Drought Trend Analysis Based on the Standardized Precipitation–Evapotranspiration Index Using NASA’s Earth Exchange Global Daily Downscaled Projections, High Spatial Resolution Coupled Model Intercomparison Project Phase 5 Projections, and Assessment of Potential Impacts on China’s Crop Yield in the 21st Century
Abstract
:1. Introduction
2. Datasets: CMIP5 Simulations and Forcing Data
3. General Method and Study Area
3.1. The General Method: Standardized Precipitation–Evapotranspiration Index and the Hargreaves–Samani Equation
3.2. Mann–Kendall (MK) and Sen’s Tests
3.3. Climate Regions of the Study Area
4. Simulation Results and Discussion
4.1. Validation of CMIP5 and MME Temperature and Precipitation Simulations
4.2. Projected Change of SPEI_3 on a Decadal Scale
4.3. Change of Seasonal SPEI_3 on a Decadal Scale
4.4. The Change of Drought Frequency and Duration on a Decadal Scale
4.5. Potential Risk for Crop Production Caused by Future Drought
5. Conclusions
- Evaluations of downscaled CMIP5 models over China show that CMIP5 projections from NEX-GDDP could better reproduce the monthly temperature and precipitation, but they have systematic errors, which are highly dependent on the region and climate model.
- In general, CMIP5 is much better in reproducing the monthly minimum temperature than the monthly maximum temperature. The accuracy of temperature is noticeably higher than that of precipitation in CMIP5 models. The ability of MME to reconstruct spatial–temporal information (temperature, precipitation) is better than any single CMIP5 model.
- Taken as a whole, SPEI_3 is projected to significantly decrease across China (−0.15/decade under RCP4.5; −0.14/decade under RCP8.5). SPEI_3 reveals a spatial pattern of drier conditions in high-latitude and high-altitude regions (including NWC, NEC, NC, and the Tibetan Plateau) and wetter conditions in Southern China (including SEC, EC, SC, and SWC), and this pattern will be intensified under RCP8.5.
- Overall, China is projected to get drier in the four seasons. The decreased rate of SPEI_3 is projected to be largest in winter (−0.2/decade and −0.31/decade), then lower in spring (−0.18/decade and −0.27/decade) and autumn (−0.11/decade and −0.14/decade), and the lowest in summer (−0.08/decade and −0.10/decade) under RCP4.5 and RCP8.5, respectively.
- From the zonal perspective, winter and spring will get drier in high-latitude/-altitude areas, and summer and autumn will get wetter (especially for Southern China).
- For moderate and severe droughts, drought frequency and duration are projected to decrease, while both drought frequency and duration will increase for extreme drought. Furthermore, the values of frequency and duration for extreme drought are higher in high-latitude and elevated regions than those in southern regions.
- There is a significant increase in the percentage of potential crop production affected by drought over time, where the values will increase up to 36% (RCP4.5) and 47% (RCP8.5) at the end of this century. The ratio caused by extreme and severe drought areas are projected to show an increasing trend with larger magnitudes under RCP8.5.
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
- Allen, M.R.; Ingram, W.J. Constraints on future changes in climate and the hydrologic cycle. Nature 2002, 419, 224–232. [Google Scholar] [CrossRef] [PubMed]
- Ziegler, A.D.; Sheffield, J.; Maurer, E.P.; Nijssen, B.; Wood, E.F.; Lettenmaier, D.P. Detection of Intensification in Global- and Continental-Scale Hydrological Cycles: Temporal Scale of Evaluation. J. Clim. 2003, 16, 535–547. [Google Scholar] [CrossRef]
- Easterling, D.R.; Evans, J.L.; Groisman, P.Y.; Karl, T.R.; Kunkel, K.E.; Ambenje, P. Observed Variability and Trends in Extreme Climate Events: A Brief Review*. Bull. Am. Meteorol. Soc. 2000, 81, 417–425. [Google Scholar] [CrossRef]
- Huang, Q.; Zhang, Q.; Singh, V.P.; Shi, P.; Zheng, Y. Variations of dryness/wetness across China: Changing properties, drought risks, and causes. Glob. Planet. Chang. 2017, 155, 1–12. [Google Scholar] [CrossRef]
- Qin, Z.; Tang, H.; Li, W.; Zhang, H.; Zhao, S.; Wang, Q. Modelling impact of agro-drought on grain production in China. Int. J. Disaster Risk Reduct. 2014, 7, 109–121. [Google Scholar] [CrossRef]
- Zhao, H.; Gao, G.; An, W.; Zou, X.; Li, H.; Hou, M. Timescale differences between SC-PDSI and SPEI for drought monitoring in China. Phys. Chem. Earth Parts A B C 2017, 102, 48–58. [Google Scholar] [CrossRef]
- Li, W.G.; Yi, X.; Hou, M.T.; Chen, H.L.; Chen, Z.L. Standardized precipitation evapotranspiration index shows drought trends in China. Chin. J. Eco-Agric. 2012, 20, 643–649. [Google Scholar] [CrossRef]
- Palmer, W.C. Meteorological Drought; Research Paper No. 45; US Weather Bureau: Washington, DC, USA, 1965.
- McKee, T.B.; Doesken, N.J.; Kleist, J. The relationship of drought frequency and duration to time scales. In Proceedings of the 8th Conference on Applied Climatology, Boston, MA, USA, 17–22 January 1993. [Google Scholar]
- Chen, Z.; Lei, H.; Yang, H.; Yang, D.; Cao, Y. Historical and future trends in wetting and drying in 291 catchments across China. Hydrol. Earth Syst. Sci. 2017, 21, 1–24. [Google Scholar] [CrossRef]
- Faustin Katchele, O.; Ma, Z.-G.; Yang, Q.; Batebana, K. Comparison of trends and frequencies of drought in central North China and sub-Saharan Africa from 1901 to 2010. Atmos. Ocean. Sci. Lett. 2017, 10, 418–426. [Google Scholar] [CrossRef]
- Liu, X.; Wang, S.; Zhou, Y.; Wang, F.; Li, W.; Liu, W. Regionalization and spatiotemporal variation of drought in China based on standardized precipitation evapotranspiration index (1961–2013). Adv. Meteorol. 2015, 2015, 950262. [Google Scholar] [CrossRef]
- Shen, G.; Zheng, H.; Lei, Z. Applicability analysis of SPEI for drought research in Northeast China. Acta Ecol. Sin. 2017, 37, 1–9. [Google Scholar]
- Liu, K.; Jiang, D. Projected Changes in the Dry/Wet Climate of China under the RCP4.5 Scenario. Chin. J. Atmos. Sci. 2015, 39, 489–502. [Google Scholar]
- Cook, B.I.; Smerdon, J.E.; Seager, R.; Coats, S. Global warming and 21 st century drying. Clim. Dyn. 2014, 43, 2607–2627. [Google Scholar] [CrossRef]
- Tong, S.; Lai, Q.; Zhang, J.; Bao, Y.; Lusi, A.; Ma, Q.; Li, X.; Zhang, F. Spatiotemporal drought variability on the Mongolian Plateau from 1980–2014 based on the SPEI-PM, intensity analysis and Hurst exponent. Sci. Total Environ. 2018, 615, 1557–1565. [Google Scholar] [CrossRef]
- Yao, J.; Zhao, Y.; Yu, X. Spatial-temporal variation and impacts of drought in Xinjiang (Northwest China) during 1961–2015. PeerJ 2018, 6, e4926. [Google Scholar] [CrossRef]
- Yu, M.; Li, Q.; Hayes, M.J.; Svoboda, M.D.; Heim, R.R. Are droughts becoming more frequent or severe in China based on the standardized precipitation evapotranspiration index: 1951–2010? Int. J. Climatol. 2014, 34, 545–558. [Google Scholar] [CrossRef]
- Zhao, S.; Cong, D.; He, K.; Yang, H.; Qin, Z. Spatial-Temporal Variation of Drought in China from 1982 to 2010 Based on a modified Temperature Vegetation Drought Index (mTVDI). Sci. Rep. 2017, 7, 17473. [Google Scholar] [CrossRef]
- Wang, L.; Chen, W.; Zhou, W. Assessment of future drought in Southwest China based on CMIP5 multimodel projections. Adv. Atmos. Sci. 2014, 31, 1035–1050. [Google Scholar] [CrossRef]
- Chen, H.; Sun, J. Characterizing present and future drought changes over eastern China. Int. J. Climatol. 2017, 37, 138–156. [Google Scholar] [CrossRef]
- Thibeault, J.M.; Seth, A. Changing climate extremes in the Northeast United States: observations and projections from CMIP5. Clim. Chang. 2014, 127, 273–287. [Google Scholar] [CrossRef]
- Gao, X.; Zhao, Q.; Zhao, X.; Wu, P.; Pan, W.; Gao, X.; Sun, M. Temporal and spatial evolution of the standardized precipitation evapotranspiration index (SPEI) in the Loess Plateau under climate change from 2001 to 2050. Sci. Total Environ. 2017, 595, 191–200. [Google Scholar] [CrossRef] [PubMed]
- Sheffield, J.; Wood, E.F. Projected changes in drought occurrence under future global warming from multi-model, multi-scenario, IPCC AR4 simulations. Clim. Dyn. 2008, 31, 79–105. [Google Scholar] [CrossRef]
- Jiang, D.; Tian, Z.; Lang, X. Reliability of climate models for China through the IPCC Third to Fifth Assessment Reports. Int. J. Climatol. 2016, 36, 1114–1133. [Google Scholar] [CrossRef]
- Sheffield, J.; Xu, E.; Kam, J.; Chaney, N.; Wood, E. Uncertainties in Regional to Global Variability in Drought and Implications for Future Projections. In Proceedings of the EGU General Assembly Conference Abstracts, Vienna, Austria, 7–12 April 2013; Volume 15. [Google Scholar]
- Dai, A. Increasing drought under global warming in observations and models. Nat. Clim. Chang. 2013, 3, 52–58. [Google Scholar] [CrossRef]
- Huo-Po, C.; Jian-Qi, S. Drought Response to Air Temperature Change over China on the Centennial Scale. Atmos. Ocean. Sci. Lett. 2015, 8, 113–119. [Google Scholar] [CrossRef]
- Jiang, J.; Jiang, D.; Lin, Y. Changes and Projection of Dry/Wet Areas over China. Chin. J. Atmos. Sci. 2017, 41, 43–56. [Google Scholar]
- Mo, X.; Hu, S.; Lu, H.; Lin, Z.; Liu, S. Drought Trends over the Terrestrial China in the 21st Century in Climate Change Scenarios with Ensemble GCM Projections. J. Nat. Resour. 2018, 33, 1244–1256. [Google Scholar] [CrossRef]
- Sheffield, J.; Livneh, B.; Wood, E.F. Representation of Terrestrial Hydrology and Large-Scale Drought of the Continental United States from the North American Regional Reanalysis. J. Hydrometeorol. 2012, 13, 856–876. [Google Scholar] [CrossRef]
- Xu, C.; Luo, Y.; Xu, Y. Simulation and prediction of the drought variations in China by multi-model ensemble. J. Glaciol. Geocryol. 2010, 32, 867–874. [Google Scholar]
- Trenberth, K.E.; Dai, A.; der Schrier, G.V.; Jones, P.D.; Barichivich, J.; Briffa, K.R.; Sheffield, J. Global warming and changes in drought. Nat. Clim. Chang. 2014, 4, 17–22. [Google Scholar] [CrossRef]
- Dai, A. Drought under global warming: A review. Wiley Interdiscip. Rev. Clim. Chang. 2011, 2, 45–65. [Google Scholar] [CrossRef]
- Mo, K.C. Model-Based Drought Indices over the United States. J. Hydrometeorol. 2008, 9, 1212–1230. [Google Scholar] [CrossRef]
- Qin, Y.; Yan, H.; Liu, J.; Dong, J.; Chen, J.; Xiao, X. Impacts of ecological restoration projects on agricultural yield in China. J. Geogr. Sci. 2013, 23, 404–416. [Google Scholar] [CrossRef]
- Wood, A.W.; Maurer, E.P.; Kumar, A.; Lettenmaier, D.P. Long-range experimental hydrologic forecasting for the eastern United States. J. Geophys. Res. Atmos. 2002, 107, ACL 6–1–ACL 6–15. [Google Scholar] [CrossRef]
- Maurer, E.P.; Hidalgo, H.G. Utility of daily vs. monthly large-scale climate data: an intercomparison of two statistical downscaling methods. Hydrol. Earth Syst. Sci. 2008, 12, 551–563. [Google Scholar] [CrossRef] [Green Version]
- Wood, A.W.; Leung, L.R.; Sridhar, V.; Lettenmaier, D.P. Hydrologic Implications of Dynamical and Statistical Approaches to Downscaling Climate Model Outputs. Clim. Chang. 2004, 62, 189–216. [Google Scholar] [CrossRef]
- Sheffield, J.; Goteti, G.; Wood, E.F. Development of a 50-Year High-Resolution Global Dataset of Meteorological Forcings for Land Surface Modeling. J. Clim. 2006, 19, 3088–3111. [Google Scholar] [CrossRef] [Green Version]
- Bao, Y.; Wen, X. Projection of China’s near- and long-term climate in a new high-resolution daily downscaled dataset NEX-GDDP. J. Meteorol. Res. 2017, 31, 236–249. [Google Scholar] [CrossRef]
- Chen, H.-P.; Sun, J.-Q.; Li, H.-X. Future changes in precipitation extremes over China using the NEX-GDDP high-resolution daily downscaled data-set. Atmos. Ocean. Sci. Lett. 2017, 10, 403–410. [Google Scholar] [CrossRef] [Green Version]
- Kim, H.G.; Lee, D.K.; Park, C.; Kil, S.; Son, Y.; Park, J.H. Evaluating landslide hazards using RCP 4.5 and 8.5 scenarios. Environ. Earth Sci. 2015, 73, 1385–1400. [Google Scholar] [CrossRef]
- Yan, H.; Liu, J.; Cao, M. Remotely sensed multiple cropping index variations in China during 1981–2000. Acta Geogr. Sin. 2005, 60, 559. [Google Scholar]
- Chen, X.; Su, Z.; Ma, Y.; Liu, S.; Yu, Q.; Xu, Z. Development of a 10 year (2001–2010) 0.1° dataset of land-surface energy balance for mainland China. Atmos. Chem. Phys. 2014, 14, 13097–13117. [Google Scholar] [CrossRef] [Green Version]
- Chen, Y.; Yang, K.; Jie, H.; Qin, J.; Shi, J.; Du, J.; He, Q. Improving land surface temperature modeling for dry land of China. J. Geophys. Res. Atmos. 2011, 116. [Google Scholar] [CrossRef]
- Yang, K.; Jie, H.; Tang, W.; Qin, J.; Cheng, C.C.K. On downward shortwave and longwave radiations over high altitude regions: Observation and modeling in the Tibetan Plateau. Agric. For. Meteorol. 2010, 150, 38–46. [Google Scholar] [CrossRef]
- He, J.; Yang, K. China Meteorological Forcing Dataset. Cold Arid Reg. Sci. Data Cent. Lanzhou 2011. [Google Scholar] [CrossRef]
- Vicente-Serrano, S.M.; Beguería, S.; López-Moreno, J.I. A multiscalar drought index sensitive to global warming: the standardized precipitation evapotranspiration index. J. Clim. 2010, 23, 1696–1718. [Google Scholar] [CrossRef] [Green Version]
- Mishra, A.K.; Singh, V.P. A review of drought concepts. J. Hydrol. 2010, 391, 202–216. [Google Scholar] [CrossRef]
- Středová, H.; Středa, T.; Chuchma, F. Climatic factors of soil estimated system. In Bioclimate: Source and Limit of Social Development; SPU v Nitre: Nitra, Slovakia, 2011; pp. 137–138. ISBN 978-80-552-0640. [Google Scholar]
- Vicente-Serrano, S.M.; Van der Schrier, G.; Beguería, S.; Azorine-Molina, C.; Lopeze-Moreno, J.I. Contribution of precipitation and reference evapotranspiration to drought indices under different climates. J. Hydrol. 2015, 526, 42–54. [Google Scholar] [CrossRef] [Green Version]
- Bo, M.; Guo, Y.Q.; Tao, H.B.; Liu, G.Z.; Li, S.K.; Pu, W.A. SPEIPM-based research on drought impact on maize yield in North China Plain. J. Integr. Agric. 2015, 14, 660–669. [Google Scholar]
- Beguería, S.; Vicente-Serrano, S.M.; Reig, F.; Latorre, B. Standardized precipitation evapotranspiration index (SPEI) revisited: parameter fitting, evapotranspiration models, tools, datasets and drought monitoring. Int. J. Climatol. 2014, 34, 3001–3023. [Google Scholar] [CrossRef] [Green Version]
- Rhee, J.; Cho, J. Future Changes in Drought Characteristics: Regional Analysis for South Korea under CMIP5 Projections. J. Hydrometeorol. 2016, 17, 437–451. [Google Scholar] [CrossRef]
- Stagge, J.H.; Tallaksen, L.M.; Gudmundsson, L.; van Loon, A.F.; Stahl, K. Candidate distributions for climatological drought indices (SPI and SPEI). Int. J. Climatol. 2015, 35, 4027–4040. [Google Scholar] [CrossRef]
- Wang, W.; Zhu, Y.; Xu, R.; Liu, J. Drought severity change in China during 1961–2012 indicated by SPI and SPEI. Nat. Hazards 2015, 75, 2437–2451. [Google Scholar] [CrossRef]
- Almorox, J.; Quej, V.H.; Martí, P. Global performance ranking of temperature-based approaches for evapotranspiration estimation considering Köppen climate classes. J. Hydrol. 2015, 528, 514–522. [Google Scholar] [CrossRef]
- Hargreaves, G.H. Accuracy of estimated reference crop evapotranspiration. J. Irrig. Drain. Eng. 1989, 115, 1000–1007. [Google Scholar] [CrossRef]
- Hargreaves, G.H.; Allen, R.G. History and evaluation of Hargreaves evapotranspiration equation. J. Irrig. Drain. Eng. 2003, 129, 53–63. [Google Scholar] [CrossRef]
- Hargreaves, G.H.; Samani, Z.A. Reference Crop Evapotranspiration from Temperature. Appl. Eng. Agric. 1985, 1, 96–99. [Google Scholar] [CrossRef]
- Hess, A.; Iyer, H.; Malm, W. Linear trend analysis: a comparison of methods. Atmos. Environ. 2001, 35, 5211–5222. [Google Scholar] [CrossRef]
- Droogers, P.; Allen, R.G. Estimating Reference Evapotranspiration under Inaccurate Data Conditions. Irrig. Drain. Syst. 2002, 16, 33–45. [Google Scholar] [CrossRef]
- Piao, S.; Ciais, P.; Huang, Y.; Shen, Z.; Peng, S.; Li, J.; Zhou, L.; Liu, H.; Ma, Y.; Ding, Y.; et al. The impacts of climate change on water resources and agriculture in China. Nature 2010, 467, 43–51. [Google Scholar] [CrossRef]
- Jia, W.; Bo-Tao, Z.; Ying, X. Response of precipitation and its extremes over China to warming: CMIP5 simulation and projection. Chin. J. Geophys. 2015, 58, 461–473. [Google Scholar] [CrossRef]
- Douglas, E.M.; Vogel, R.M.; Kroll, C.N. Trends in floods and low flows in the United States: impact of spatial correlation. J. Hydrol. 2000, 240, 90–105. [Google Scholar] [CrossRef]
- Tabari, H.; Marofi, S. Changes of Pan Evaporation in the West of Iran. Water Resour. Manag. 2011, 25, 97–111. [Google Scholar] [CrossRef]
- Tabari, H.; Marofi, S.; Aeini, A.; Talaee, P.H.; Mohammadi, K. Trend analysis of reference evapotranspiration in the western half of Iran. Agric. For. Meteorol. 2011, 151, 128–136. [Google Scholar] [CrossRef]
- Wu, C.; Huang, G.; Yu, H. Prediction of extreme floods based on CMIP5 climate models: a case study in the Beijiang River basin, South China. Hydrol. Earth Syst. Sci. 2015, 19, 1385–1399. [Google Scholar] [CrossRef] [Green Version]
- You, Q.; Min, J.; Kang, S. Rapid warming in the Tibetan Plateau from observations and CMIP5 models in recent decades. Int. J. Climatol. 2016, 36, 2660–2670. [Google Scholar] [CrossRef]
- Sen, P.K. Estimates of the regression coefficient based on Kendall’s tau. J. Am. Stat. Assoc. 1968, 63, 1379–1389. [Google Scholar] [CrossRef]
- Gocic, M.; Trajkovic, S. Analysis of changes in meteorological variables using Mann-Kendall and Sen’s slope estimator statistical tests in Serbia. Glob. Planet. Chang. 2013, 100, 172–182. [Google Scholar] [CrossRef]
- Mann, H.B. Nonparametric tests against trend. Econom. J. Econom. Soc. 1945, 13, 245–259. [Google Scholar] [CrossRef]
Institution | Model | Spatial Resolution (Lon×Lat, Degree) | Country |
---|---|---|---|
Centre National de Recherches Météorologiques/Centre Européen de Recherche et Formation Avancée en Calcul Scientifique (CNRM–CERFACS) | CNRM-CM5 | 1.40° × 1.40 | French |
Atmosphere and Ocean Research Institute (The University of Tokyo), National Institute for Environmental Studies, and Japan Agency for Marine-Earth Science and Technology (MIROC) | MIROC5 | 1.40° × 1.40° | Japan |
National Science Foundation, Department of Energy, National Center for Atmospheric Research | CESM-BGC | 0.94° × 1.25° | USA |
Commonwealth Scientific and Industrial Research Organization (CSIRO) and Bureau of Meteorology (BOM), Australia | ACCESS1.0 | 1.25° × 1.875° | Australia |
National Center For Atmospheric Research (NCAR) | CCSM4 | 0.94° × 1.25° | USA |
Commonwealth Scientific and Industrial Research Organization in collaboration with the Queensland Climate Change Centre of Excellence(CSIRO-QCCCE), Australia | CSIRO-Mk3.6.0 | 1.875° × 1.875° | Australia |
Institute for Numerical Mathematics(INM), Russia | INM-CM4 | 2.0° × 1.5° | Russia |
Institute Pierre-Simon Laplace (IPSL), France | IPSL-CM5A-MR | 1.267° × 3.750° | France |
Max Planck Institute for Meteorology(MPI-M),Germany | MPI-ESM-MR | 1.875 × 1.875 | Germany |
Meteorological Research Institute(MRI), Japan | MRI-CGCM3 | 1.125 × 1.125 | Japan |
Drought Classification | SPEI Value (Probability) |
---|---|
Extreme humid | SPEI ≥ 2.0 (2.3%) |
Severe humid | 1.5 ≤ SPEI < 2.0(4.4%) |
Moderate humid | 1.0 ≤ SPEI < 1.5(9.2%) |
Normal | −1.0 < SPEI < 1.0(68.2%) |
Moderate dry | −1.5 < SPEI ≤ −1.0(9.2%) |
Severe dry | −2.0 < SPEI ≤ −1.5(4.4%) |
Extreme dry | SPEI ≤ −2.0(2.3%) |
RCP4.5 (SPEI/Decade) | RCP8.5 (SPEI/Decade) | |
---|---|---|
Northwest | * −0.285 | * −0.348 |
North | * −0.161 | * −0.2299 |
Northeast | * −0.068 | * −0.1048 |
Tibetan Plateau | * −0.107 | * −0.124 |
Southwest | * −0.037 | −0.0005 |
South | −0.001 | 0.0035 |
Central | * −0.017 | * −0.0367 |
East | −0.003 | −0.014 |
National | −0.150 | −0.140 |
© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Guo, X.; Yang, Y.; Li, Z.; You, L.; Zeng, C.; Cao, J.; Hong, Y. Drought Trend Analysis Based on the Standardized Precipitation–Evapotranspiration Index Using NASA’s Earth Exchange Global Daily Downscaled Projections, High Spatial Resolution Coupled Model Intercomparison Project Phase 5 Projections, and Assessment of Potential Impacts on China’s Crop Yield in the 21st Century. Water 2019, 11, 2455. https://doi.org/10.3390/w11122455
Guo X, Yang Y, Li Z, You L, Zeng C, Cao J, Hong Y. Drought Trend Analysis Based on the Standardized Precipitation–Evapotranspiration Index Using NASA’s Earth Exchange Global Daily Downscaled Projections, High Spatial Resolution Coupled Model Intercomparison Project Phase 5 Projections, and Assessment of Potential Impacts on China’s Crop Yield in the 21st Century. Water. 2019; 11(12):2455. https://doi.org/10.3390/w11122455
Chicago/Turabian StyleGuo, Xiaolin, Yuan Yang, Zhansheng Li, Liangzhi You, Chao Zeng, Jing Cao, and Yang Hong. 2019. "Drought Trend Analysis Based on the Standardized Precipitation–Evapotranspiration Index Using NASA’s Earth Exchange Global Daily Downscaled Projections, High Spatial Resolution Coupled Model Intercomparison Project Phase 5 Projections, and Assessment of Potential Impacts on China’s Crop Yield in the 21st Century" Water 11, no. 12: 2455. https://doi.org/10.3390/w11122455