[1] KING-OKUMU C, TSEGAI D, PANDEY R P, et al. Less to Lose? Drought impact and vulnerability assessment in disadvantaged regions[J]. Water, 2020, 12(4): 1136.
[2] SHI H Y, CHEN J, WANG K Y, et al. A new method and a new index for identifying socioeconomic drought events under climate change: A case study of the East river basin in China[J]. Science of the Total Environment, 2018, 616: 363-375.
[3] YAO N, LI Y, LEI T J, et al. Drought evolution, severity and trends in mainland China over 1961-2013[J]. Science of the Total Environment, 2018, 616: 73-89.
[4] ZHOU Z Q, SHI H Y, FU Q, et al. Is the cold region in Northeast China still getting warmer under climate change impact?[J]. Atmospheric Research, 2020, 237: 104864.
[5] SU B D, HUANG J L, FISCHER T, et al. Drought losses in China might double between the 1.5 ℃ and 2.0 ℃ warming[J]. Proceedings of the National Academy of Sciences of the United States of America, 2018, 115(42): 10600-10605.
[6] SOCIETY A M. Drought - An Information Statement of the American Meteorological Society[Z]. 2013
[7] EKLUND L, SEAQUIST J. Meteorological, agricultural and socioeconomic drought in the Duhok Governorate, Iraqi Kurdistan[J]. Natural Hazards, 2015, 76(1): 421-441.
[8] HUANG S Z, HUANG Q, LENG G Y, et al. A nonparametric multivariate standardized drought index for characterizing socioeconomic drought: A case study in the Heihe River Basin[J]. Journal of Hydrology, 2016, 542: 875-883.
[9] MEHRAN A, MAZDIYASNI O, AGHAKOUCHAK A. A hybrid framework for assessing socioeconomic drought: Linking climate variability, local resilience, and demand[J]. Journal of Geophysical Research-Atmospheres, 2015, 120(15): 7520-7533.
[10] ZHOU Z Q, SHI H Y, FU Q, et al. Characteristics of propagation from meteorological drought to hydrological drought in the Pearl river basin[J]. Journal of Geophysical Research-Atmospheres, 2021, 126(4): e2020JD033959.
[11] ZHANG L K, QIN X Q, LIU P Y, et al. Estimation of carbon sink fluxes in the Pearl River basin (China) based on a water-rock-gas-organism interaction model[J]. Environmental Earth Sciences, 2015, 74(2): 945-952.
[12] ZHOU Y L, ZHOU P. Decline in net primary productivity caused by severe droughts: evidence from the Pearl River basin in China[J]. Hydrology Research, 2021, 52(6): 1559-1576.
[13] DENG S L, CHEN T, YANG N, et al. Spatial and temporal distribution of rainfall and drought characteristics across the Pearl River basin[J]. Science of the Total Environment, 2018, 619: 28-41.
[14] SHI H Y, ZHOU Z Q, LIU L, et al. A global perspective on propagation from meteorological drought to hydrological drought during 1902-2014[J]. Atmospheric Research, 2022, 280: 106441.
[15] DING Y B, HE X F, ZHOU Z Q, et al. Response of vegetation to drought and yield monitoring based on NDVI and SIF[J]. Catena, 2022, 219: 106328.
[16] WANG Y D, LIU X L, REN G X, et al. Analysis of the spatiotemporal variability of droughts and the effects of drought on potato production in northern China[J]. Agricultural and Forest Meteorology, 2019, 264: 334-342.
[17] HEIM R R. A review of twentieth-century drought indices used in the United States[J]. Bulletin of the American Meteorological Society, 2002, 83(8): 1149-1165.
[18] FAO. Soil Resources, Development and Conservation Service. Guidelines: land evaluation for rainfed agriculture[M]. 1983.
[19] PALMER W C. Meteorological drought. US Department of Commerce, Weather Bureau Wshington, DC[M]. 1965.
[20] 赵聚宝,李克煌. 干旱与农业 [M]. 北京 : 中国农业出版社 , 1995.
[21] PANU U S, SHARMA T C. Challenges in drought research: some perspectives and future directions[J]. Hydrological Sciences Journal-Journal Des Sciences Hydrologiques, 2002, 47: S19-S30.
[22] KEYANTASH J, DRACUP J A. The quantification of drought: An evaluation of drought indices[J]. Bulletin of the American Meteorological Society, 2002, 83(8): 1167-1180.
[23] 冯平. 干旱灾害的识别途径[J]. 自然灾害学报 , 1997, 6(3): 40-43.
[24] MISHRA A K, SINGH V P. A review of drought concepts[J]. Journal of Hydrology, 2010, 391(1-2): 204-216.
[25] 张巧凤,刘桂香,于红博,玉山,包海. 锡林郭勒草原土壤含水量遥感反演模型及干旱监测[J]. 草业学报 , 2017, 26(11): 1-11.
[26] SHI H Y, CHEN J, LI T J, et al. A new method for estimation of spatially distributed rainfall through merging satellite observations, raingauge records, and terrain digital elevation model data[J]. Journal of Hydro-Environment Research, 2020, 28: 1-14.
[27] FU Q, ZHOU Z Q, LI T X, et al. Spatiotemporal characteristics of droughts and floods in northeastern China and their impacts on agriculture[J]. Stochastic Environmental Research and Risk Assessment, 2018, 32(10): 2913-2931.
[28] ZHOU Z Q, SHI H Y, FU Q, et al. Assessing spatiotemporal characteristics of drought and its effects on climate-induced yield of maize in Northeast China[J]. Journal of Hydrology, 2020, 588: 125097.
[29] DING Y B, XU J T, WANG X W, et al. Spatial and temporal effects of drought on Chinese vegetation under different coverage levels[J]. Science of the Total Environment, 2020, 716: 137166.
[30] DING Y B, GONG X L, XING Z X, et al. Attribution of meteorological, hydrological and agricultural drought propagation in different climatic regions of China[J]. Agricultural Water Management, 2021, 255: 106996.
[31] VICENTE-SERRANO S M, BEGUERIA S, LOPEZ-MORENO J I. A multiscalar drought index sensitive to global warming: The standardized precipitation evapotranspiration index[J]. Journal of Climate, 2010, 23(7): 1696-1718.
[32] ZHOU K K, LI J Z, ZHANG T, et al. The use of combined soil moisture data to characterize agricultural drought conditions and the relationship among different drought types in China[J]. Agricultural Water Management, 2021, 243: 106479.
[33] SHIN J Y, KWON H H, LEE J H, et al. Probabilistic long-term hydrological drought forecast using Bayesian networks and drought propagation[J]. Meteorological Applications, 2020, 27(1): 10.1002/met.1827.
[34] GUO Y, HUANG S Z, HUANG Q, et al. Assessing socioeconomic drought based on an improved Multivariate Standardized Reliability and Resilience Index[J]. Journal of Hydrology, 2019, 568: 904-918.
[35] HAN L Y, ZHANG Q, MA P L, et al. The spatial distribution characteristics of a comprehensive drought risk index in southwestern China and underlying causes[J]. Theoretical and Applied Climatology, 2016, 124(3-4): 517-528.
[36] HUANG S Z, LI P, HUANG Q, et al. The propagation from meteorological to hydrological drought and its potential influence factors[J]. Journal of Hydrology, 2017, 547: 184-195.
[37] DING Y B, XU J T, WANG X W, et al. Propagation of meteorological to hydrological drought for different climate regions in China[J]. Journal of Environmental Management, 2021, 283: 111980.
[38] 吴杰峰,陈兴伟,高路. 水文干旱对气象的响应及其临界条件[J]. 灾害学 , 2017, 32(1): 199-204.
[39] BHARDWAJ K, SHAH D, AADHAR S, et al. Propagation of meteorological to hydrological droughts in India[J]. Journal of Geophysical Research-Atmospheres, 2020, 125(22): e2020JD033455.
[40] BAE H, JI H, LIM Y J, et al. Characteristics of drought propagation in South Korea: relationship between meteorological, agricultural, and hydrological droughts[J]. Natural Hazards, 2019, 99(1): 1-16.
[41] HUANG S Z, HUANG Q, CHANG J X, et al. The response of agricultural drought to meteorological drought and the influencing factors: A case study in the Wei River Basin, China[J]. Agricultural Water Management, 2015, 159: 45-54.
[42] 罗纲阮,陈财,高超,李鹏,马松根,李贺丽,王欢. 农业干旱与气象关联性 —— 以淮河蚌埠闸上地区为例[J]. 自然资源学报 , 2020, 35(4): 977-991.
[43] LENG G Y, TANG Q H, RAYBURG S. Climate change impacts on meteorological, agricultural and hydrological droughts in China[J]. Global and Planetary Change, 2015, 126: 23-34.
[44] LI R H, CHEN N C, ZHANG X, et al. Quantitative analysis of agricultural drought propagation process in the Yangtze river basin by using cross wavelet analysis and spatial autocorrelation[J]. Agricultural and Forest Meteorology, 2020, 280: 107809.
[45] TU X J, WU H O, SINGH V P, et al. Multivariate design of socioeconomic drought and impact of water reservoirs[J]. Journal of Hydrology, 2018, 566: 192-204.
[46] HAN Z M, HUANG S Z, HUANG Q, et al. Propagation dynamics from meteorological to groundwater drought and their possible influence factors[J]. Journal of Hydrology, 2019, 578: 124102.
[47] ZHAO L, LYU A F, WU J J, et al. Impact of meteorological drought on streamflow drought in Jinghe river basin of China[J]. Chinese Geographical Science, 2014, 24(6): 694-705.
[48] WU J F, YAO H X, CHEN X H, et al. A framework for assessing compound drought events from a drought propagation perspective[J]. Journal of Hydrology, 2022, 604: 127228.
[49] HO S, TIAN L, DISSE M, et al. A new approach to quantify propagation time from meteorological to hydrological drought[J]. Journal of Hydrology, 2021, 603: 127056.
[50] EDOSSA D C, BABEL M S, DAS GUPTA A. Drought Analysis in the Awash River Basin, Ethiopia[J]. Water Resources Management, 2010, 24(7): 1441-1460.
[51] WU J F, CHEN X W, YAO H X, et al. Non-linear relationship of hydrological drought responding to meteorological drought and impact of a large reservoir[J]. Journal of Hydrology, 2017, 551: 495-507.
[52] WU J F, CHEN X H, YAO H X, et al. Multi-timescale assessment of propagation thresholds from meteorological to hydrological drought[J]. Science of the Total Environment, 2021, 765: 144232.
[53] ALTIN T B, ALTIN B N. Response of hydrological drought to meteorological drought in the eastern Mediterranean Basin of Turkey[J]. Journal of Arid Land, 2021, 13(5): 470-486.
[54] RAHMOUNI A, MEDDI M, SAAED A H. Hydrological drought response to meteorological drought propagation and basin Characteristics (Case Study: Northwest of Algeria)[J]. Russian Meteorology and Hydrology, 2022, 47(9): 708-717.
[55] LI Q F, HE P F, HE Y C, et al. Investigation to the relation between meteorological drought and hydrological drought in the upper Shaying river basin using wavelet analysis[J]. Atmospheric Research, 2020, 234: 104743.
[56] WU J F, CHEN X H, YAO H X, et al. Hydrological drought instantaneous propagation speed based on the variable motion relationship of speed-time process[J]. Water Resources Research, 2018, 54(11): 9549-9565.
[57] BEVACQUA A G, CHAFFE P L B, CHAGAS V B P, et al. Spatial and temporal patterns of propagation from meteorological to hydrological droughts in Brazil[J]. Journal of Hydrology, 2021, 603:126902.
[58] WANG T, TU X J, SINGH V P, et al. Global data assessment and analysis of drought characteristics based on CMIP6[J]. Journal of Hydrology, 2021, 596: 126091.
[59] HAO Z C, SINGH V P, XIA Y L. Seasonal drought prediction: Advances, challenges, and future prospects[J]. Reviews of Geophysics, 2018, 56(1): 108-141.
[60] XU H J, WANG X P, ZHAO C Y, et al. Assessing the response of vegetation photosynthesis to meteorological drought across northern China[J]. Land Degradation & Development, 2021, 32(1): 20-34.
[61] LESK C, ROWHANI P, RAMANKUTTY N. Influence of extreme weather disasters on global crop production[J]. Nature, 2016, 529(7584): 84-+.
[62] ZHOU Z Q, DING Y B, SHI H Y, et al. Analysis and prediction of vegetation dynamic changes in China: Past, present and future[J]. Ecological Indicators, 2020, 117: 106642.
[63] SHAMMI S A, MENG Q M. Use time series NDVI and EVI to develop dynamic crop growth metrics for yield modeling[J]. Ecological Indicators, 2021, 121: 107124.
[64] ZHANG Y L, GAO J G, LIU L S, et al. NDVI-based vegetation changes and their responses to climate change from 1982 to 2011: A case study in the Koshi river basin in the middle Himalayas[J]. Global and Planetary Change, 2013, 108: 139-148.
[65] VICENTE-SERRANO S M, GOUVEIA C, CAMARERO J J, et al. Response of vegetation to drought time-scales across global land biomes[J]. Proceedings of the National Academy of Sciences of the United States of America, 2013, 110(1): 52-57.
[66] ZHONG S B, SUN Z H, DI L P. Characteristics of vegetation response to drought in the CONUS based on long-term remote sensing and meteorological data[J]. Ecological Indicators, 2021, 127: 107767.
[67] ZHAO A Z, YU Q Y, FENG L L, et al. Evaluating the cumulative and time-lag effects of drought on grassland vegetation: A case study in the Chinese Loess Plateau[J]. Journal of Environmental Management, 2020, 261: 110214.
[68] LIU L Y, YANG X Q, GONG F X, et al. The novel microwave temperature vegetation drought index (MTVDI) captures canopy seasonality across Amazonian ttropical evergreen forests[J]. Remote Sensing, 2021, 13(3): 339.
[69] ZHANG Q, KONG D D, SINGH V P, et al. Response of vegetation to different time-scales drought across China: Spatiotemporal patterns, causes and implications[J]. Global and Planetary Change, 2017, 152: 1-11.
[70] COOLEY S S, WILLIAMS C A, FISHER J B, et al. Assessing regional drought impacts on vegetation and evapotranspiration: a case study in Guanacaste, Costa Rica[J]. Ecological Applications, 2019, 29(2): e01834.
[71] LIU X Y, TIAN Y, LIU S Q, et al. Time-lag effect of climate conditions on vegetation productivity in a temperate forest-grassland ecotone[J]. Forests, 2022, 13(7): 1024.
[72] NIU J, CHEN J, SUN L Q, et al. Time-lag effects of vegetation responses to soil moisture evolution: a case study in the Xijiang basin in South China[J]. Stochastic Environmental Research and Risk Assessment, 2018, 32(8): 2423-2432.
[73] ZHAN C, LIANG C, ZHAO L, et al. Drought-related cumulative and time-lag effects on vegetation dynamics across the Yellow River Basin, China[J]. Ecological Indicators, 2022, 143: 109409.
[74] ZUO D P, HAN Y N, XU Z X, et al. Time-lag effects of climatic change and drought on vegetation dynamics in an alpine river basin of the Tibet Plateau, China[J]. Journal of Hydrology, 2021, 600: 126532.
[75] ZHAO J, HUANG S Z, HUANG Q, et al. Time-lagged response of vegetation dynamics to climatic and teleconnection factors[J]. Catena, 2020, 189: 104474.
[76] JIAO W Z, CHANG Q, WANG L X. The sensitivity of satellite solar-induced chlorophyll fluorescence to meteorological drought[J]. Earths Future, 2019, 7(5): 558-573.
[77] ZHOU Z Q, LIU S N, DING Y B, et al. Assessing the responses of vegetation to meteorological drought and its influencing factors with partial wavelet coherence analysis[J]. Journal of Environmental Management, 2022, 311: 114879.
[78] DING Y B, WANG F Z, MU Q, et al. Estimating land use/land cover change impacts on vegetation response to drought under 'Grain for Green' in the Loess Plateau[J]. Land Degradation & Development, 2021, 32(17): 5083-5098.
[79] CHEN X J, MO X G, ZHANG Y C, et al. Drought detection and assessment with solar-induced chlorophyll fluorescence in summer maize growth period over North China Plain[J]. Ecological Indicators, 2019, 104: 347-356.
[80] GUANTER L, ALONSO L, GOMEZ-CHOVA L, et al. Estimation of solar-induced vegetation fluorescence from space measurements[J]. Geophysical Research Letters, 2007, 34(8): L08401.
[81] SONG L, GUANTER L, GUAN K Y, et al. Satellite sun-induced chlorophyll fluorescence detects early response of winter wheat to heat stress in the Indian Indo-Gangetic Plains[J]. Global Change Biology, 2018, 24(9): 4023-4037.
[82] MOYA I, CAMENEN L, EVAIN S, et al. A new instrument for passive remote sensing measurements of sunlight-induced chlorophyll fluorescence[J]. Remote Sensing of Environment, 2004, 91(2): 186-197.
[83] HE B, CHEN A F, WANG H L, et al. Dynamic response of satellite-derived vegetation growth to climate change in the Three North Shelter Forest Region in China[J]. Remote Sensing, 2015, 7(8): 9998-10016.
[84] LI J D, LEI H M. Impacts of climate change on winter wheat and summer maize dual-cropping system in the North China Plain[J]. Environmental Research Communications, 2022, 4(7): 075014.
[85] FANG W, HUANG S Z, HUANG Q, et al. Probabilistic assessment of remote sensing-based terrestrial vegetation vulnerability to drought stress of the Loess Plateau in China[J]. Remote Sensing of Environment, 2019, 232: 111290.
[86] WAN S Q, WANG L, FENG G L, et al. Potential impacts of global warming on extreme warm month events in China[J]. Acta Physica Sinica, 2009, 58(7): 5083-5090.
[87] DIFFENBAUGH N S, SINGH D, MANKIN J S, et al. Quantifying the influence of global warming on unprecedented extreme climate events[J]. Proceedings of the National Academy of Sciences of the United States of America, 2017, 114(19): 4881-4886.
[88] MARJANI S, ALIZADEH-CHOOBARI O, IRANNEJAD P. Frequency of extreme El Nino and La Nina events under global warming[J]. Climate Dynamics, 2019, 53(9-10): 5799-5813.
[89] LUO M, LIU Y Z, SHAO T B. Response of drylands' water-cycle to the global warming[J]. International Journal of Climatology, 2021, 41(9): 4587-4602.
[90] YUAN C X, ZHU G Q, YANG S N, et al. Soil warming increases soil temperature sensitivity in subtropical Forests of SW China[J]. Peerj, 2019, 7: e7721.
[91] GAMAL G, SAMAK M, SHAHBA M. The possible impacts of different global warming levels on major crops in Egypt[J]. Atmosphere, 2021, 12(12): 1589.
[92] ZHANG Q, HAN L Y, JIA J Y, et al. Management of drought risk under global warming[J]. Theoretical and Applied Climatology, 2016, 125(1-2): 187-196.
[93] ZHANG X, HAO Z C, SINGH V P, et al. Drought propagation under global warming: Characteristics, approaches, processes, and controlling factors[J]. Science of the Total Environment, 2022, 838: 156021.
[94] COOK B I, MANKIN J S, MARVEL K, et al. Twenty-First century drought projections in the CMIP6 forcing scenarios[J]. Earths Future, 2020, 8(6): UNSP e2019EF001461.
[95] AADHAR S, MISHRA V. On the Projected decline in droughts over South Asia in CMIP6 multimodel ensemble[J]. Journal of Geophysical Research-Atmospheres, 2020, 125(20): e2020JD033587.
[96] TA Z J, YU Y, SUN L X, et al. Assessment of precipitation simulations in Central Asia by CMIP5 climate models[J]. Water, 2018, 10(11): 1516.
[97] ZHU B L, XUE L Q, WEI G H, et al. CMIP5 projected changes in temperature and precipitation in arid and humid basins[J]. Theoretical and Applied Climatology, 2019, 136(3-4): 1133-1144.
[98] YANG X Y, ZHANG S B, LYU Y Q, et al. Characteristics and future projections of summer extreme precipitation in Sichuan Province, China[J]. Journal of Mountain Science, 2020, 17(7): 1696-1711.
[99] PENALBA O C, RIVERA J A. Regional aspects of future precipitation and meteorological drought characteristics over Southern South America projected by a CMIP5 multi-model ensemble[J]. International Journal of Climatology, 2016, 36(2): 974-986.
[100] YAO N, LI L C, FENG P Y, et al. Projections of drought characteristics in China based on a standardized precipitation and evapotranspiration index and multiple GCMs[J]. Science of the Total Environment, 2020, 704: 135245.
[101] EYRING V, BONY S, MEEHL G A, et al. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization[J]. Geoscientific Model Development, 2016, 9(5): 1937-1958.
[102] BOCK L, LAUER A, SCHLUND M, et al. Quantifying progress across different CMIP phases with the ESMValTool[J]. Journal of Geophysical Research-Atmospheres, 2020, 125(21): e2019JD032321.
[103] XIN X G, WU T W, ZHANG J, et al. Comparison of CMIP6 and CMIP5 simulations of precipitation in China and the East Asian summer monsoon[J]. International Journal of Climatology, 2020, 40(15): 6423-6440.
[104] ZHU H H, JIANG Z H, LI J, et al. Does CMIP6 inspire more confidence in simulating climate extremes over China?[J]. Advances in Atmospheric Sciences, 2020, 37(10): 1119-1132.
[105] LI Y, YAN D H, PENG H, et al. Evaluation of precipitation in CMIP6 over the Yangtze River Basin[J]. Atmospheric Research, 2021, 253: 105406.
[106] SU B D, HUANG J L, MONDAL S K, et al. Insight from CMIP6 SSP-RCP scenarios for future drought characteristics in China[J]. Atmospheric Research, 2021, 250: 105375.
[107] ZHAI J Q, MONDAL S K, FISCHER T, et al. Future drought characteristics through a multi-model ensemble from CMIP6 over South Asia[J]. Atmospheric Research, 2020, 246: 105111.
[108] SONG Y H, SHAHI A, CHUNG E S. Differences in multi-model ensembles of CMIP5 and CMIP6 projections for future droughts in South Korea[J]. International Journal of Climatology, 2022, 42(5): 2688-2716.
[109] O'NEILL B C, TEBALDI C, VAN VUUREN D P, et al. The scenario model intercomparison project (ScenarioMIP) for CMIP6[J]. Geoscientific Model Development, 2016, 9(9): 3461-3482.
[110] LAMBERT S J, BOER G J. CMIP1 evaluation and intercomparison of coupled climate models[J]. Climate Dynamics, 2001, 17(2-3): 83-106.
[111] GLECKLER P J, TAYLOR K E, DOUTRIAUX C. Performance metrics for climate models[J]. Journal of Geophysical Research-Atmospheres, 2008, 113(D6): D06104.
[112] ZHOU Z Q, SHI H Y, FU Q, et al. Investigating the propagation from meteorological to hydrological drought by introducing the nonlinear dependence with directed information transfer index[J]. Water Resources Research, 2021, 57(8): e2021WR030028.
[113] PELOSI A, TERRIBILE F, D'URSO G, et al. Comparison of ERA5-Land and UERRA MESCAN-SURFEX reanalysis data with spatially interpolated weather observations for the regional assessment of reference evapotranspiration[J]. Water, 2020, 12(6): 1669.
[114] SUN Y, FRANKENBERG C, JUNG M, et al. Overview of solar-induced chlorophyll fluorescence (SIF) from the Orbiting Carbon Observatory-2: Retrieval, cross-mission comparison, and global monitoring for GPP[J]. Remote Sensing of Environment, 2018, 209: 808-823.
[115] GAO Y, WANG S H, GUAN K Y, et al. The ability of sun-induced chlorophyll fluorescence from OCO-2 and MODIS-EVI to monitor spatial variations of soybean and maize yields in the Midwestern USA[J]. Remote Sensing, 2020, 12(7):1111.
[116] LI X, XIAO J F. A global, 0.05-Degree product of solar-induced chlorophyll fluorescence derived from OCO-2, MODIS, and reanalysis data[J]. Remote Sensing, 2019, 11(5): 1111.
[117] TOUMA D, ASHFAQ M, NAYAK M A, et al. A multi-model and multi-index evaluation of drought characteristics in the 21st century[J]. Journal of Hydrology, 2015, 526: 196-207.
[118] RAO J, GARFINKEL C I. CMIP5/6 models project little change in the statistical characteristics of sudden stratospheric warmings in the 21st century[J]. Environmental Research Letters, 2021, 16(3): 034024.
[119] XU K, XU B B, JU J L, et al. Projection and uncertainty of precipitation extremes in the CMIP5 multimodel ensembles over nine major basins in China[J]. Atmospheric Research, 2019, 226: 122-137.
[120] BAGALE D, SIGDEL M, ARYAL D. Drought monitoring over Nepal for the last four decades and its connection with Southern Oscillation Index[J]. Water, 2021, 13(23): 3411.
[121] MAIR A, FARES A. Influence of groundwater pumping and rainfall spatio-temporal variation on streamflow[J]. Journal of Hydrology, 2010, 393(3-4): 287-308.
[122] PIYOOSH A K, GHOSH S K. Identification and analysis of recent temporal temperature trends for Dehradun, Uttarakhand, India[J]. Meteorology and Atmospheric Physics, 2019, 131(4): 863-882.
[123] DABA M H, AYELE G T, YOU S C. Long-term homogeneity and trends of hydroclimatic variables in upper Awash River Basin, Ethiopia[J]. Advances in Meteorology, 2020, 2020: 8861959.
[124] GIRMA A, YAN D H, WANG H, et al. Trends of hydroclimate variables in the upper Huai River Basin: Implications of managing water resource for climate change mitigation[J]. Advances in Meteorology, 2020, 2020: 8817068.
[125] MERRIKHPOUR M H, RAHIMZADEGAN M. Analysis of temporal and spatial variations of total precipitable water vapor in western Iran using radiosonde and MODIS measurements[J]. Journal of Applied Remote Sensing, 2019, 13(4): 044508.
[126] DEHGHANI M, SAGHAFIAN B, ZARGAR M. Probabilistic hydrological drought index forecasting based on meteorological drought index using Archimedean copulas[J]. Hydrology Research, 2019, 50(5): 1230-1250.
[127] DEHGHANNIK M, KAVIANPOUR M R, MOAZAMI S. Spatial analysis of meteorological and hydrological drought characteristics using Copula model[J]. Environmental Earth Sciences, 2021, 80(24): 804.
[128] SALIMI H, ASADI E, DARBANDI S. Meteorological and hydrological drought monitoring using several drought indices[J]. Applied Water Science, 2021, 11(2): 11.
[129] ZOU L D, CAO S, SANCHEZ-AZOFEIFA A. Evaluating the utility of various drought indices to monitor meteorological drought in Tropical Dry Forests[J]. International Journal of Biometeorology, 2020, 64(4): 701-711.
[130] NIU J, CHEN J, SUN L Q. Exploration of drought evolution using numerical simulations over the Xijiang (West River) basin in South China[J]. Journal of Hydrology, 2015, 526: 68-77.
[131] LIU B J, CHEN X H, CHEN J F, et al. Impacts of different threshold definition methods on analyzing temporal-spatial features of extreme precipitation in the Pearl River Basin[J]. Stochastic Environmental Research and Risk Assessment, 2017, 31(5): 1241-1252.
[132] YANG Y, GAN T Y, TAN X Z. Spatiotemporal changes of drought characteristics and their dynamic drivers in Canada[J]. Atmospheric Research, 2020, 232: 104695.
[133] GRINSTED A, MOORE J C, JEVREJEVA S. Application of the cross wavelet transform and wavelet coherence to geophysical time series[J]. Nonlinear Processes in Geophysics, 2004, 11(5-6): 561-566.
[134] TIWARI A K, BHANJA N, DAR A B, et al. Time-frequency relationship between share prices and exchange rates in India: Evidence from continuous wavelets[J]. Empirical Economics, 2015, 48(2): 699-714.
[135] YANG R W, CAO J, HUANG W, et al. Cross wavelet analysis of the relationship between total solar irradiance and sunspot number[J]. Chinese Science Bulletin, 2010, 55(20): 2126-2130.
[136] KUMAR K S, ANANDRAJ P, SREELATHA K, et al. Monthly and seasonal drought characterization using GRACE-Based groundwater drought index and its link to teleconnections across South Indian river basins[J]. Climate, 2021, 9(4): 56.
[137] CHONG K L, HUANG Y F, KOO C H, et al. Spatiotemporal variability analysis of standardized precipitation indexed droughts using wavelet transform[J]. Journal of Hydrology, 2022, 605: 127299.
[138] TORRENCE C, COMPO G P. A practical guide to wavelet analysis[J]. Bulletin of the American Meteorological Society, 1998, 79(1): 61-78.
[139] 刘可,杜灵通,侯静,胡悦,朱玉果,宫菲. 近 30年中国陆地生态系统NDVI时空变化特征[J]. 生态学报 , 2018, 38(6): 1885-1896.
[140] HU W, SI B. Technical Note: Improved partial wavelet coherency for understanding scale-specific and localized bivariate relationships in geosciences[J]. Hydrology and Earth System Sciences, 2021, 25(1): 321-331.
[141] AGUIAR-CONRARIA L, SOARES M J. The continuous wavelet transform: Moving beyond uniand bivariate analysis[J]. Journal of Economic Surveys, 2014, 28(2): 344-375.
[142] GUO Y, HUANG S Z, HUANG Q, et al. Propagation thresholds of meteorological drought for triggering hydrological drought at various levels[J]. Science of the Total Environment, 2020, 712: 136502.
[143] PEARSON K. Notes on regression and inheritance in the case of two parents[J]. Proceedings of the Royal Society of London, 1895, 580: 240-242.
[144] FANG W, HUANG S Z, HUANG Q, et al. Identifying drought propagation by simultaneously considering linear and nonlinear dependence in the Wei River basin of the Loess Plateau, China[J]. Journal of Hydrology, 2020, 591: 125287.
[145] SHI B, JIANG J P, SIVAKUMAR B, et al. Quantitative design of emergency monitoring network for river chemical spills based on discrete entropy theory[J]. Water Research, 2018, 134: 140-152.
[146] SHI Y Q, ZHAO C, PENG Z Q, et al. Analysis of the lag effect of embankment dam seepage based on delayed mutual information[J]. Engineering Geology, 2018, 234: 132-137.
[147] TU X J, SINGH V P, CHEN X H, et al. Uncertainty and variability in bivariate modeling of hydrological droughts[J]. Stochastic Environmental Research and Risk Assessment, 2016, 30(5): 1317-1334.
[148] SATTAR M N, LEE J Y, SHIN J Y, et al. Probabilistic characteristics of drought propagation from meteorological to hydrological drought in South Korea[J]. Water Resources Management, 2019, 33(7): 2439-2452.
[149] 黎伟标,杜尧东,王国栋,吴美双,许吟隆. 基于卫星探测资料的珠江三角洲城市群对降水影响的观测研究[J]. 大气科学 , 2009, 33(6): 1259-1266.
[150] WU J F, CHEN X H, YU Z X, et al. Assessing the impact of human regulations on hydrological drought development and recovery based on a 'simulated-observed' comparison of the SWAT model[J]. Journal of Hydrology, 2019, 577: 123990.
[151] WU J F, LIU Z Y, YAO H X, et al. Impacts of reservoir operations on multi-scale correlations between hydrological drought and meteorological drought[J]. Journal of Hydrology, 2018, 563: 726-736.
[152] GE J W, JIA X J, LIN H. The interdecadal change of the leading mode of the winter precipitation over China[J]. Climate Dynamics, 2016, 47(7-8): 2397-2411.
[153] WANG L, HUANG G, CHEN W, et al. Decadal background for active extreme drought episodes in the decade of 2010-19 over Southeastern Mainland Asia[J]. Journal of Climate, 2022, 35(9): 2785-2803.
[154] FANG Y Y, MICHALAK A M, SCHWALM C R, et al. Global land carbon sink response to temperature and precipitation varies with ENSO phase[J]. Environmental Research Letters, 2017, 12(6): 064007.
[155] ZANCHETTIN D, RUBINO A, TRAVERSO P, et al. Impact of variations in solar activity on hydrological decadal patterns in northern Italy[J]. Journal of Geophysical Research-Atmospheres, 2008, 113(D12): D12102.
[156] HUANG C, ZHANG Q, SINGH V P, et al. Spatio-temporal variation of dryness/wetness across the Pearl River basin, China, and relation to climate indices[J]. International Journal of Climatology, 2017, 37: 318-332.
[157] DUAN L M, ZHENG J Y, LI W, et al. Multivariate properties of extreme precipitation events in the Pearl River basin, China: Magnitude, frequency, timing, and related causes[J]. Hydrological Processes, 2017, 31(21): 3662-3671.
[158] WU J F, TAN X Z, CHEN X H, et al. Dynamic changes of the dryness/wetness characteristics in the largest river basin of South China and their possible climate driving factors[J]. Atmospheric Research, 2020, 232: 104685.
[159] CHEN Z H, GRASBY S E. Reconstructing river discharge trends from climate variables and prediction of future trends[J]. Journal of Hydrology, 2014, 511: 267-278.
[160] WANG Z L, ZHONG R D, LAI C G, et al. Climate change enhances the severity and variability of drought in the Pearl River Basin in South China in the 21st century[J]. Agricultural and Forest Meteorology, 2018, 249: 149-162.
[161] WANG F, WANG Z M, YANG H B, et al. Comprehensive evaluation of hydrological drought and its relationships with meteorological drought in the Yellow River basin, China[J]. Journal of Hydrology, 2020, 584: 124751.
[162] YAN H, WANG S Q, WANG J B, et al. Assessing spatiotemporal variation of drought in China and its impact on agriculture during 1982-2011 by using PDSI indices and agriculture drought survey data[J]. Journal of Geophysical Research-Atmospheres, 2016, 121(5): 2283-2298.
[163] 黄强,陈子燊,唐常源,李绍峰. 珠江流域重大干旱事件时空发展过程反演研究[J]. 地球科学进展 , 2019, 34(10): 1050-1059.
[164] TIAN Q, YANG S L. Regional climatic response to global warming: Trends in temperature and precipitation in the Yellow, Yangtze and Pearl River basins since the 1950s[J]. Quaternary International, 2017, 440: 1-11.
[165] 李天生,夏军. 基于 Budyko 理论分析珠江流域中上游地区气候与植被变化对径流的影响[J]. 地球科学进展 地球科学进展 , 2018, 33(12): 1248-1258.
[166] GUO Y, HUANG Q, HUANG S Z, et al. Elucidating the effects of mega reservoir on watershed drought tolerance based on a drought propagation analytical method[J]. Journal of Hydrology, 2021, 598
[167] GORNALL J, BETTS R, BURKE E, et al. Implications of climate change for agricultural productivity in the early twenty-first century[J]. Philosophical Transactions of the Royal Society B-Biological Sciences, 2010, 365(1554): 2973-2989.
[168] XU Y, ZHANG X, WANG X, et al. Propagation from meteorological drought to hydrological drought under the impact of human activities: A case study in northern China[J]. Journal of Hydrology, 2019, 579: 124147.
[169] WANG T, CHEN J S, ZHANG C M, et al. An entropy-based analysis method of precipitation isotopes revealing main moisture transport corridors globally[J]. Global and Planetary Change, 2020, 187: 103134.
[170] BARKER L J, HANNAFORD J, CHIVERTON A, et al. From meteorological to hydrological drought using standardised indicators[J]. Hydrology and Earth System Sciences, 2016, 20(6): 2483-2505.
[171] BIN GHOMASH S K, CAVIEDES-VOULLIEME D, HINZ C. Effects of erosion-induced changes to topography on runoff dynamics[J]. Journal of Hydrology, 2019, 573: 811-828.
[172] 李默然,丁贵杰,鲍斌,高祥,彭云,张鹏. 东南地区不同森林类型涵养水源功能研究[J]. 广东农业科学 , 2013, 40(10): 162-165.
[173] FERNANDEZ T, TREJO I. Rainfall interception based on indirect methods: A Case ctudy in temperate forests in Oaxaca, Mexico[J]. Journal of the American Water Resources Association, 2020, 56(4): 712-723.
[174] 刘霞,张光灿,李雪蕾,邢先双,赵玫. 小流域生态修复过程中不同森林植被土壤入渗与贮水特征[J]. 水土保持学报 , 2004, 6: 1-5.
[175] KILIC O M. Effects of land use and land cover changes on soil erosion in semi-arid regions of Turkey: a case study in Almus lake watershed [J]. Carpathian Journal of Earth and Environmental Sciences, 2021, 16(1): 129-138.
[176] WANG B, ZHANG G H, SHI Y Y, et al. Soil detachment by overland flow under different vegetation restoration models in the Loess Plateau of China[J]. Catena, 2014, 116: 51-59.
[177] ZHANG X X, SONG J X, WANG Y R, et al. Effects of land use on slope runoff and soil loss in the Loess Plateau of China: A meta-analysis[J]. Science of the Total Environment, 2021, 755: 142418.
[178] GARDON F R, DE TOLEDO R M, BRENTAN B M, et al. Rainfall interception and plant community in young forest restorations[J]. Ecological Indicators, 2020, 109: 105779.
[179] MA C K, LUO Y, SHAO M A, et al. Estimation and testing of linkages between forest structure and rainfall interception characteristics of a Robinia pseudoacacia plantation on China's Loess Plateau[J]. Journal of Forestry Research, 2022, 33(2): 529-542.
[180] CHRISTIAN J I, BASARA J B, OTKIN J A, et al. A methodology for flash drought identification: Application of flash drought frequency across the United States[J]. Journal of Hydrometeorology, 2019, 20(5): 833-846.
[181] MOZNY M, TRNKA M, ZALUD Z, et al. Use of a soil moisture network for drought monitoring in the Czech Republic[J]. Theoretical and Applied Climatology, 2012, 107(1-2): 99-111.
[182] ZHANG Y Q, YOU Q L, CHEN C C, et al. Flash droughts in a typical humid and subtropical basin: A case study in the Gan River Basin, China[J]. Journal of Hydrology, 2017, 551: 162-176.
[183] YUAN X, MA Z G, PAN M, et al. Microwave remote sensing of short-term droughts during crop growing seasons[J]. Geophysical Research Letters, 2015, 42(11): 4394-4401.
[184] 王睿卿,蒋聂. 基于地理探测器的珠江流域 NDVI时空变化及驱动力分析[J]. 人民珠江 , 2022, 7: 61-73.
[185] MA Y, LIU L Y, CHEN R N, et al. Generation of a global spatially continuous TanSat solar-induced chlorophyll fluorescence product by considering the impact of the solar radiation intensity[J]. Remote Sensing, 2020, 12(13): 2167.
[186] WU J P, SU Y X, CHEN X Z, et al. Leaf shedding of Pan-Asian tropical evergreen forests depends on the synchrony of seasonal variations of rainfall and incoming solar radiation[J]. Agricultural and Forest Meteorology, 2021, 311: 108691.
[187] LIU L Z, ZHAO W H, WU J, et al. The impacts of growth and environmental parameters on solar-induced chlorophyll fluorescence at seasonal and diurnal scales[J]. Remote Sensing, 2019, 11(17): 2002.
[188] JEONG S J, SCHIMEL D, FRANKENBERG C, et al. Application of satellite solar-induced chlorophyll fluorescence to understanding large-scale variations in vegetation phenology and function over northern high latitude forests[J]. Remote Sensing of Environment, 2017, 190: 178-187.
[189] 谢毅文,李娟,陈伟荣,罗世豪. 1959-2013年珠江流域平均气温时空变化特征[J]. 中山大学报(自然科版), 2016, 55(3): 30-38.
[190] JEONG S J, HO C H, GIM H J, et al. Phenology shifts at start vs. end of growing season in temperate vegetation over the Northern Hemisphere for the period 1982-2008[J]. Global Change Biology, 2011, 17(7): 2385-2399.
[191] CHANG T P, LIU F J, KO H H, et al. Oscillation characteristic study of wind speed, global solar radiation and air temperature using wavelet analysis[J]. Applied Energy, 2017, 190: 650-657.
[192] FANG J Y, PIAO S L, ZHOU L M, et al. Precipitation patterns alter growth of temperate vegetation[J]. Geophysical Research Letters, 2005, 32(21): L21411.
[193] HENNEKAM R, DONDERS T H, ZWIEP K, et al. Integral view of Holocene precipitation and vegetation changes in the Nile catchment area as inferred from its delta sediments[J]. Quaternary Science Reviews, 2015, 130: 189-199.
[194] PAPAGIANNOPOULOU C, MIRALLES D G, DORIGO W A, et al. Vegetation anomalies caused by antecedent precipitation in most of the world[J]. Environmental Research Letters, 2017, 12(7): 074016.
[195] WANG S H, HUANG C P, ZHANG L F, et al. Monitoring and assessing the 2012 drought in the Great Plains: Analyzing satellite-retrieved solar-induced chlorophyll fluorescence, drought indices, and gross primary production[J]. Remote Sensing, 2016, 8(2): 61.
[196] DOBROWSKI S Z, PUSHNIK J C, ZARCO-TEJADA P J, et al. Simple reflectance indices track heat and water stress-induced changes in steady-state chlorophyll fluorescence at the canopy scale[J]. Remote Sensing of Environment, 2005, 97(3): 403-414.
[197] DAUMARD F, CHAMPAGNE S, FOURNIER A, et al. A field platform for continuous measurement of canopy fluorescence[J]. Ieee Transactions on Geoscience and Remote Sensing, 2010, 48(9): 3358-3368.
[198] YEH S W, KUG J S, DEWITTE B, et al. El Nino in a changing climate[J]. Nature, 2009, 461(7263): 511-U570.
[199] 李艳,马百胜, 杨宣. 两类 ENSO事件对中国东部地区极端降水的影响[J]. 长江流域资源与环境 , 2019, 28(2): 469-482.
[200] CHEN W, FENG J, WU R G. Roles of ENSO and PDO in the link of the east Asian winter monsoon to the following summer monsoon[J]. Journal of Climate, 2013, 26(2): 622-635.
[201] ZHANG W X, ZHOU T J, ZHANG L X. Wetting and greening Tibetan Plateau in early summer in recent decades[J]. Journal of Geophysical Research-Atmospheres, 2017, 122(11): 5808-5822.
[202] ZHANG Y, ZHU Z C, LIU Z, et al. Seasonal and interannual changes in vegetation activity of tropical forests in Southeast Asia[J]. Agricultural and Forest Meteorology, 2016, 224: 1-10.
[203] ZENG P, SUN F Y, LIU Y Y, et al. Changes of potential evapotranspiration and its sensitivity across China under future climate scenarios[J]. Atmospheric Research, 2021, 261: 105763.
[204] DONG L Y, FU C S, LIU J G, et al. Combined effects of solar Activity and El Nino on hydrologic patterns in the Yoshino River Basin, Japan[J]. Water Resources Management, 2018, 32(7): 2421-2435.
[205] 石朋,侯爱冰,马欣,陈喜,张志才. 西南喀斯特流域水循环研究进展[J]. 水利电科技进展 , 2012, 32(1): 69-73.
[206] 焦珂伟,高江波,吴绍洪,侯文娟. 植被活动对气候变化的响应过程研究进展[J]. 生态学报 , 2018, 38(6): 2229-2238.
[207] ZHANG Y W, DENG L, YAN W M, et al. Interaction of soil water storage dynamics and long-term natural vegetation succession on the Loess Plateau, China[J]. Catena, 2016, 137: 52-60.
[208] PEEL M C. Hydrology: catchment vegetation and runoff[J]. Progress in Physical Geography, 2009, 33(6): 837-844.
[209] WEI X T, HUANG Q, HUANG S Z, et al. Assessing the feedback relationship between vegetation and soil moisture over the Loess Plateau, China[J]. Ecological Indicators, 2022, 134: 108493.
[210] SHI H Y, ZHAO Y Y, LIU S N, et al. A new perspective on drought propagation: causality[J]. Geophysical Research Letters, 2022, 49(2): e2021GL096758.
[211] WANG Y Q, YANG J, CHEN Y N, et al. Detecting the causal effect of soil moisture on precipitation using convergent cross mapping[J]. Scientific Reports, 2018, 8: 12171.
[212] RIBEIRO A F S, RUSSO A, GOUVEIA C M, et al. Modelling drought-related yield losses in Iberia using remote sensing and multiscalar indices[J]. Theoretical and Applied Climatology, 2019, 136(1-2): 203-220.
[213] LEES T, TSENG G, ATZBERGER C, et al. Deep learning for vegetation health forecasting: A case study in Kenya[J]. Remote Sensing, 2022, 14(3): 698.
[214] CUI Y K, LONG D, HONG Y, et al. Validation and reconstruction of FY-3B/MWRI soil moisture using an artificial neural network based on reconstructed MODIS optical products over the Tibetan Plateau[J]. Journal of Hydrology, 2016, 543: 242-254.
[215] ALEXAKIS D D, TSANIS I K. Comparison of multiple linear regression and artificial neural network models for downscaling TRMM precipitation products using MODIS data[J]. Environmental Earth Sciences, 2016, 75(14): 1077.
[216] SOHRABI M M, RYU J H, ABATZOGLOU J, et al. Development of soil moisture drought index to characterize droughts[J]. Journal of Hydrologic Engineering, 2015, 20(11): 04015025.
[217] PAN H, JIN Y J, ZHU X C. Comparison of projections of precipitation over Yangtze river basin of China by different climate models[J]. Water, 2022, 14(12): 1888.
[218] ZHANG G X, GAN T Y, SU X L. Twenty-first century drought analysis across China under climate change[J]. Climate Dynamics, 2022, 59(5-6): 1665-1685.
[219] DING Y B, JIANG C Z, ZHOU Z Q, et al. Evaluation of precipitation and its time series components in CMIP6 over the Yellow River Basin[J]. Climate Dynamics, 2023, 60(3-4): 1203-1223.
[220] ALLAN R P, BARLOW M, BYRNE M P, et al. Advances in understanding large-scale responses of the water cycle to climate change[J]. Annals of the New York Academy of Sciences, 2020, 1472(1): 49-75.
[221] TONG L, DONG J, YUAN W P. Effects of precipitation and vegetation cover onannual runoff and sediment yield in Northeast China: A preliminary analysis[J]. Water Resources, 2020, 47(3): 491-505.
[222] YU Y, WEI W, CHEN L D, et al. Quantifying the effects of precipitation, vegetation, and land preparation techniques on runoff and soil erosion in a Loess watershed of China[J]. Science of the Total Environment, 2019, 652: 755-764.
[223] FAN H X, XU L G, WANG X L, et al. Identify the influencing paths of precipitation and soil water storage on runoff: an example from Xinjiang River Basin, Poyang Lake, China[J]. Water Science and Technology-Water Supply, 2018, 18(5): 1598-1605.
[224] LI G C, CHEN W, ZHANG X P, et al. Spatiotemporal dynamics of vegetation in China from 1981 to 2100 from the perspective of hydrothermal factor analysis[J]. Environmental Science and Pollution Research, 2022, 29(10): 14219-14230.
[225] YUAN W, WU S Y, HOU S G, et al. Projecting future vegetation change for Northeast China using CMIP6 model[J]. Remote Sensing, 2021, 13(17): 3531.
[226] GEYAERT A I, VELDKAMP T I E, WARD P J. The effect of climate type on timescales of drought propagation in an ensemble of global hydrological models[J]. Hydrology and Earth System Sciences, 2018, 22(9): 4649-4665.
[227] JEHANZAIB M, SATTAR M N, LEE J H, et al. Investigating effect of climate change on drought propagation from meteorological to hydrological drought using multi-model ensemble projections[J]. Stochastic Environmental Research and Risk Assessment, 2020, 34(1): 7-21.
[228] ZENG J X, LI J X, LU X J, et al. Assessment of global meteorological, hydrological and agricultural drought under future warming based on CMIP6[J]. Atmospheric and Oceanic Science Letters, 2022, 15(1): 100143.
[229] 黄金龙,王艳君,苏布达,翟建青. RCP4.5情景下长江上游流域未来气候变化及其对径流的影响[J]. 气象 , 2016, 42(5): 614-620.
[230] TAN X L, LIU S, TIAN Y, et al. Impacts of climate change and land use/cover change on regional hydrological processes: Case of the Guangdong-Hong Kong-Macao Greater Bay Area[J]. Frontiers in Environmental Science, 2022, 9: 783324.
[231] SINGH J, KARMAKAR S, PAIMAZUMDER D, et al. Urbanization alters rainfall extremes over the contiguous United States[J]. Environmental Research Letters, 2020, 15(7): 074033.
[232] SHI P J, BAI X M, KONG F, et al. Urbanization and air quality as major drivers of altered spatiotemporal patterns of heavy rainfall in China[J]. Landscape Ecology, 2017, 32(8): 1723-1738.
[233] JIANG J, ZHOU T J, CHEN X L, et al. Future changes in precipitation over Central Asia based on CMIP6 projections[J]. Environmental Research Letters, 2020, 15(5): 054009.
[234] SONG Z H, XIA J, SHE D X, et al. Assessment of meteorological drought change in the 21st century based on CMIP6 multi-model ensemble projections over mainland China[J]. Journal of Hydrology, 2021, 601: 126643.
[235] GU L, CHEN J, YIN J B, et al. Responses of precipitation and runoff to climate warming and implications for future drought changes in China[J]. Earths Future, 2020, 8(10): e2020EF001718.
[236] BURKE E J, BROWN S J. Evaluating uncertainties in the projection of future drought[J]. Journal of Hydrometeorology, 2008, 9(2): 292-299.
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