Abstract
Under climate change, drought assessment, which can address nonstationarity in drought indicators and anthropogenic implications, is required to mitigate drought impacts. However, the development of drought indices for a reliable drought assessment is a challenging task in the warming climate. Thus, this study discusses factors that should be considered in developing drought indices in changing climate. Inconsistent drought assessment can be obtained, depending on the baseline period defined in developing drought indices. Therefore, the baseline period should represent the contemporary climate but should also correspond to long enough observations for stable parameter estimation. The importance of accurate potential evapotranspiration (PET) for drought indices becomes higher under a warming climate. Although the Penman–Monteith method yields accurate PET values, depending on the climate and vegetation cover, other suitable PET formulas, such as the Hargreaves method, with fewer hydrometeorological data can be used. Since a single drought index is not enough to properly monitor drought evolution, a method that can objectively combine multiple drought indices is required. Besides, quantifying anthropogenic impacts, which can add more uncertainty, on drought assessment is also important to adapt to the changing drought conditions and minimize human-induced drought. Drought is expected to occur more frequently with more severe, longer, and larger areal extent under global warming, since a more arid background, which climate change will provide, intensifies land–atmosphere feedback, leading to the desiccation of land and drying atmosphere. Thus, an accurate drought assessment, based on robust drought indices, is required.
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Abbreviations
- AMSA:
-
Annual mean of spatially averaged
- AMSU:
-
Advanced microwave sounding unit
- BT:
-
Brightness temperature
- CDF:
-
Cumulative distribution function
- CPC:
-
Climate Prediction Center
- CLM:
-
Community land model
- CRU:
-
Climatic Research Unit
- 20CRV3:
-
20Th Century Reanalysis V3
- DA:
-
Detection and attribution
- GAMLSS:
-
Generalized additive models for location, scale, and shape
- GGI:
-
GRACE groundwater drought index
- GPCP:
-
Global Precipitation Climatology Project
- IR:
-
Infrared
- MERRA-2:
-
Modern-era retrospective analysis for research and applications, version 2
- NDVI:
-
Normalized difference vegetation index
- NST_a:
-
Near-surface temperature anomalies
- PDSI:
-
Palmer drought severity index
- PM:
-
Penman-Monteith
- RI:
-
Simplified rainfall index
- SC-PDSI-PM:
-
SC-PDSI with Penman–Monteith
- SDWLI:
-
Standardized depth to water level index
- SMP:
-
Soil moisture percentile
- SPEI:
-
Standardized precipitation evapotranspiration index
- SRI:
-
Standardized runoff index
- SSI:
-
Standardized soil moisture index
- SSMI/S:
-
Scanning sensor microwave/imager
- SWE:
-
Snow water equivalent
- SWSI:
-
Surface water supply index
- TWDB:
-
Texas Water Development Board
- USDM:
-
U.S. Drought Monitor
- VHI:
-
Vegetation health index
- VIS:
-
Visible
- AMSR-E:
-
Advanced Microwave Scanning Radiometer for Earth Observing System
- ARI:
-
Aerosol-radiation interaction
- CAFEC:
-
Climatologically appropriate for existing conditions
- CDVI:
-
Comprehensive drought vulnerability indicator
- CPC-Prec/L:
-
CPC-precipitation reconstruction over land
- CONUS:
-
Continental U.S.
- CRU TS3.0:
-
CRU time-series version 3.0
- CV:
-
Coefficient of variation
- ET:
-
Evapotranspiration
- GCM:
-
General circulation model
- GPCC:
-
Global Precipitation Climatology Centre
- GRACE:
-
Gravity recovery and climate experiment
- LULC:
-
Land use land cover
- MS:
-
Microwave sensor
- NLDAS:
-
North American Land Data Assimilation System
- PDM:
-
Percent departure from mean
- PET:
-
Potential evapotranspiration
- PRSC:
-
Percent reservoir storage capacity
- SC-PDSI:
-
Self-calibrated PDSI
- SDI:
-
Streamflow drought index
- SGI:
-
Standardized groundwater-level index
- SMRI:
-
Standardized snowmelt and rain index
- SPI:
-
Standardized precipitation index
- SRSI:
-
Standardized reservoir storage index
- SSM/I:
-
Scanning sensor microwave/imager
- SST:
-
Sea surface temperature
- SWEI:
-
Standardized snow water equivalent index
- TCI:
-
Temperature condition index
- TWS:
-
Terrestrial water storage
- VCI:
-
Vegetation condition index
- VIC:
-
Variable infiltration capacity
References
AghaKouchak, A., Farahmand, A., Melton, F. S., Teixeira, J., Anderson, M. C., Wardlow, B. D., & Hain, C. R. (2015). Remote sensing of drought: Progress, challenges and opportunities. Reviews of Geophysics, 53(2), 452–480. https://doi.org/10.1002/2014RG000456
Alley, W. M. (1984). The Palmer drought severity index: Limitations and assumptions. Journal of Applied Meteorology and Climatology, 23(7), 1100–1109. https://doi.org/10.1175/1520-0450(1984)023%3c1100:TPDSIL%3e2.0.CO;2
Bloomfield, J. P., & Marchant, B. P. (2013). Analysis of groundwater drought building on the standardised precipitation index approach. Hydrology and Earth System Sciences, 17(12), 4769–4787. https://doi.org/10.5194/hess-17-4769-2013
Cook, B. I., Miller, R. L., & Seager, R. (2009). Amplification of the North American “dust bowl” drought through human-induced land degradation. Proceedings of the National Academy of Sciences, 106(13), 4997–5001. https://doi.org/10.1073/pnas.0810200106
Cook, B. I., Seager, R., Miller, R. L., & Mason, J. A. (2013). Intensification of North American megadroughts through surface and dust aerosol forcing. Journal of Climate, 26(13), 4414–4430. https://doi.org/10.1175/JCLI-D-12-00022.1
Dai, A. (2011). Drought under global warming: A review. Wiley Interdisciplinary Reviews: Climate Change, 2(1), 45–65. https://doi.org/10.1002/wcc.81
Dai, A. (2013). Increasing drought under global warming in observations and models. Nature Climate Change, 3(1), 52–58. https://doi.org/10.1038/nclimate1633
Donohoe, A., & Battisti, D. S. (2011). Atmospheric and surface contributions to planetary albedo. Journal of Climate, 24(16), 4402–4418. https://doi.org/10.1175/2011JCLI3946.1
Donohue, R. J., McVicar, T. R., & Roderick, M. L. (2010). Assessing the ability of potential evaporation formulations to capture the dynamics in evaporative demand within a changing climate. Journal of Hydrology, 386(1–4), 186–197. https://doi.org/10.1016/j.jhydrol.2010.03.020
Durand, M., Molotch, N. P., & Margulis, S. A. (2008). Merging complementary remote sensing datasets in the context of snow water equivalent reconstruction. Remote Sensing of Environment, 112(3), 1212–1225. https://doi.org/10.1016/j.rse.2007.08.010
English, N. B., Weltzin, J. F., Fravolini, A., Thomas, L., & Williams, D. G. (2005). The influence of soil texture and vegetation on soil moisture under rainout shelters in a semi-desert grassland. Journal of Arid Environments, 63(1), 324–343. https://doi.org/10.1016/j.jaridenv.2005.03.013
Estilow, T. W., Young, A. H., & Robinson, D. A. (2015). A long-term Northern Hemisphere snow cover extent data record for climate studies and monitoring. Earth System Science Data, 7(1), 137–142. https://doi.org/10.5194/essd-7-137-2015
Gusyev, M., Hasegawa, A., Magome, J., Kuribayashi, D., Sawano, H., & Lee, S. (2015). Drought assessment in the Pampanga River basin, the Philippines–Part 1: Characterizing a role of dams in historical droughts with standardized indices. In Proceedings of the 21st international congress on modelling and simulation (MODSIM 2015), November 29th–December 4th, Queensland, Australia.
Guttman, N. B. (1999). Accepting the standardized precipitation index: a calculation algorithm 1. JAWRA Journal of the American Water Resources Association, 35(2), 311–322. https://doi.org/10.1111/j.1752-1688.1999.tb03592.x
Guttman, N. B., Wallis, J. R., & Hosking, J. R. M. (1992). Spatial comparability OF the palmer drought severity index 1. JAWRA Journal of the American Water Resources Association, 28(6), 1111–1119. https://doi.org/10.1111/j.1752-1688.1992.tb04022.x
Guttman, N. B. (1994). On the sensitivity of sample L moments to sample size. Journal of Climate, 1026–1029. https://www.jstor.org/stable/26198664. Accessed 16 Oct 2023
Han, J., & Singh, V. P. (2023). Understanding drought: Definitions, causes, assessments, forecasts, and management. In V. P. Singh, D. Jhajharia, R Mirabbasu, & R. Kumar (Eds.), Integrated Drought Management (vol. 1, pp. 1–42). Routledge
Han, J., & Singh, V. P. (2020). Forecasting of droughts and tree mortality under global warming: A review of causative mechanisms and modeling methods. Journal of Water and Climate Change, 11(3), 600–632. https://doi.org/10.2166/wcc.2020.239
Hao, Z., & AghaKouchak, A. (2013). Multivariate standardized drought index: A parametric multi-index model. Advances in Water Resources, 57, 12–18. https://doi.org/10.1016/j.advwatres.2013.03.009
Hao, Z., Singh, V. P., & Xia, Y. (2018). Seasonal drought prediction: Advances, challenges, and future prospects. Reviews of Geophysics, 56(1), 108–141. https://doi.org/10.1002/2016RG000549
Hauser, M., Gudmundsson, L., Orth, R., Jézéquel, A., Haustein, K., Vautard, R., ... & Seneviratne, S. I. (2017). Methods and model dependency of extreme event attribution: the 2015 European drought. Earth’s Future, 5(10), 1034–1043. https://doi.org/10.1002/2017EF000612
Heim, R. R., Jr. (2002). A review of twentieth-century drought indices used in the United States. Bulletin of the American Meteorological Society, 83(8), 1149–1166. https://doi.org/10.1175/1520-0477-83.8.1149
Heim, R. R. (2017). A comparison of the early twenty-first century drought in the United States to the 1930s and 1950s drought episodes. Bulletin of the American Meteorological Society, 98(12), 2579–2592. https://doi.org/10.1175/BAMS-D-16-0080.1
Hoylman, Z. H., Bocinsky, R. K., & Jencso, K. G. (2022). Drought assessment has been outpaced by climate change: Empirical arguments for a paradigm shift. Nature Communications, 13(1), 2715. https://doi.org/10.1038/s41467-022-30316-5
Huning, L. S., & AghaKouchak, A. (2020). Global snow drought hot spots and characteristics. Proceedings of the National Academy of Sciences, 117(33), 19753–19759. https://doi.org/10.1073/pnas.1915921117
Kim, T. W., & Jehanzaib, M. (2020). Drought risk analysis, forecasting and assessment under climate change. Water, 12(7), 1862. https://doi.org/10.3390/w12071862
Kloos, S., Yuan, Y., Castelli, M., & Menzel, A. (2021). Agricultural drought detection with MODIS based vegetation health indices in southeast Germany. Remote Sensing, 13(19), 3907. https://doi.org/10.3390/rs13193907
Kogan, F. N. (1995). Application of vegetation index and brightness temperature for drought detection. Advances in Space Research, 15(11), 91–100. https://doi.org/10.1016/0273-1177(95)00079-T
Kogan, F., Salazar, L., & Roytman, L. (2012). Forecasting crop production using satellite-based vegetation health indices in Kansas, USA. International Journal of Remote Sensing, 33(9), 2798–2814. https://doi.org/10.1080/01431161.2011.621464
Li, Z., Rosenfeld, D., & Fan, J. (2017). Aerosols and their impact on radiation, clouds, precipitation, and severe weather events. In Oxford Research Encyclopedia of Environmental Science. In: Oxford Research Encyclopedia of Environmental Science. Oxford University Press, New York, USA. https://doi.org/10.1093/acrefore/9780199389414.013.126
Van Loon, A. F., Stahl, K., Di Baldassarre, G., Clark, J., Rangecroft, S., Wanders, N., ... & Van Lanen, H. A. (2016). Drought in a human-modified world: Reframing drought definitions, understanding, and analysis approaches. Hydrology and Earth System Sciences, 20(9), 3631-3650. https://doi.org/10.5194/hess-20-3631-2016
Marx, S. M., Weber, E. U., Orlove, B. S., Leiserowitz, A., Krantz, D. H., Roncoli, C., & Phillips, J. (2007). Communication and mental processes: Experiential and analytic processing of uncertain climate information. Global Environmental Change, 17(1), 47–58. https://doi.org/10.1016/j.gloenvcha.2006.10.004
McKee, T. B., Doesken, N. J., & Kleist, J. (1993). The relationship of drought frequency and duration to time scales. In Proceedings of the 8th Conference on Applied Climatology, 17(22), 179–183.
Milly, P. C., Betancourt, J., Falkenmark, M., Hirsch, R. M., Kundzewicz, Z. W., Lettenmaier, D. P., & Stouffer, R. J. (2008). Stationarity is dead: Whither water management? Science, 319(5863), 573–574. https://doi.org/10.1126/science.1151915
Miralles, D. G., Gentine, P., Seneviratne, S. I., & Teuling, A. J. (2019). Land–atmospheric feedbacks during droughts and heatwaves: State of the science and current challenges. Annals of the New York Academy of Sciences, 1436(1), 19–35. https://doi.org/10.1111/nyas.13912
Mishra, A. K., & Singh, V. P. (2010). A review of drought concepts. Journal of Hydrology, 391(1–2), 202–216. https://doi.org/10.1016/j.jhydrol.2010.07.012
Moon, S., & Ha, K. J. (2020). Future changes in monsoon duration and precipitation using CMIP6. Npj Climate and Atmospheric Science, 3(1), 45. https://doi.org/10.1038/s41612-020-00151-w
Mukherjee, S., Mishra, A., & Trenberth, K. E. (2018). Climate change and drought: A perspective on drought indices. Current Climate Change Reports, 4, 145–163. https://doi.org/10.1007/s40641-018-0098-x
Müller Schmied, H., Adam, L., Eisner, S., Fink, G., Flörke, M., Kim, H., ... & Döll, P. (2016). Variations of global and continental water balance components as impacted by climate forcing uncertainty and human water use. Hydrology and Earth System Sciences, 20(7), 2877-2898. https://doi.org/10.5194/hess-20-2877-2016
Nalbantis, I., & Tsakiris, G. (2009). Assessment of hydrological drought revisited. Water resources management, 23, 881–897. https://doi.org/10.1007/s11269-008-9305-1
Nandi, S., & Swain, S. (2022). Analysis of heatwave characteristics under climate change over three highly populated cities of South India: A CMIP6-based assessment. Environmental Science and Pollution Research, 1–13. https://doi.org/10.1007/s11356-022-22398-x
NASA Earth Observatory (2023). World of change: Global temperatures. Retrieved October 16, 2023, from https://earthobservatory.nasa.gov/world-of-change/global-temperatures
Palmer, W. C. (1965). Meteorological Drought. US Department of Commerce, Weather Bureau.
Pascale, S., Kapnick, S. B., Delworth, T. L., & Cooke, W. F. (2020). Increasing risk of another Cape Town “day zero” drought in the 21st century. Proceedings of the National Academy of Sciences, 117(47), 29495–29503. https://doi.org/10.1073/pnas.200914411
Pei, F., Wu, C., Liu, X., Li, X., Yang, K., Zhou, Y., … & Xia, G. (2018). Monitoring the vegetation activity in China using vegetation health indices. Agricultural and Forest Meteorology, 248, 215–227. https://doi.org/10.1016/j.agrformet.2017.10.001
Pimentel, R., Arheimer, B., Crochemore, L., Andersson, J. C. M., Pechlivanidis, I. G., & Gustafsson, D. (2023). Which potential evapotranspiration formula to use in hydrological modelling world‐wide?. Water Resources Research, e2022WR033447. https://doi.org/10.1029/2022WR033447
Quiring, S., Nielsen-Gammon, J. W., Srinivasan, R., Miller, T., & Narasimhan, B. (2007). Drought monitoring index for Texas. Texas Water Development Board Final Tech.
Rigby, R. A., & Stasinopoulos, D. M. (2005). Generalized additive models for location, scale and shape. Journal of the Royal Statistical Society Series c: Applied Statistics, 54(3), 507–554. https://doi.org/10.1111/j.1467-9876.2005.00510.x
Rodell, M. (2012). Satellite gravimetry applied to drought monitoring. Remote Sensing of Drought: Innovative Monitoring Approaches, Chapter, 11, 261–277.
Rodell, M., Chen, J., Kato, H., Famiglietti, J. S., Nigro, J., & Wilson, C. R. (2007). Estimating groundwater storage changes in the Mississippi River basin (USA) using GRACE. Hydrogeology Journal, 15, 159–166. https://doi.org/10.1007/s10040-006-0103-7
Sahoo, S., Swain, S., Goswami, A., Sharma, R., & Pateriya, B. (2021). Assessment of trends and multi-decadal changes in groundwater level in parts of the Malwa region, Punjab, India. Groundwater for Sustainable Development, 14, 100644. https://doi.org/10.1016/j.gsd.2021.100644
Seager, R., Henderson, N., Cane, M. A., Liu, H., & Nakamura, J. (2017). Is there a role for human-induced climate change in the precipitation decline that drove the California drought? Journal of Climate, 30(24), 10237–10258. https://doi.org/10.1175/JCLI-D-17-0192.1
Shafer, B. A., & Dezman L. E. (1982). Development of a Surface Water Supply Index (SWSI) to assess the severity of drought conditions in snowpack runoff areas. In Proceedings of the Western Snow Conference (pp. 164–175). Reno, Nevada.
Shao, S., Zhang, H., Singh, V. P., Ding, H., Zhang, J., & Wu, Y. (2022). Nonstationary analysis of hydrological drought index in a coupled human-water system: application of the GAMLSS with meteorological and anthropogenic covariates in the Wuding River basin, China. Journal of Hydrology, 608, 127692. https://doi.org/10.1016/j.jhydrol.2022.127692
Sheffield, J., Wood, E. F., & Roderick, M. L. (2012). Little change in global drought over the past 60 years. Nature, 491(7424), 435–438. https://doi.org/10.1038/nature11575
Sheffield, J., & Wood, E. F. (2012). Drought: Past Problems and Future Scenarios. Routledge.
Sheffield, J., Goteti, G., Wen, F., & Wood, E. F. (2004). A simulated soil moisture based drought analysis for the United States. Journal of Geophysical Research: Atmospheres, 109(D24). https://doi.org/10.1029/2004JD005182
Shukla, S., & Wood, A. W. (2008). Use of a standardized runoff index for characterizing hydrologic drought. Geophysical Research Letters, 35(2). https://doi.org/10.1029/2007GL032487
Staudinger, M., Stahl, K., & Seibert, J. (2014). A drought index accounting for snow. Water Resources Research, 50(10), 7861–7872. https://doi.org/10.1002/2013WR015143
Stott, P. A., Gillett, N. P., Hegerl, G. C., Karoly, D. J., Stone, D. A., Zhang, X., & Zwiers, F. (2010). Detection and attribution of climate change: A regional perspective. Wiley Interdisciplinary Reviews: Climate Change, 1(2), 192–211. https://doi.org/10.1002/wcc.34
Su, C., & Chen, X. (2019). Assessing the effects of reservoirs on extreme flows using nonstationary flood frequency models with the modified reservoir index as a covariate. Advances in Water Resources, 124, 29–40. https://doi.org/10.1016/j.advwatres.2018.12.004
Sutanto, S. J., Vitolo, C., Di Napoli, C., D’Andrea, M., & Van Lanen, H. A. (2020). Heatwaves, droughts, and fires: exploring compound and cascading dry hazards at the pan-European scale. Environment International, 134, 105276. https://doi.org/10.1016/j.envint.2019.105276
Swain, S., Mishra, S. K., & Pandey, A. (2021). A detailed assessment of meteorological drought characteristics using simplified rainfall index over Narmada River Basin, India. Environmental Earth Sciences, 80, 1–15. https://doi.org/10.1007/s12665-021-09523-8
Swain, S., Mishra, S. K., & Pandey, A. (2022a). Assessing spatiotemporal variation in drought characteristics and their dependence on timescales over Vidarbha Region, India. Geocarto International, 37(27), 17971–17993. https://doi.org/10.1080/10106049.2022.2136260
Swain, S., Mishra, S. K., Pandey, A., & Dayal, D. (2022b). Assessment of drought trends and variabilities over the agriculture-dominated Marathwada Region, India. Environmental Monitoring and Assessment, 194(12), 883. https://doi.org/10.1007/s10661-022-10532-8
Swain, S., Taloor, A. K., Dhal, L., Sahoo, S., & Al-Ansari, N. (2022c). Impact of climate change on groundwater hydrology: A comprehensive review and current status of the Indian hydrogeology. Applied Water Science, 12(6), 120. https://doi.org/10.1007/s13201-022-01652-0
Swain, S., Mishra, S. K., Pandey, A., Dayal, D., & Srivastava, P. K. (2022d). Appraisal of historical trends in maximum and minimum temperature using multiple non-parametric techniques over the agriculture-dominated Narmada Basin, India. Environmental Monitoring and Assessment, 194(12), 893. https://doi.org/10.1007/s10661-022-10534-6
Swain, S., Mishra, S. K., Pandey, A., & Kalura, P. (2022e). Inclusion of groundwater and socio-economic factors for assessing comprehensive drought vulnerability over Narmada River Basin, India: A geospatial approach. Applied Water Science, 12(2), 14. https://doi.org/10.1007/s13201-021-01529-8
Swain, S., Mishra, S. K., & Pandey, A. (2020). Assessment of meteorological droughts over Hoshangabad district, India. In IOP Conference Series: Earth and Environmental Science, 491(1), 012012. IOP Publishing. https://doi.org/10.1088/1755-1315/491/1/012012
Tate, E. L., & Gustard, A. (2000). Drought definition: A hydrological perspective. In J. V. Vogt, & F. Somma (Eds.), Drought and Drought Mitigation in Europe (pp. 23–48). Springer.
Trenberth, K. E. (2011). Changes in precipitation with climate change. Climate Research, 47(1–2), 123–138. https://doi.org/10.3354/cr00953
Trenberth, K. E., & Fasullo, J. T. (2013). An apparent hiatus in global warming? Earth’s Future, 1(1), 19–32. https://doi.org/10.1002/2013EF000165
Trenberth, K. E., Dai, A., Van Der Schrier, G., Jones, P. D., Barichivich, J., Briffa, K. R., & Sheffield, J. (2014). Global warming and changes in drought. Nature Climate Change, 4(1), 17–22. https://doi.org/10.1038/nclimate2067
Trenberth, K. E., & Dai, A. (2007). Effects of Mount Pinatubo volcanic eruption on the hydrological cycle as an analog of geoengineering. Geophysical Research Letters, 34(15). https://doi.org/10.1029/2007GL030524.
USBR (U.S. Bureau of Reclamation) (2023, October 16). Biden-Harris administration makes nearly $200 million available for drought and climate resiliency projects as part of investing in America agenda. Retrieved October 16, 2023, https://www.usbr.gov/newsroom/news-release/4595
van der Schrier, G., Barichivich, J., Briffa, K. R., & Jones, P. D. (2013). A scPDSI-based global data set of dry and wet spells for 1901–2009. Journal of Geophysical Research: Atmospheres, 118(10), 4025–4048. https://doi.org/10.1002/jgrd.50355
Van Loon, A. F., & Van Lanen, H. A. (2013). Making the distinction between water scarcity and drought using an observation modeling framework. Water Resources Research, 49(3), 1483–1502. https://doi.org/10.1002/wrcr.20147
Vicente-Serrano, S. M., Beguería, S., & López-Moreno, J. I. (2010). A multiscalar drought index sensitive to global warming: The standardized precipitation evapotranspiration index. Journal of Climate, 23(7), 1696–1718. https://doi.org/10.1175/2009JCLI2909.1
Wan, W., Zhao, J., Li, H. Y., Mishra, A., Ruby Leung, L., Hejazi, M., ... & Wang, H. (2017). Hydrological drought in the Anthropocene: Impacts of local water extraction and reservoir regulation in the US. Journal of Geophysical Research: Atmospheres, 122(21), 11–313. https://doi.org/10.1002/2017JD026899
Wells, N., Goddard, S., & Hayes, M. J. (2004). A self-calibrating Palmer drought severity index. Journal of Climate, 17(12), 2335–2351. https://doi.org/10.1175/1520-0442(2004)017%3c2335:ASPDSI%3e2.0.CO;2
Wilhite, D. A. (2002). Combating drought through preparedness. Natural Resources Forum, 26(4), 275–285. https://doi.org/10.1111/1477-8947.00030
Wu, H., Hayes, M. J., Wilhite, D. A., & Svoboda, M. D. (2005). The effect of the length of record on the standardized precipitation index calculation. International Journal of Climatology: A Journal of the Royal Meteorological Society, 25(4), 505–520. https://doi.org/10.1002/joc.1142
Zeng, J., Zhou, T., Qu, Y., Bento, V. A., Qi, J., Xu, Y., ... & Wang, Q. (2023). An improved global vegetation health index dataset in detecting vegetation drought. Scientific Data, 10(1), 338. https://doi.org/10.1038/s41597-023-02255-3
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Jeongwoo Han: data collection and curation, data analysis and visualization, writing-original draft, and writing-review & editing. Vijay P. Singh: conceptualization, supervision, validation, and writing-review & editing.
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Han, J., Singh, V.P. A review of widely used drought indices and the challenges of drought assessment under climate change. Environ Monit Assess 195, 1438 (2023). https://doi.org/10.1007/s10661-023-12062-3
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DOI: https://doi.org/10.1007/s10661-023-12062-3