and systematic in high-frequency flood (HFF) areas but neglected in low-frequency flood (LFF) areas. Here, using satellite imagery, we map global spatial distributions of LFF and HFF at 250 m resolution for 3,427 flood events between 2000 and 2021. We show that LFF affected a larger proportion of cropland area (4.7%) than HFF (1.2%), and HFF occurred in smaller regions with less intensity. Cropland expansion between 2000 and 2019 increased the area affected by LFF (3.1 × 104 km2) more than that affected by HFF (1.3 × 104 km2). Moreover, the mean yield losses of wheat and rice from LFF were greater than those from HFF, owing to the higher precipitation anomalies, soil moisture anomalies and greater crop flooding during their growing seasons. Our findings highlight the urgency of this issue and identify priority areas to prevent these neglected low-frequency but high-impact floods, providing valuable information for developing flood-adapted policy.

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Fig. 1: Global patterns of cropland exposed to flooding from 2000 to 2021.
Fig. 2: Changes in flood-affected cropland area due to global cropland change from 2000 to 2019.
Fig. 3: MYA in LFF and HFF areas.
Fig. 4: Relationship between the inundation period and crop phenology during flooding events (2000–2021).
Fig. 5: PA/SMA during flooding and crop growth anomalies after flooding.
Fig. 6: Different crop yield impacts from LFF and HFF.

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Data availability

The global flood events database from the DFO is accessible at https://floodobservatory.colorado.edu/temp/. The HydroSHEDS Basins Level 6 watersheds database is accessible at https://developers.google.com/earth-engine/datasets/catalog/WWF_HydroSHEDS_v1_Basins_hybas_6#description. The HAND data are accessible at https://gee-community-catalog.org/projects/hand/. MOD09GQ is accessible at https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD09GQ. MYD09GQ is accessible at https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD09GQ. MOD09GA is accessible at https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD09GA. MYD09GA is accessible at https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD09GA. MCD12Q2 is accessible at https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD12Q2. MOD09Q1 is accessible at https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD09Q1. The global surface water dataset is accessible at https://developers.google.com/earth-engine/datasets/catalog/JRC_GSW1_4_YearlyHistory#description. The cropland datasets are accessible at https://glad.umd.edu/dataset/croplands. The flood verification points are accessible at https://github.com/cloudtostreet/MODIS_GlobalFloodDatabase. The crop calendar product (GGCMI Phase III) is accessible via Zenodo at https://doi.org/10.5281/zenodo.5062513 (ref. 62). CHIRPS is accessible at https://developers.google.com/earth-engine/datasets/catalog/UCSB-CHG_CHIRPS_DAILY. IMERG is accessible at https://developers.google.com/earth-engine/datasets/catalog/NASA_GPM_L3_IMERG_V06. ERA5-Land is accessible at https://developers.google.com/earth-engine/datasets/catalog/ECMWF_ERA5_LAND_DAILY_AGGR. The crop area datasets are accessible at https://mapspam.info/. The global land cover and land use change datasets are accessible at https://glad.umd.edu/dataset/GLCLUC2020. Spatial data for all countries and their subdivisions are available at https://gadm.org/. The crop yield data are available in the Supplementary Information (Supplementary Table 12).

Code availability

The key code for mapping the inundation extent and duration of global flood events is available via Zenodo at https://doi.org/10.5281/zenodo.11181120 (ref. 63).

References

  1. Qamer, F. M. et al. A framework for multi-sensor satellite data to evaluate crop production losses: the case study of 2022 Pakistan floods. Sci. Rep. 13, 4240 (2023).

    Article  CAS  Google Scholar 

  2. Dryden, R., Anand, M., Lehner, B. & Fluet-Chouinard, E. Do we prioritize floodplains for development and farming? Mapping global dependence and exposure to inundation. Glob. Environ. Change 71, 102370 (2021).

    Article  Google Scholar 

  3. Kim, W., Iizumi, T. & Nishimori, M. Global patterns of crop production losses associated with droughts from 1983 to 2009. J. Appl. Meteorol. Climatol. 58, 1233–1244 (2019).

    Article  Google Scholar 

  4. Lesk, C., Rowhani, P. & Ramankutty, N. Influence of extreme weather disasters on global crop production. Nature 529, 84–87 (2016).

    Article  CAS  Google Scholar 

  5. Schewe, J. et al. State-of-the-art global models underestimate impacts from climate extremes. Nat. Commun. 10, 1005 (2019).

    Article  Google Scholar 

  6. Liu, K. et al. Silver lining to a climate crisis in multiple prospects for alleviating crop waterlogging under future climates. Nat. Commun. 14, 765 (2023).

    Article  CAS  Google Scholar 

  7. Li, Y., Guan, K., Schnitkey, G. D., DeLucia, E. & 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 (2019).

    Article  Google Scholar 

  8. Hirabayashi, Y., Tanoue, M., Sasaki, O., Zhou, X. & Yamazaki, D. Global exposure to flooding from the new CMIP6 climate model projections. Sci. Rep. 11, 3740 (2021).

    Article  CAS  Google Scholar 

  9. Rodell, M. & Li, B. Changing intensity of hydroclimatic extreme events revealed by GRACE and GRACE-FO. Nat. Water 1, 241–248 (2023).

    Article  Google Scholar 

  10. Zhang, S. et al. Reconciling disagreement on global river flood changes in a warming climate. Nat. Clim. Change 12, 1160–1167 (2022).

    Article  Google Scholar 

  11. Chen, H., Liang, Q., Liang, Z., Liu, Y. & Xie, S. Remote-sensing disturbance detection index to identify spatio-temporal varying flood impact on crop production. Agric. For. Meteorol. 269–270, 180–191 (2019).

    Article  Google Scholar 

  12. Li, S., Tompkins, A. M., Lin, E. & Ju, H. Simulating the impact of flooding on wheat yield—case study in East China. Agric. For. Meteorol. 216, 221–231 (2016).

    Article  Google Scholar 

  13. Shirzaei, M. et al. Persistent impact of spring floods on crop loss in U.S. Midwest. Weather Clim. Extrem. 34, 100392 (2021).

    Article  Google Scholar 

  14. Fu, J. et al. Extreme rainfall reduces one-twelfth of China’s rice yield over the last two decades. Nat. Food https://doi.org/10.1038/s43016-023-00753-6 (2023).

  15. Kim, W., Iizumi, T., Hosokawa, N., Tanoue, M. & Hirabayashi, Y. Flood impacts on global crop production: advances and limitations. Environ. Res. Lett. 18, 054007 (2023).

    Article  Google Scholar 

  16. Venkatappa, M., Sasaki, N., Han, P. & Abe, I. Impacts of droughts and floods on croplands and crop production in Southeast Asia—an application of Google Earth Engine. Sci. Total Environ. 795, 148829 (2021).

    Article  CAS  Google Scholar 

  17. Ray, D. K. et al. Climate change has likely already affected global food production. PLoS ONE 14, e0217148 (2019).

    Article  CAS  Google Scholar 

  18. Martinis, S., Groth, S., Wieland, M., Knopp, L. & Rättich, M. Towards a global seasonal and permanent reference water product from Sentinel-1/2 data for improved flood mapping. Remote Sens. Environ. 278, 113077 (2022).

    Article  Google Scholar 

  19. Pekel, J.-F., Cottam, A., Gorelick, N. & Belward, A. S. High-resolution mapping of global surface water and its long-term changes. Nature 540, 418–422 (2016).

    Article  CAS  Google Scholar 

  20. Potapov, P. et al. Global maps of cropland extent and change show accelerated cropland expansion in the twenty-first century. Nat. Food 3, 19–28 (2022).

    Article  Google Scholar 

  21. Banerjee, L. Effects of flood on agricultural productivity in Bangladesh. Oxf. Dev. Stud. 38, 339–356 (2010).

    Article  Google Scholar 

  22. Reed, C. et al. The impact of flooding on food security across Africa. Proc. Natl Acad. Sci. USA 119, e2119399119 (2022).

    Article  CAS  Google Scholar 

  23. Yin, J. et al. Flash floods: why are more of them devastating the world’s driest regions? Nature 615, 212–215 (2023).

    Article  CAS  Google Scholar 

  24. Policelli, F. et al. The NASA Global Flood Mapping System. in Remote Sensing of Hydrological Extremes (ed. Lakshmi, V.) 47–63 (Springer, 2017).

  25. Tellman, B. et al. Satellite imaging reveals increased proportion of population exposed to floods. Nature 596, 80–86 (2021).

    Article  CAS  Google Scholar 

  26. Nigro, J., Slayback, D., Policelli, F. & Brakenridge, G. R. NASA/DFO MODIS near real-time (NRT) global flood mapping product evaluation of flood and permanent water detection. Technical Report 1–27 (NASA Goddard Space Flight Center, 2014).

  27. Hansen, M. C. et al. Global land use extent and dispersion within natural land cover using Landsat data. Environ. Res. Lett. 17, 034050 (2022).

    Article  Google Scholar 

  28. Song, X.-P. et al. Global land change from 1982 to 2016. Nature 560, 639–643 (2018).

    Article  CAS  Google Scholar 

  29. Zhang, S. & Wang, B. Global summer monsoon rainy seasons. Int. J. Climatol. 28, 1563–1578 (2008).

    Article  Google Scholar 

  30. Balke, T. & Nilsson, C. Increasing synchrony of annual river-flood peaks and growing season in Europe. Geophys. Res. Lett. 46, 10446–10453 (2019).

    Article  Google Scholar 

  31. Ficchì, A. & Stephens, L. Climate variability alters flood timing across Africa. Geophys. Res. Lett. 46, 8809–8819 (2019).

    Article  Google Scholar 

  32. Jägermeyr, J. et al. Climate impacts on global agriculture emerge earlier in new generation of climate and crop models. Nat. Food 2, 873–885 (2021).

    Article  Google Scholar 

  33. Cea, L. & Fraga, I. Incorporating antecedent moisture conditions and intraevent variability of rainfall on flood frequency analysis in poorly gauged basins. Water Resour. Res. 54, 8774–8791 (2018).

    Article  Google Scholar 

  34. Wasko, C., Nathan, R. & Peel, M. C. Changes in antecedent soil moisture modulate flood seasonality in a changing climate. Water Resour. Res. 56, e2019WR026300 (2020).

    Article  Google Scholar 

  35. Tramblay, Y., Villarini, G., El Khalki, E. M., Gründemann, G. & Hughes, D. Evaluation of the drivers responsible for flooding in Africa. Water Resour. Res. 57, e2021WR029595 (2021).

    Article  Google Scholar 

  36. Bofana, J. et al. How long did crops survive from floods caused by Cyclone Idai in Mozambique detected with multi-satellite data. Remote Sens. Environ. 269, 112808 (2022).

    Article  Google Scholar 

  37. Shrestha, B. B., Kawasaki, A. & Zin, W. W. Development of flood damage functions for agricultural crops and their applicability in regions of Asia. J. Hydrol. Reg. Stud. 36, 100872 (2021).

    Article  Google Scholar 

  38. Bolton, D. K. & Friedl, M. A. Forecasting crop yield using remotely sensed vegetation indices and crop phenology metrics. Agric. For. Meteorol. 173, 74–84 (2013).

    Article  Google Scholar 

  39. Tangdamrongsub, N., Forgotson, C., Gangodagamage, C. & Forgotson, J. The analysis of using satellite soil moisture observations for flood detection, evaluating over the Thailand’s Great Flood of 2011. Nat. Hazards 108, 2879–2904 (2021).

    Article  Google Scholar 

  40. Pyka, L., Al-Maruf, A., Shamsuzzoha, M., Jenkins, J. & Braun, B. Floating gardening in coastal Bangladesh: evidence of sustainable farming for food security under climate change. J. Agric. Food Environ. 1, 161–168 (2020).

    Article  Google Scholar 

  41. Varela, R. P., Apdohan, A. G. & Balanay, R. M. Climate resilient agriculture and enhancing food production: field experience from Agusan del Norte, Caraga Region, Philippines. Front. Sustain. Food Syst. 6, 974789 (2022).

  42. Gommes, R., Wu, B., Li, Z. & Zeng, H. Design and characterization of spatial units for monitoring global impacts of environmental factors on major crops and food security. Food Energy Secur. 5, 40–55 (2016).

    Article  Google Scholar 

  43. Spence, A., Poortinga, W., Butler, C. & Pidgeon, N. F. Perceptions of climate change and willingness to save energy related to flood experience. Nat. Clim. Change 1, 46–49 (2011).

    Article  Google Scholar 

  44. Hirst, S. M. & Ibrahim, A. M. Effects of flood protection on soil fertility in a Riverine floodplain area in Bangladesh. Commun. Soil Sci. Plant Anal. 27, 119–156 (1996).

    Article  CAS  Google Scholar 

  45. Kaur, G. et al. Impacts and management strategies for crop production in waterlogged or flooded soils: a review. Agron. J. 112, 1475–1501 (2020).

    Article  Google Scholar 

  46. Sharma, R. K. et al. Impact of recent climate change on corn, rice, and wheat in southeastern USA. Sci. Rep. 12, 16928 (2022).

    Article  CAS  Google Scholar 

  47. Najibi, N. & Devineni, N. Recent trends in the frequency and duration of global floods. Earth Syst. Dyn. 9, 757–783 (2018).

    Article  Google Scholar 

  48. He, X., Pan, M., Wei, Z., Wood, E. F. & Sheffield, J. A global drought and flood catalogue from 1950 to 2016. Bull. Am. Meteorol. Soc. 101, E508–E535 (2020).

    Article  Google Scholar 

  49. Lehner, B. & Grill, G. Global river hydrography and network routing: baseline data and new approaches to study the world’s large river systems. Hydrol. Process. 27, 2171–2186 (2013).

    Article  Google Scholar 

  50. Marsalek, J., Stancalie, G. & Balint, G. Transboundary Floods: Reducing Risks through Flood Management Vol. 72 (Springer Science & Business Media, 2006).

  51. Nobre, A. D. et al. HAND contour: a new proxy predictor of inundation extent. Hydrol. Process. 30, 320–333 (2016).

    Article  Google Scholar 

  52. Meng, Z. et al. Post-2020 biodiversity framework challenged by cropland expansion in protected areas. Nat. Sustain. https://doi.org/10.1038/s41893-023-01093-w (2023).

  53. Dai, Y. Coastal phytoplankton blooms expand and intensify in the 21st century. Nature 615, 280–284 (2023).

    Article  CAS  Google Scholar 

  54. Potapov, P. et al. The global 2000–2020 land cover and land use change dataset derived from the Landsat archive: first results. Front. Remote Sens. 3, 856903 (2022).

    Article  Google Scholar 

  55. Cao, J. et al. Forecasting global crop yields based on El Nino Southern Oscillation early signals. Agric. Syst. 205, 103564 (2023).

    Article  Google Scholar 

  56. Iizumi, T. et al. Impacts of El Niño Southern Oscillation on the global yields of major crops. Nat. Commun. 5, 3712 (2014).

    Article  CAS  Google Scholar 

  57. Gray, J., Sulla-Menashe, D. & Friedl, M. A. User Guide to Collection 6 MODIS Land Cover Dynamics (MCD12Q2) Product (NASA EOSDIS LP DAAC, 2019).

  58. Jiang, Z., Huete, A., Didan, K. & Miura, T. Development of a two-band enhanced vegetation index without a blue band. Remote Sens. Environ. 112, 3833–3845 (2008).

    Article  Google Scholar 

  59. Funk, C. et al. The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes. Sci. Data 2, 150066 (2015).

    Article  Google Scholar 

  60. Pradhan, R. K. et al. Review of GPM IMERG performance: a global perspective. Remote Sens. Environ. 268, 112754 (2022).

    Article  Google Scholar 

  61. Muñoz-Sabater, J. et al. ERA5-Land: a state-of-the-art global reanalysis dataset for land applications. Earth Syst. Sci. Data 13, 4349–4383 (2021).

    Article  Google Scholar 

  62. Jägermeyr, J., Müller, C., Minoli, S., Ray, D. & Siebert, S. GGCMI Phase 3 crop calendar. Zenodo https://doi.org/10.5281/zenodo.5062513 (2021).

  63. Han, J., & Zhang, Z. The code for mapping the inundation extent and duration of flood events. Zenodo https://doi.org/10.5281/zenodo.11181120 (2024).

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Acknowledgements

We acknowledge financial support from the National Natural Science Foundation of China (grant nos 42061144003 (Z.Z.) and 42301187 (J.X.)) and the CAS-CSRIO cooperation project (grant no.177GJHZ2022052MI (F.T.)).

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Authors and Affiliations

  1. Joint International Research Laboratory of Catastrophe Simulation and Systemic Risk Governance, School of National Safety and Emergency Management, Beijing Normal University, Beijing, P. R. China

    Jichong Han, Zhao Zhang, Jialu Xu, Juan Cao, Fei Cheng, Huimin Zhuang, Huaqing Wu, Qinghang Mei & Jie Song

  2. School of Systems Science, Beijing Normal University, Beijing, P. R. China

    Jichong Han & Jie Song

  3. Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, P. R. China

    Yi Chen & Fulu Tao

  4. NASA Goddard Institute for Space Studies, New York, NY, USA

    Jonas Jägermeyr

  5. Center for Climate Systems Research, Columbia University, New York, NY, USA

    Jonas Jägermeyr

  6. Potsdam Institute for Climate Impacts Research, Leibniz Association, Potsdam, Germany

    Jonas Jägermeyr

  7. Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, School of Geographical Sciences, Southwest University, Chongqing, P. R. China

    Yuchuan Luo

  8. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, P. R. China

    Fulu Tao

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  1. Jichong Han
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Contributions

Z.Z., F.T. and J.H. conceptualized the project. J.H., Z.Z., J.X. and J.C. designed the methodology. J.H., Y.C., J.C., Y.L. and F.C. undertook the formal analyses. J.H., J.C., Z.Z., Y.C. and H.Z. performed the investigations. Z.Z. and F.T. supervised the work. J.H., J.X. and J.C. validated the results. J.H., H.W., Q.M. and J.S. visualized the data. J.H., Z.Z. and F.T. wrote the first draft. J.J., Z.Z., F.T. and J.X. contributed to the review and editing of the paper.

Corresponding authors

Correspondence to Zhao Zhang or Fulu Tao.

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Nature Sustainability thanks Sawaid Abbas, Manoochehr Shirzaei and Feng Zhou for their contribution to the peer review of this work.

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Supplementary Information

Supplementary Methods 1–4, Figs. 1–58 and Tables 1–12.

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Han, J., Zhang, Z., Xu, J. et al. Threat of low-frequency high-intensity floods to global cropland and crop yields. Nat Sustain (2024). https://doi.org/10.1038/s41893-024-01375-x

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