Abstract
Exploring the effects of meteorological factors on drought dynamic is of important significance for in-depth understanding drought formation mechanism and developing strategies to adapt to climate change. Nevertheless, existing studies have neglected the influence of nonlinear characteristics of meteorological factors on drought evolution, as well as their complex interactions, inhibiting in-depth understanding drought formation mechanism and accurate forecasting. To this end, the standardized precipitation evapotranspiration index (SPEI) is adopted in this study to characterize meteorological drought, and the ensemble empirical mode decomposition (EEMD) is used to explore the nonlinear trend of meteorological factors. Moreover, considering that the interaction between meteorological factors brings some uncertainty in quantifying their individual contributions, a new framework for quantifying drought dynamics taking into account nonlinear trends in meteorological factors and their interactions is proposed based on numerical experiments under eight climate experiments. The Loess Plateau (LP), where drought occurs frequently and its ecological environment is very fragile, is selected as a case study. Results show that: (1) the LP generally shows a drying trend on annual and seasonal scales except for summer especially for its northwest; (2) the interaction between meteorological factors affects the evolution of drought. Eliminating the interactions, the dominant factor on annual SPEI trend is wind speed, both in spring and winter, while sunshine hours and precipitation dominate summer and autumn SPEI trend, respectively; (3) the positive trend contribution of sunshine hours to SPEI is greater than the negative contribution of temperature in summer, and its decline plays an important role in alleviating the drought on the LP in summer. In general, this study sheds a new insight into quantifying the nonlinear effects of meteorological factors to drought dynamics. Relevant findings will help to further understand the mechanism of drought formation under changing environments and provide scientific and technical support for drought early warning and scientific response.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Allen RG, Pereira LS, Raes D et al (1998) Crop evapotranspiration-guidelines for computing crop water requirements-FAO irrigation and drainage paper 56. Fao Rome 300(9):D05109
Anderegg WRL, Trugman AT, Bowling DR et al (2019) Plant functional traits and climate influence drought intensification and land-atmosphere feedbacks. Proc Natl Acad Sci U S A 116(28):14071–14076. https://doi.org/10.1073/pnas.1904747116
Bai L, Xu J, Chen Z et al (2015) The regional features of temperature variation trends over Xinjiang in China by the ensemble empirical mode decomposition method. Int J Climatol 35(11):3229–3237. https://doi.org/10.1002/joc.4202
Ben-Ari T, Adrian J, Klein T et al (2016) Identifying indicators for extreme wheat and maize yield losses. Agric for Meteorol 220:130–140. https://doi.org/10.1016/j.agrformet.2016.01.009
Bera B, Shit PK, Sengupta N et al (2021) Trends and variability of drought in the extended part of Chhota Nagpur plateau (Singbhum Protocontinent), India applying SPI and SPEI indices. Environ Chall 5:100310. https://doi.org/10.1016/j.envc.2021.100310
Che HZ (2005) Analysis of 40 years of solar radiation data from China, 1961–2000. Geophys Res Lett. https://doi.org/10.1029/2004gl022322
Dai A (2012) Increasing drought under global warming in observations and models. Nat Clim Change 3(1):52–58. https://doi.org/10.1038/nclimate1633
Fang W, Huang S, Huang Q et al (2019) Probabilistic assessment of remote sensing-based terrestrial vegetation vulnerability to drought stress of the loess plateau in China. Remote Sens Environ. https://doi.org/10.1016/j.rse.2019.111290
Feng S, Trnka M, Hayes M et al (2017) Why do different drought indices show distinct future drought risk outcomes in the US great plains? J Clim 30(1):265–278. https://doi.org/10.1175/jcli-d-15-0590.1
Gu L, Chen J, Yin J et al (2020) Projected increases in magnitude and socioeconomic exposure of global droughts in 1 5 and 2 °C warmer climates. Hydrol Earth Syst Sci 24(1):451–472. https://doi.org/10.5194/hess-24-451-2020
Guo W, Huang S, Huang Q et al (2023) Drought trigger thresholds for different levels of vegetation loss in China and their dynamics. Agric for Meteorol 331:109349. https://doi.org/10.1016/j.agrformet.2023.109349
Han Z, Huang S, Huang Q et al (2020) Effects of vegetation restoration on groundwater drought in the Loess Plateau, China. J Hydrol. https://doi.org/10.1016/j.jhydrol.2020.125566
Han Z, Huang S, Huang Q et al (2021) GRACE-based high-resolution propagation threshold from meteorological to groundwater drought. Agric for Meteorol 307:108476. https://doi.org/10.1016/j.agrformet.2021.108476
Huo Z, Dai X, Feng S et al (2013) Effect of climate change on reference evapotranspiration and aridity index in arid region of China. J Hydrol 492:24–34. https://doi.org/10.1016/j.jhydrol.2013.04.011
Ji F, Wu Z, Huang J et al (2014) Evolution of land surface air temperature trend. Nat Clim Chang 4(6):462–466. https://doi.org/10.1038/nclimate2223
Jin J, Wang Q, Li L (2016) Long-term oscillation of drought conditions in the western China: an analysis of PDSI on a decadal scale. J Arid Land 8(6):819–831. https://doi.org/10.1007/s40333-016-0089-5
Kaiser DP (2002) Decreasing trends in sunshine duration over China for 1954–1998: indication of increased haze pollution? Geophys Res Lett. https://doi.org/10.1029/2002gl016057
Lesk C, Rowhani P, Ramankutty N (2016) Influence of extreme weather disasters on global crop production. Nature 529(7584):84–87. https://doi.org/10.1038/nature16467
Li Y, Sun C (2016) Impacts of the superimposed climate trends on droughts over 1961–2013 in Xinjiang. China Theor Appl Climatol 129(3–4):977–994. https://doi.org/10.1007/s00704-016-1822-x
Li Z, Feng Q, Zhang W et al (2012b) Decreasing trend of sunshine hours and related driving forces in Southwestern China. Theoret Appl Climatol 109(1–2):305–321. https://doi.org/10.1007/s00704-012-0583-4
Li C, Wu PT, Li XL et al (2017a) Spatial and temporal evolution of climatic factors and its impacts on potential evapotranspiration in Loess Plateau of Northern Shaanxi, China. Sci Total Environ 589:165–172. https://doi.org/10.1016/j.scitotenv.2017.02.122
Li Y, Yao N, Chau HW (2017b) Influences of removing linear and nonlinear trends from climatic variables on temporal variations of annual reference crop evapotranspiration in Xinjiang, China. Sci Total Environ 592:680–692. https://doi.org/10.1016/j.scitotenv.2017.02.196
Li Y, Li Y (2017) Advances in adaptability of meteorological drought indices in China. J Arid Meteorol 35(05):709–723. https://doi.org/10.11755/j.issn.1006-7639(2017)-05-0709. (in Chinese)
Li W, Yi X, Hou M et al (2012a) Standardized precipitation evapotranspiration index shows drought trends in China. Chinese J Eco-Agri 20(05):643–649. https://doi.org/10.3724/SP.J.1011.2012.00643. (in Chinese)
Li B, Chen Z, Yuan X (2015a) The nonlinear variation of drought and its relation to atmospheric circulation in Shandong Province, East China. PeerJ 3:e1289. https://doi.org/10.7717/peerj.1289
Li JZ, Wang YX, Li SF et al (2015b) A nonstationary standardized precipitation index incorporating climate indices as covariates. J Geophys Res Atmos. https://doi.org/10.1002/2015jd023920
Li T, Xia J, Zhang L et al (2021) An improved complementary relationship for estimating evapotranspiration attributed to climate change and revegetation in the Loess Plateau, China. J Hydrol. https://doi.org/10.1016/j.jhydrol.2020.125516
Li P, Huang Q, Huang S et al (2022a) Various maize yield losses and their dynamics triggered by drought thresholds based on Copula-Bayesian conditional probabilities. Agri Water Manage. https://doi.org/10.1016/j.agwat.2021.107391
Li Y, Huang S, Wang H et al (2022b) High-resolution propagation time from meteorological to agricultural drought at multiple levels and spatiotemporal scales. Agri Water Manage 262:107428. https://doi.org/10.1016/j.jhydrol.2020.125566
Liu Z, Wang Y, Shao M et al (2016) Spatiotemporal analysis of multiscalar drought characteristics across the Loess Plateau of China. J Hydrol 534:281–299. https://doi.org/10.1016/j.jhydrol.2016.01.003
Liu H, Cao L, Jia J et al (2021) Effects of land use changes on the nonlinear trends of net primary productivity in arid and semiarid areas. China Land Degrad Dev 32(6):2183–2196. https://doi.org/10.1002/ldr.3885
Manfreda S, Pizzolla T, Caylor KK (2013) Modelling vegetation patterns in semiarid environments. Procedia Environ Sci 19:168–177. https://doi.org/10.1016/j.proenv.2013.06.019
Mann HB (1945) Nonparametric tests against trend. Econom J Econom Soc 13:245–259
McKee TB, Doesken NJ, Kleist J (1993) The relationship of drought frequency and duration to time scales. In: Proceedings of the 8th conference on applied climatology. Boston, pp 179–183
Mishra AK, Singh VP (2010) A review of drought concepts. J Hydrol 391(1–2):202–216. https://doi.org/10.1016/j.jhydrol.2010.07.012
Nedealcov M, Rileanu V, Srbu R et al (2015) The use of standardized indicators (SPI And SPEI) in predicting droughts over the republic of moldova territory. Present Environ Sustain Dev. https://doi.org/10.1515/PESD-2015-0032
Qin Y, Li B, Chen Z et al (2018) Spatio-temporal variations of nonlinear trends of precipitation over an arid region of northwest China according to the extreme-point symmetric mode decomposition method. Int J Climatol 38(5):2239–2249. https://doi.org/10.1002/joc.5330
Roderick ML, Farquhar GD (2002) The cause of decreased pan evaporation over the past 50 years. Science 298(5597):1410–1411. https://doi.org/10.1126/science.1075390
Sen PK (1968) Estimates of the regression coefficient based on Kendall’s tau. J Am Stat as 63(324):1379–1389
She D, Xia J, Zhang Y (2017) Changes in reference evapotranspiration and its driving factors in the middle reaches of Yellow River Basin, China. Sci Total Environ 607–608:1151–1162. https://doi.org/10.1016/j.scitotenv.2017.07.007
Shi H, Shao M (2000) Soil and water loss from the Loess Plateau in China. J Arid Environ 45(1):9–20. https://doi.org/10.1006/jare.1999.0618
Sun C, Ma Y (2015) Effects of non-linear temperature and precipitation trends on Loess Plateau droughts. Quatern Int 372:175–179. https://doi.org/10.1016/j.quaint.2015.01.051
Sun S, Chen H, Ju W et al (2014) On the attribution of the changing hydrological cycle in Poyang Lake Basin, China. J Hydrol 514:214–225. https://doi.org/10.1016/j.jhydrol.2014.04.013
Sun S, Chen H, Ju W et al (2016) On the coupling between precipitation and potential evapotranspiration: contributions to decadal drought anomalies in the Southwest China. Clim Dyn 48(11–12):3779–3797. https://doi.org/10.1007/s00382-016-3302-5
Sun S, Chen H, Li J et al (2018) Dependence of 3-month standardized precipitation-evapotranspiration index dryness/wetness sensitivity on climatological precipitation over southwest China. Int J Climatol 38(12):4568–4578. https://doi.org/10.1002/joc.5690
Sun S, Li Q, Li J et al (2019) Revisiting the evolution of the 2009–2011 meteorological drought over Southwest China. J Hydrol 568:385–402. https://doi.org/10.1016/j.jhydrol.2018.10.071
Tabari H, Hosseinzadeh Talaee P, Mousavi Nadoushani SS et al (2014) A survey of temperature and precipitation based aridity indices in Iran. Quatern Int 345:158–166. https://doi.org/10.1016/j.quaint.2014.03.061
Tirivarombo S, Osupile D, Eliasson P (2018) Drought monitoring and analysis: standardised precipitation evapotranspiration index (SPEI) and standardised precipitation index (SPI). Phys Chem Earth Parts a/b/c 106:1–10. https://doi.org/10.1016/j.pce.2018.07.001
Tomas-Burguera M, Vicente-Serrano SM, Peña-Angulo D et al (2020) Global characterization of the varying responses of the standardized precipitation evapotranspiration index to atmospheric evaporative demand. J Geophys Res Atmos. https://doi.org/10.1029/2020jd033017
Trenberth KE, Dai A, van der Schrier G et al (2013) Global warming and changes in drought. Nat Clim Chang 4(1):17–22. https://doi.org/10.1038/nclimate2067
Tsanis IK, Vrohidou A-EK, Koutroulis AG (2011) Spatiotemporal characteristics of meteorological drought for the Island of crete. J Hydrometeorol 12(2):206–226. https://doi.org/10.1175/2010jhm1252.1
Uddin MJ, Hu J, Islam ARMT et al (2020) A comprehensive statistical assessment of drought indices to monitor drought status in Bangladesh. Arab J Geosci 13:323. https://doi.org/10.1007/s12517-020-05302-0
Vicente-Serrano SM, Beguería S, López-Moreno JI (2010) A multiscalar drought index sensitive to global warming: the standardized precipitation evapotranspiration index. J Clim 23(7):1696–1718. https://doi.org/10.1175/2009jcli2909.1
Wang Z, Ding Y, He J et al (2004) An updating analysis of the climate change in china in recent 50 years. Acta Meteor Sin 02:228–236 (in Chinese)
Wang J, Fei X, Wei F (2008) Further study of temperature change in Northwest China in recent 50 years. J Desert Res 28(4):724–732. http://www.desert.ac.cn/CN/Y2008/V28/I4/724(in Chinese)
Wang T, Tu X, Singh VP et al (2021) Global data assessment and analysis of drought characteristics based on CMIP6. J Hydrol. https://doi.org/10.1016/j.jhydrol.2021.126091
Willett K, Dunn R, Thorne P et al (2014) HadISDH land surface multi-variable humidity and temperature record for climate monitoring. Clim past 10(6):1983–2006. https://doi.org/10.5194/cp-10-1983-2014
Wu J, Chen X (2019) Spatiotemporal trends of dryness/wetness duration and severity: the respective contribution of precipitation and temperature. Atmos Res 216:176–185. https://doi.org/10.1016/j.atmosres.2018.10.005
Wu Z, Huang NE (2009) Ensemble empirical mode decomposition: a noise-assisted data analysis method. Adv Adapt Data Anal 1(01):1–41
Wu J, Miao C, Tang X et al (2018) A nonparametric standardized runoff index for characterizing hydrological drought on the Loess Plateau, China. Global Planet Change 161:53–65. https://doi.org/10.1016/j.gloplacha.2017.12.006
Wu H, Su X, Singh VP et al (2021) Agricultural drought prediction based on conditional distributions of vine copulas. Water Resour Res 57(8):e2021WR029562. https://doi.org/10.1029/2021WR029562
Xia X (2010) Spatiotemporal changes in sunshine duration and cloud amount as well as their relationship in China during 1954–2005. J Geophys Res. https://doi.org/10.1029/2009jd012879
Xie S, Mo X, Hu S et al (2020) Contributions of climate change, elevated atmospheric CO2 and human activities to ET and GPP trends in the Three-North Region of China. Agri for Meteorol. https://doi.org/10.1016/j.agrformet.2020.108183
Xu C, Gong L, Jiang T et al (2006) Analysis of spatial distribution and temporal trend of reference evapotranspiration and pan evaporation in Changjiang (Yangtze River) catchment. J Hydrol 327(1–2):81–93. https://doi.org/10.1016/j.jhydrol.2005.11.029
Xu K, Yang D, Yang H et al (2015) Spatio-temporal variation of drought in China during 1961–2012: a climatic perspective. J Hydrol 526:253–264. https://doi.org/10.1016/j.jhydrol.2014.09.047
Yan X, Zhang Q, Zhang W et al (2021) Analysis of climate characteristics in the Pan-Central-Asia arid region. Arid Zone Res 38(01):1–11. https://doi.org/10.13866/j.azr.2021. (in Chinese)
Yao J, Liu H, Huang J et al (2020) Accelerated dryland expansion regulates future variability in dryland gross primary production. Nat Commun 11(1):1665. https://doi.org/10.1038/s41467-020-15515-2
Yuan W, Zheng Y, Piao S et al (2019) Increased atmospheric vapor pressure deficit reduces global vegetation growth. Sci Adv. https://doi.org/10.1126/sciadv.aax1396
Yue Y, Shen S-H, Wang Q (2018) Trend and variability in droughts in Northeast China based on the reconnaissance drought index. Water. https://doi.org/10.3390/w10030318
Zhang B, Wu P, Zhao X et al (2012) Drought variation trends in different subregions of the Chinese Loess Plateau over the past four decades. Agric Water Manag 115:167–177. https://doi.org/10.1016/j.agwat.2012.09.004
Zhang J, Sun F, Xu J et al (2016) Dependence of trends in and sensitivity of drought over China (1961–2013) on potential evaporation model. Geophys Res Lett 43(1):206–213. https://doi.org/10.1002/2015gl067473
Zhang Y, Liu C, Tang Y et al (2007) Trends in pan evaporation and reference and actual evapotranspiration across the Tibetan Plateau. J Geophys Res. https://doi.org/10.1029/2006jd008161
Zhang D, Liu X, Bai P (2018) Different influences of vegetation greening on regional water-energy balance under different climatic conditions. Forests. https://doi.org/10.3390/f9070412
Zhang B, Tian L, Zhao X et al (2021) Study on the feedback effect of vegetation restoration on local precipitation on the Loess Plateau. Sci Sin (terrae) 51(7):1080–1091. https://doi.org/10.1360/SSTe-2020-0273. (in Chinese)
Zhao Y, Zou X, Zhang J et al (2014) Spatio-temporal variation of reference evapotranspiration and aridity index in the Loess Plateau Region of China, during 1961–2012. Quatern Int 349:196–206. https://doi.org/10.1016/j.quaint.2014.06.050
Zhao J, Yan D, Yang Z et al (2015) Improvement and adaptability evaluation of standardized precipitation evapotranspiration index. Acta Phys Sin 64(4):382–390. https://doi.org/10.7498/aps.64.049202. (in Chinese)
Zhao R, Wang H, Chen J et al (2021a) Quantitative analysis of nonlinear climate change impact on drought based on the standardized precipitation and evapotranspiration index. Ecol Indic. https://doi.org/10.1016/j.ecolind.2020.107107
Zhao Y, Huang W, Cao M et al (2021b) Potential evapotranspiration and influence factors of vegetation in the Loess Plateau. Res Environ Sci 5:1–16. https://doi.org/10.13198/j.issn.1001-6929.2021.05.40. (in Chinese)
Zheng X, Huang S, Peng J et al (2022) Flash droughts identification based on an improved framework and their contrasting impacts on vegetation over the Loess Plateau, China. Water Resour Res. https://doi.org/10.1029/2021wr031464
Zhou S, Sun S, Shi W et al (2019) Spatiotemporal differences in dominants of dryness/wetness changes in Southwest China. Adv Meteorol 2019:1–16. https://doi.org/10.1155/2019/2820769
Funding
This work was jointly supported by the National Key Research and Development Program of China (grant number 2022YFC3202300), the National Natural Science Foundation of China (grant number 52279026), the Key R&D Program of Xinjiang Uygur Autonomous Region (grant number 2022B03024-4), and the Strategic Priority Research Program of the Chinese Academy of Sciences (grant number XDA28060100).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Guo, W., Huang, S., Zhao, Y. et al. Quantifying the effects of nonlinear trends of meteorological factors on drought dynamics. Nat Hazards 117, 2505–2526 (2023). https://doi.org/10.1007/s11069-023-05954-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11069-023-05954-7