Key Climatic Drivers of Droughts and Floods in Henan, China (1955–2015): Insights from Random Forest and Spatial Analysis
DOI:
https://doi.org/10.15377/2410-3624.2026.1.3Keywords:
China, Henan, Climatic drivers, Spatial statistics, Flood prediction, Drought prediction, Random forest modeling.Abstract
Global climate change continues to occur and induces a series of severe ecological consequences, including an increased risk of droughts and floods. Extensive studies have explored the climatic drivers that may trigger the occurrence of disasters. However, monthly climatic factors have rarely been employed at the county scale for long-term drought and flood assessment. Henan Province, located in central China, is a region prone to natural disasters. This study is dedicated to conducting training and validation analyses of drought and flood intensities in Henan from 1955 to 2015, based on monthly and annual climatic factors using random forest modeling, spatial statistical description, and geographical information system (GIS) techniques. A total of 100 climatic variables were initially considered, and the most important predictors were identified through variable importance ranking in the random forest framework. Model performance was evaluated using out-of-bag (OOB) error and independent validation statistics. The results indicate that early spring temperature serves as a robust signal for predicting annual drought and flood intensities, although slight differences may exist in the temperatures from January to March across the three sub-climatic zones of Henan. This study provides valuable insights into predicting droughts and floods based on early spring temperature, thereby supporting mitigation measures for natural disasters that are expected to increase under global warming.
References
[1] Zhang Q, Gu X, Singh VP, Xu CY, Kong D, Xiao M, et al. Homogenization of precipitation and flow regimes across China: Changing properties, causes and implications. J Hydrol. 2015; 530: 462-75. https://doi.org/10.1016/j.jhydrol.2015.09.041
[2] Garschagen M, Doshi D, Reith J, Hagenlocher M. Global patterns of disaster and climate risk-an analysis of the consistency of leading index-based assessments and their results. Clim Change. 2021; 169: 11. https://doi.org/10.1007/s10584-021-03209-7
[3] Easterling DR, Meehl GA, Parmesan C, Changnon SA, Karl TR, Mearns LO. Climate extremes: observations, modeling and impacts. Science. 2000; 289(5487): 2068-74. https://doi.org/10.1126/science.289.5487.2068
[4] Hu P, Zhang Q, Shi P, Chen B, Fang J. Flood-induced mortality across the globe: Spatiotemporal pattern and influencing factors. Sci Total Environ. 2018; 643: 171-82. https://doi.org/10.1016/j.scitotenv.2018.06.197
[5] Peng Y, Song JY, Cui TT, Cheng X. Temporal-spatial variability of atmospheric and hydrological natural disasters during recent 500 years in Inner Mongolia, China. Nat Hazards. 2017; 89: 441-56. https://doi.org/10.1007/s11069-017-2973-5
[6] Yi C, Rustic G, Xu X. Climate extremes and grassland potential productivity. Environ Res Let. 2012; 7: 035703. https://doi.org/10.1088/1748-9326/7/3/035703
[7] Wang X, Hou X, Li Z, Wang Y. Spatial and temporal characteristics of meteorological drought in Shandong province, China, from 1961 to 2008. Advan Meteor. 2014; 2014: 873593. https://doi.org/10.1155/2014/873593
[8] Chen HP, Sun JQ. Changes in drought characteristics over China using the standardized precipitation evapotranspiration index. J Clim. 2015; 28: 5430-47. https://doi.org/10.1175/JCLI-D-14-00707.1
[9] Stott P. How climate change affects extreme weather events. Science. 2016; 352(6293): 1517-18. https://doi.org/10.1126/science.aaf7271
[10] Peng Y, Long S, Ma J, Song J, Liu Z. Temporal-spatial variability in correlations of drought and flood during recent 500 years in Inner Mongolia, China. Sci Total Environ. 2018; 633: 484-91. https://doi.org/10.1016/j.scitotenv.2018.03.200
[11] Kim D, Chun JA, Aikins CM. An hourly-scale scenario-neutral flood risk assessment in a mesoscale catchment under climate change. Hydrol Proc. 2018; 32: 3416-30. https://doi.org/10.1002/hyp.13273
[12] Rogers JS, Maneta MP, Sain SR, Madaus LE, Hacker JP. The role of climate and population change in global flood exposure and vulnerability. Nat Commun. 2025; 16: 1287. https://doi.org/10.1038/s41467-025-56654-8
[13] Vautard RP, Yiou F, Andrea D, de Noblet N, Viovy N, Cassou J, et al. Summertime European heat and drought waves induced by wintertime Mediterranean rainfall deficit. Geophys Res Lett. 2007; 34: L07711. https://doi.org/10.1029/2006GL028001
[14] Sang-Hyun L, Seung-Hwan Y, Jin-Yong C, Seungjong B. Assessment of the impact of climate change on drought characteristics in the Hwanghae plain north Korea using time series SPI and SPEI: 1981-2100. Water. 2017; 9(8): 579. https://doi.org/10.3390/w9080579
[15] Chiang F, Mazdiyasni O, AghaKouchak A. Amplified warming of droughts in southern United States in observations and model simulations. Sci Adv. 2018; 4: eaat2380. https://doi.org/10.1126/sciadv.aat2380
[16] Blöschl G, Hall J, Parajka J, Rui A, Perdigão P, Merz B, et al. Changing climate shifts timing of European floods. Science. 2017; 357(6351): 588-90. https://doi.org/10.1126/science.aan2506
[17] Allan RP, Soden BJ. Atmospheric warming and the amplification of precipitation extremes. Science. 2008; 321: 1481-4. https://doi.org/10.1126/science.1160787
[18] Almazroui M, Saeed F, Islam MN, Alkhalaf AK. Assessing the robustness and uncertainties of projected changes in temperature and precipitation in AR5 global climate models over the Arabian peninsula. Atmos Res. 2016; 182: 163-75. https://doi.org/10.1016/j.atmosres.2016.07.025
[19] Dai A. Drought under global warming: a review. Clim Change. 2011; 2(1): 45-65. https://doi.org/10.1002/wcc.81
[20] Trenberth KE, Dai A, van der Schrier G, Jones PD, Barichivich J, Briffa KR, Sheffield J. Global warming and changes in drought. Nat Clim Chang. 2014; 4(1): 17-22. https://doi.org/10.1038/nclimate2067
[21] Zhai J, Huang J, Su B, Cao L, Fischer T. Intensity-area-duration analysis of droughts in China 1960-2013. Clim Dyn. 2016; 48: 151-68. https://doi.org/10.1007/s00382-016-3066-y
[22] Hermans TDG, Šakić Trogrlić R, van den Homberg MJC, Bailon H, Sarku R, Mosurska A, et al. Exploring the integration of local and scientific knowledge in early warning systems for disaster risk reduction: a review. Nat Hazards. 2022; 114: 1125-52. https://doi.org/10.1007/s11069-022-05468-8
[23] Raoufi A, Tsubaki K. Reviving ancestral water management practices: A sustainable and resilient design approach addressing the flood-drought paradox in rural Ahvaz, Iran. J Environ Manage. 2025 Jun; 385: 125476. https://doi.org/10.1016/j.jenvman.2025.125476.
[24] He C, Li T. Does global warming amplify interannual climate variability? Clim Dyn. 2019; 52: 2667-84. https://doi.org/10.1007/s00382-018-4286-0
[25] Wang Z, Yuan J, Peng Y, Wang C, Li G. 500-Year Records Demonstrating a Sharp Increase in Intensities of Three Natural Hazards at Multiple Spatiotemporal Scales in China. Glob Environ Eng. 2023; 10: 18-32. https://doi.org/10.15377/2410-3624.2023.10.3
[26] Wang Y, Zhang Q. Distribution characteristics of drought and flood hazards in northern China against the background of climate warming. Nat Hazards. 2024; 120: 5987-6009. https://doi.org/10.1007/s11069-024-06468-6.
[27] Henan Hydrological Station. Historical Hydrological and Meteorological Disasters of Henan Province. Inner Materials Complied and Printed by Henan Hydrological Station; 1983.
[28] Jia L, Vecchi GA, Yang XS, Gudgel RG, Delworth TL, Stern WF, et al. The roles of radiative forcing, sea surface temperatures, and atmospheric and land initial conditions in U.S. summer warming episodes. J Clim. 2016; 29: 4121-35. https://doi.org/10.1175/JCLI-D-15-0471.1
[29] Murakami H, Vecchi GA, Underwood S. Increasing frequency of extremely severe cyclonic storms over the Arabian Sea. Nat Clim Change. 2017; 7: 885-89. https://doi.org/10.1038/s41558-017-0008-6
[30] Myhre G, Alterskjær K, Stjern CW, Hodnebrog Ø, Marelle L, Samset BH, et al. Frequency of extreme precipitation increases extensively with event rareness under global warming. Sci Rep. 2019; 9: 16063. https://doi.org/10.1038/s41598-019-52277-4
[31] Nida H, Kashif M, Janjua AA, Aslam M, Cheema KS, Ullah S. Impacts of climate change on Pakistan's weather patterns: a comprehensive study of temperature and precipitation trends. Environ Monit Assess. 2025; 197(5): 509. https://doi.org/10.1007/s10661-025-13931-9
[32] Breiman L. Random forest. Machine Learn. 2001; 45: 5-32. https://doi.org/10.1023/A:1010933404324
[33] Liaw A, Wiener M. Classification and regression by randomForest. R News. 2002; 2(3): 18-22.
[34] Cutler DR, Edwards TC Jr, Beard KH, Cutler A, Hess KT, Gibson J, et al. Random forests for classification in ecology. Ecology. 2007; 88(11): 2783-92. https://doi.org/10.1890/07-0539.1
[35] Abebaw SE. A global review of the impacts of climate change and variability on agricultural productivity and farmers' adaptation strategies. Food Sci Nutr. 2025; 13: e70260. https://doi.org/10.1002/fsn3.70260
[36] Wang N, Sun F, Yang S, Feng Y, Sun FQ, Wang ZG. Flood fatalities and displacement influence human migration in floodplains of developing countries. Commun Earth Environ. 2025; 6: 319. https://doi.org/10.1038/s43247-025-02293-2
[37] Shi BL, Zhu XY, Hu YC, Yang YY. Drought characteristics of Henan province in 1961-2013 based on standardized precipitation evapotranspiration index. J Geogr Sci. 2017; 27(3): 311-25. https://doi.org/10.1007/s11442-017-1378-4
[38] Tong Y, Chen Y, Qu Y, Bento VA, Song H, Qiu H, et al. Evolution and prediction of drought-flood abrupt alternation in mainland China using an improved index. Clim Dyn. 2025; 63: 392. https://doi.org/10.1007/s00382-025-07885-4
[39] Rajsekhar D, Gorelick SM. Increasing drought in Jordan: climate change and cascading Syrian land-use impacts on reducing transboundary flow. Sci Adv. 2017; 3(8): e1700581. https://doi.org/10.1126/sciadv.1700581
[40] IPCC. 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: Cambridge University Press; 2021.
[41] Editorial Board of Meteorological Disasters in China (EBMDC). The Meteorological Disasters in China. Henan Volume. Beijing: China Meteorological Press; 2005: pp. 11-116.
[42] Kuang XY, Zhu YY, Xu SP, Pan P. Analysis of the change of flood and abnormal characters of atmospheric circulation in typical year over Henan Province. Meteor Environ Sci. 2010; 3(2): 20-5.
[43] Wilby RL, Troni J, Biot Y, Tedd L, Hewitson BC, Smith DM, et al. A review of climate risk information for adaptation and development planning. Inter J Climatol. 2009; 29: 1193-1215. https://doi.org/10.1002/joc.1839
[44] Seyoum M. Impact of climate change and El Niño episodes on droughts in sub-Saharan Africa. Clim Dyn. 2016; 49: 1-18. https://doi.org/10.1007/s00382-016-3366-2
[45] Zscheischler J, Westra S, van den Hurk BJJM, Seneviratne SI, Ward PJ, Pitman A, et al. Future climate risk from compound events. Nat Clim Change. 2020; 10(7): 611-7.
[46] Raymond C, Horton RM, Zscheischler J, Martius O, AghaKouchak A, Balch J, et al. Understanding and managing connected extreme events. Nat Clim Change. 2020; 10(7): 611-21. https://doi.org/10.1038/s41558-020-0790-4
[47] Zhang JJ, Guo ZF, Li ZG. Research on time and spatial characteristics of flood and drought disasters risk in Henan. J Nat Resour. 2013; 28(6): 957-68. https://doi.org/10.11849/zrzyxb.2013.06.007
[48] Fasihi S, Lim WZ, Wu W, Proverbs D. Key Drivers in Flood and Drought Risk Assessment: Unveiling Risk Factors through a Multi-Metric Network Approach. Water Resour. 2025; 52: 885-903. https://doi.org/10.1134/S0097807824606320
[49] Keraghel MA, Gaouaou F. Assessing flood dynamics in Algiers, Algeria: influence of land cover change and precipitation regimes. Theor Appl Climatol. 2025; 156: 15. https://doi.org/10.1007/s00704-024-05226-9
[50] van der Wiel K, Kapnick SB, Vecchi GA, Smith JA, Milly PCD, Jia L. 100-year lower Mississippi floods in a global climate model: characteristics and future changes. J Hydrometeoro. 2018; 19(10): 1547-63. https://doi.org/10.1175/JHM-D-18-0018.1
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Ling Yao, Ying Ye, Xiaoyue Zhang, Yu Peng
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
All the published articles are licensed under the terms of the Creative Commons Attribution Non-Commercial License (CC BY-NC 4.0) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.
