Journals / Türkiye Uzaktan Algılama Dergisi / 2026 / Volume: 8

Drought regime shifts and changing drought characteristics in Jharkhand, India: A SPEI based analysis from 1951 to 2025

Shrinwantu Raha(Bhairab Ganguly College)0000-0003-4883-8657
Sayan Deb(Bhairab Ganguly College)0000-0001-7021-2218
Pages
—
Publication Date
2026-10-05
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The drought frequency increased significantly in the central districts and the drought return period fluctuates overall by around −80.46% to 17% in the Jharkhand state, that is more frequent drought recurrence.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Drought is a recurring hydro-climatic hazard that heavily alters agricultural productivity and depletes water resources.
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Key Findings & Data:This research investigates the spatiotemporal characteristics of drought in Jharkhand, India, using the Standardized Precipitation Evapotranspiration Index (SPEI) derived from long-term climatic records (1951–2025) acquired from the India Meteorological Department.
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Conclusion & Impact:The drought frequency increased significantly in the central districts and the drought return period fluctuates overall by around −80.46% to 17% in the Jharkhand state, that is more frequent drought recurrence.
🔬Design:Research Article
📍Scope:2025 Verileri
⚙️Variables:SPEI · homogeneity test · drought variability

Abstract

Drought is a recurring hydro-climatic hazard that heavily alters agricultural productivity and depletes water resources. This research investigates the spatiotemporal characteristics of drought in Jharkhand, India, using the Standardized Precipitation Evapotranspiration Index (SPEI) derived from long-term climatic records (1951–2025) acquired from the India Meteorological Department. This research evaluated key drought characteristics: frequency, duration, magnitude, intensity, return period and trend. Trend-Free Pre-Whitening was applied prior to Mann–Kendall and Sen’s slope analyses to ensure robust trend detection, and the Pettitt test was used to identify significant change points in regional drought dynamics. The results identified a significant hydro-climatic transition during 2008–2010, followed by intensified and more persistent drought conditions across Jharkhand. Post-change-point analysis revealed a positive upward trend in drought intensity, ranging from 121.4% to 275.9% in the northeastern part of the region, and an increase in average drought duration ranging from 91.2% to 237.5% in the eastern pockets with droughts of around 4.5 months in the northeast. The changes in drought magnitude were spatially variable, ranging from −72.2% to +150.6% with the greatest positive changes in the northeastern region. The drought frequency increased significantly in the central districts and the drought return period fluctuates overall by around −80.46% to 17% in the Jharkhand state, that is more frequent drought recurrence. Overall, the trend results showed an increasing drought trends especially apparent in the northeastern and northwestern portions, and decreasing drought tendencies in parts of the central region. The Sen’s slope analysis also revealed spatial differences in drought evolution with slopes from around −0.0134 to +0.058. Homogeneity tests (Student’s t-test, Wilcoxon rank-sum test, and F-test) revealed significant changes in mean and distribution at several stations after the identified change point, while variance remained statistically homogeneous across all stations. These findings provide valuable insights for drought monitoring.

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Cite This Article

Raha, S., Deb, S. (2026). Drought regime shifts and changing drought characteristics in Jharkhand, India: A SPEI based analysis from 1951 to 2025. Türkiye Uzaktan Algılama Dergisi, Volume: 8. https://doi.org/10.51489/tuzal.1979467

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References (85)

  1. 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
  2. Ahady, A. B., Klopries, E.-M., Schüttrumpf, H., & Wolf, S. (2025). Drought analysis methods: A multidisciplinary review with insights on key decision-making factors in method selection. Water, 17(15), 2248. https://doi.org/10.3390/w17152248
  3. Aktürk, G., Çıtakoğlu, H., Demir, V., & Beden, N. (2024). Meteorological drought analysis and regional frequency analysis in the Kızılırmak Basin: Creating a framework for sustainable water resources management. Water, 16(15), 2124. https://doi.org/10.3390/w16152124
  4. Alley, W. M. (1984). The Palmer drought severity index: Limitations and assumptions. Journal of Climate and Applied Meteorology, 23(7), 1100–1109. https://doi.org/10.1175/1520-0450(1984)023 2.0.CO;2
  5. Beguería, S., Vicente-Serrano, S. M., Reig, F., & Latorre, B. (2014). Standardized precipitation evapotranspiration index (SPEI) revisited: Parameter fitting, evapotranspiration models, tools, datasets and drought monitoring. International Journal of Climatology, 34(10), 3001–3023. https://doi.org/10.1002/joc.3887
  6. Bevacqua, E., Rakovec, O., Schumacher, D. L., Kumar, R., Thober, S., Samaniego, L., Seneviratne, S. I., & Zscheischler, J. (2024). Direct and lagged climate change effects intensified the 2022 European drought. Nature Geoscience, 17(11), 1100–1107. https://doi.org/10.1038/s41561-024-01559-2
  7. Bharghavi, K., Reddy, T. L., Kapa, H., Rajesh, P., Sushmitha, H., & Reddy, K. K. (2025). Evaluating climate change impact on drought: A comprehensive review of drought indices and future projections. Natural Hazards, 121(18), 20819–20854. https://doi.org/10.1007/s11069-025-07681-7
  8. Bürger, G. (2017). On trend detection. Hydrological Processes, 31(22), 4039–4042. https://doi.org/10.1002/hyp.11280
  9. Byun, H.-R., & Wilhite, D. A. (1999). Objective quantification of drought severity and duration. Journal of Climate, 12(9), 2747–2756. https://doi.org/10.1175/1520-0442(1999)012 2.0.CO;2
  10. Choudhury, A., Dutta, D., Bera, D., & Kundu, A. (2021). Regional variation of drought parameters and long-term trends over India using standardized precipitation evapotranspiration index. Journal of Environmental Management, 296, 113056. https://doi.org/10.1016/j.jenvman.2021.113056
  11. Chuphal, D. S., Kushwaha, A. P., Aadhar, S., & Mishra, V. (2024). Drought Atlas of India, 1901–2020. Scientific Data, 11(1), 7. https://doi.org/10.1038/s41597-023-02856-y
  12. Citakoglu, H., Aktürk, G., & Demir, V. (2025). Hybrid machine learning for drought prediction at multiple time scales: A case study of Ağrı station, Türkiye. Acta Geophysica, 73(2), 1643–1677. https://doi.org/10.1007/s11600-024-01501-5
  13. Cook, B. I., Mankin, J. S., & Anchukaitis, K. J. (2018). Climate change and drought: From past to future. Current Climate Change Reports, 4(2), 164–179. https://doi.org/10.1007/s40641-018-0093-2
  14. 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
  15. Das, P. K., Dutta, D., Sharma, J. R., & Dadhwal, V. K. (2016). Trends and behaviour of meteorological drought (1901–2008) over Indian region using standardized precipitation–evapotranspiration index. International Journal of Climatology, 36(2), 909–916. https://doi.org/10.1002/joc.4392
  16. Demir, V. (2022). Trend analysis of lakes and sinkholes in the Konya Closed Basin, in Turkey. Natural Hazards, 112(3), 2873–2912. https://doi.org/10.1007/s11069-022-05327-6
  17. Demir, V., & Keskin, A. Ü. (2020). Water level change of lakes and sinkholes in Central Turkey under anthropogenic effects. Theoretical and Applied Climatology, 142(3), 929–943. https://doi.org/10.1007/s00704-020-03347-5
  18. Dracup, J. A., Lee, K. S., & Paulson, E. G., Jr. (1980). On the statistical characteristics of drought events. Water Resources Research, 16(2), 289–296. https://doi.org/10.1029/WR016i002p00289
  19. Duggins, J., Williams, M., Kim, D.-Y., & Smith, E. (2010). Changepoint detection in SPI transition probabilities. Journal of Hydrology, 388(3), 456–463. https://doi.org/10.1016/j.jhydrol.2010.05.030
  20. Francis, P. A., & Gadgil, S. (2010). Towards understanding the unusual Indian monsoon in 2009. Journal of Earth System Science, 119(4), 397–415. https://doi.org/10.1007/s12040-010-0033-6
  21. Ganguli, P., & Reddy, M. J. (2014). Evaluation of trends and multivariate frequency analysis of droughts in three meteorological subdivisions of western India. International Journal of Climatology, 34(3), 911–928. https://doi.org/10.1002/joc.3742
  22. Ge, Y., Cai, X., Zhu, T., & Ringler, C. (2016). Drought frequency change: An assessment in northern India plains. Agricultural Water Management, 176, 111–121. https://doi.org/10.1016/j.agwat.2016.05.015
  23. Ghosh-Jerath, S., Kapoor, R., Ghosh, U., Singh, A., Downs, S., & Fanzo, J. (2021). Pathways of climate change impact on agroforestry, food consumption pattern, and dietary diversity among indigenous subsistence farmers of Sauria Paharia tribal community of India: A mixed methods study. Frontiers in Sustainable Food Systems, 5, 667297. https://doi.org/10.3389/fsufs.2021.667297
  24. Grinsted, A., Moore, J. C., & Jevrejeva, S. (2004). Application of the cross wavelet transform and wavelet coherence to geophysical time series. Nonlinear Processes in Geophysics, 11(5/6), 561–566. https://doi.org/10.5194/npg-11-561-2004
  25. Guhathakurta, P., Menon, P., Inkane, P. M., Krishnan, U., & Sable, S. T. (2017). Trends and variability of meteorological drought over the districts of India using standardized precipitation index. Journal of Earth System Science, 126(8), 120. https://doi.org/10.1007/s12040-017-0896-x
  26. Hamed, K. H. (2011). The distribution of Kendall’s tau for testing the significance of cross-correlation in persistent data. Hydrological Sciences Journal, 56(5), 841–853. https://doi.org/10.1080/02626667.2011.586948
  27. Han, J., & Singh, V. P. (2023). A review of widely used drought indices and the challenges of drought assessment under climate change. Environmental Monitoring and Assessment, 195(12), 1438. https://doi.org/10.1007/s10661-023-12062-3
  28. Hao, Z., & Singh, V. P. (2015). Drought characterization from a multivariate perspective: A review. Journal of Hydrology, 527, 668–678. https://doi.org/10.1016/j.jhydrol.2015.05.031
  29. Hembram, T. K., & Saha, S. (2025). Spatial land degradation vulnerability assessment in a plateau fringe river basin of Jharkhand, India. Environmental Monitoring and Assessment, 197(2), 143. https://doi.org/10.1007/s10661-024-13529-7
  30. Joshi, N., Gupta, D., Suryavanshi, S., Adamowski, J., & Madramootoo, C. A. (2016). Analysis of trends and dominant periodicities in drought variables in India: A wavelet transform based approach. Atmospheric Research, 182, 200–220. https://doi.org/10.1016/j.atmosres.2016.07.030
  31. Koley, S., & Jeganathan, C. (2022). Evaluating the climatic and socio-economic influences on the agricultural drought vulnerability in Jharkhand. Environmental Monitoring and Assessment, 195(1), 8. https://doi.org/10.1007/s10661-022-10557-z
  32. Kumar, R., Jalem, K., Swain, S. K., Kanga, S., Singh, S. K., Meraj, G., Kumar, P., & Mandal, D. (2025a). Geospatial analysis of climate-induced land cover changes and vegetation trends in western Jharkhand, India. Environmental Monitoring and Assessment, 197(9), 1019. https://doi.org/10.1007/s10661-025-14452-1
  33. Kumar, R., Jalem, K., Swain, S. K., Kanga, S., Singh, S. K., Meraj, G., Kumar, P., & Mandal, D. (2025b). Spatio-temporal analysis of precipitation dynamics and groundwater recharge trends in Jharkhand, India: Implications for water resource management. Environmental Earth Sciences, 84(22), 678. https://doi.org/10.1007/s12665-025-12682-7
  34. Lehner, B., Döll, P., Alcamo, J., Henrichs, T., & Kaspar, F. (2006). Estimating the impact of global change on flood and drought risks in Europe: A continental, integrated analysis. Climatic Change, 75(3), 273–299. https://doi.org/10.1007/s10584-006-6338-4
  35. Li, R., Chen, N., Zhang, X., Zeng, L., Wang, X., Tang, S., Li, D., & Niyogi, D. (2020). Quantitative analysis of agricultural drought propagation process in the Yangtze River Basin by using cross wavelet analysis and spatial autocorrelation. Agricultural and Forest Meteorology, 280, 107809. https://doi.org/10.1016/j.agrformet.2019.107809
  36. Liu, M., Liu, P., Guo, Y., Wang, Y., Geng, X., Nie, Z., & Yu, Y. (2020). Change-point analysis of precipitation and drought extremes in China over the past 50 years. Atmosphere, 11(1), 11. https://doi.org/10.3390/atmos11010011
  37. Mallya, G., Mishra, V., Niyogi, D., Tripathi, S., & Govindaraju, R. S. (2016). Trends and variability of droughts over the Indian monsoon region. Weather and Climate Extremes, 12, 43–68. https://doi.org/10.1016/j.wace.2016.01.002
  38. Mann, H. B. (1945). Nonparametric tests against trend. Econometrica, 13(3), 245–259. https://doi.org/10.2307/1907187
  39. Massarelli, C., Campanale, C., & Uricchio, V. F. (2022). Custom methodology to improve geospatial interpolation at regional scale with open-source software. Knowledge, 2(1), 88–102. https://doi.org/10.3390/knowledge2010005
  40. McKee, T. B., Doesken, N. J., & Kleist, J. (1993, January 17–22). The relationship of drought frequency and duration to time scales [Paper presentation]. Eighth Conference on Applied Climatology, Anaheim, CA, United States. https://www.droughtmanagement.info/literature/AMS_Relationship_Drought_Frequency_Duration_Time_Scales_1993.pdf
  41. 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
  42. Mishra, V., Smoliak, B. V., Lettenmaier, D. P., & Wallace, J. M. (2012). A prominent pattern of year-to-year variability in Indian summer monsoon rainfall. Proceedings of the National Academy of Sciences, 109(19), 7213–7217. https://doi.org/10.1073/pnas.1119150109
  43. Monish, N. T., & Rehana, S. (2019). Suitability of distributions for standard precipitation and evapotranspiration index over meteorologically homogeneous zones of India. Journal of Earth System Science, 129(1), 25. https://doi.org/10.1007/s12040-019-1271-x
  44. Mukherjee, S., Mishra, A., & Trenberth, K. E. (2018). Climate change and drought: A perspective on drought indices. Current Climate Change Reports, 4(2), 145–163. https://doi.org/10.1007/s40641-018-0098-x
  45. Naumann, G., Alfieri, L., Wyser, K., Mentaschi, L., Betts, R. A., Carrao, H., Spinoni, J., Vogt, J., & Feyen, L. (2018). Global changes in drought conditions under different levels of warming. Geophysical Research Letters, 45(7), 3285–3296. https://doi.org/10.1002/2017GL076521
  46. Niranjan Kumar, K., Rajeevan, M., Pai, D. S., Srivastava, A. K., & Preethi, B. (2013). On the observed variability of monsoon droughts over India. Weather and Climate Extremes, 1, 42–50. https://doi.org/10.1016/j.wace.2013.07.006
  47. Pai, D. S., Sridhar, L., Rajeevan, M., Sreejith, O. P., Satbhai, N. S., & Mukhopadhyay, B. (2014). Development of a new high spatial resolution (0.25° × 0.25°) long period (1901-2010) daily gridded rainfall data set over India and its comparison with existing data sets over the region. MAUSAM, 65(1), 1–18. https://doi.org/10.54302/mausam.v65i1.851
  48. Pettitt, A. N. (1979). A non-parametric approach to the change-point problem. Journal of the Royal Statistical Society: Series C (Applied Statistics), 28(2), 126–135. https://doi.org/10.2307/2346729
  49. Poschlod, B., Sailer, L., Sasse, A., Vogelbacher, A., & Ludwig, R. (2026). Climate change effects on river droughts in Bavaria using a hydrological large ensemble. Hydrology and Earth System Sciences, 30(4), 1165–1188. https://doi.org/10.5194/hess-30-1165-2026
  50. Raha, S., & Deb, S. (2023). Evaluation of the drought trend alongside of change point: A study of the Purulia District in West Bengal, India. Current World Environment, 18(2), 535–558. https://doi.org/10.12944/CWE.18.2.10
  51. Raha, S., Dey, S. K., Mondal, M., & Gayen, S. K. (2022). Spatiotemporal assessment of drought intensity and trend along with change point: A study on Bankura District, West Bengal, India. In S. Mandal, R. Maiti, M. Nones, & H. R. Beckedahl (Eds.), Applied geomorphology and contemporary issues (pp. 655–666). Springer International Publishing. https://doi.org/10.1007/978-3-031-04532-5_34
  52. Raha, S., & Gayen, S. K. (2020). Simulation of meteorological drought using exponential smoothing models: A study on Bankura District, West Bengal, India. SN Applied Sciences, 2(5), 909. https://doi.org/10.1007/s42452-020-2730-3
  53. Raha, S., & Gayen, S. K. (2022). Analysis of rainfall trend along with change-point: Study on Purulia District, West Bengal, India. Journal of Earth Sciences and Technology, 3(3), 111–129.
  54. Rao, S. A., Chaudhari, H. S., Pokhrel, S., & Goswami, B. N. (2010). Unusual central Indian drought of summer monsoon 2008: Role of southern tropical Indian Ocean warming. Journal of Climate, 23(19), 5163–5174. https://doi.org/10.1175/2010JCLI3257.1
  55. Ratnam, J. V., Behera, S. K., Masumoto, Y., Takahashi, K., & Yamagata, T. (2010). Pacific Ocean origin for the 2009 Indian summer monsoon failure. Geophysical Research Letters, 37(7), L07807. https://doi.org/10.1029/2010GL042798
  56. Roy, P., Gupta, S. K., Singh, S. K., & Kanga, S. (2024). Spatial data modelling of atmospheric water availability and stress in Jharkhand, India. Discover Civil Engineering, 1(1), 93. https://doi.org/10.1007/s44290-024-00088-6
  57. Sabut, A., & Mishra, A. (2026). A century of drought research (1900–2023): Scientific developments, methodological innovations, and emerging frontiers. Water Resources Research, 62(1), e2025WR041987. https://doi.org/10.1029/2025WR041987
  58. Saharwardi, M. S., Kumar, P., Dubey, A. K., & Kumari, A. (2022). Understanding spatiotemporal variability of drought in recent decades and its drivers over identified homogeneous regions of India. Quarterly Journal of the Royal Meteorological Society, 148(747), 2955–2972. https://doi.org/10.1002/qj.4345
  59. Satish Kumar, K., AnandRaj, P., Sreelatha, K., & Sridhar, V. (2021). Regional analysis of drought severity-duration-frequency and severity-area-frequency curves in the Godavari River Basin, India. International Journal of Climatology, 41(12), 5481–5501. https://doi.org/10.1002/joc.7137
  60. Sen, P. K. (1968). Estimates of the regression coefficient based on Kendall’s tau. Journal of the American Statistical Association, 63(324), 1379–1389. https://doi.org/10.1080/01621459.1968.10480934
  61. Shahfahad, Talukdar, S., Ghose, B., Islam, A. R. M. T., Hasanuzzaman, M., Ahmed, I. A., Praveen, B., Asif, Paarcha, A., Rahman, A., Gagnon, A. S., & Afzal, M. (2024). Predicting long term regional drought pattern in Northeast India using advanced statistical technique and wavelet-machine learning approach. Modeling Earth Systems and Environment, 10(1), 1005–1026. https://doi.org/10.1007/s40808-023-01818-y
  62. Sharma, S., & Mujumdar, P. (2017). Increasing frequency and spatial extent of concurrent meteorological droughts and heatwaves in India. Scientific Reports, 7(1), 15582. https://doi.org/10.1038/s41598-017-15896-3
  63. Sheoran, R., Dumka, U. C., Tiwari, R. K., & Hooda, R. K. (2024). An improved version of the prewhitening method for trend analysis in the autocorrelated time series. Atmosphere, 15(10), 1159. https://doi.org/10.3390/atmos15101159
  64. Sikka, D. R., Tyagi, A., & Ram, L. C. (2010). Large scale fluctuations of the Continental Tropical Convergence Zone (CTCZ) during pilot CTCZ phase-2009 and the evolution of monsoon drought in 2009. MAUSAM, 61(1), 47–74. https://doi.org/10.54302/mausam.v61i1.776
  65. Slavková, J., Gera, M., Nikolova, N., & Siman, C. (2023). Standardized Precipitation and Evapotranspiration Index approach for drought assessment in Slovakia—Statistical evaluation of different calculations. Atmosphere, 14(9), 1464. https://doi.org/10.3390/atmos14091464
  66. Spinoni, J., Naumann, G., Carrao, H., Barbosa, P., & Vogt, J. (2014). World drought frequency, duration, and severity for 1951–2010. International Journal of Climatology, 34(8), 2792–2804. https://doi.org/10.1002/joc.3875
  67. Srivastava, A. K., Rajeevan, M., & Kshirsagar, S. R. (2009). Development of a high resolution daily gridded temperature data set (1969–2005) for the Indian region. Atmospheric Science Letters, 10(4), 249–254. https://doi.org/10.1002/asl.232
  68. Taylor, K. E. (2001). Summarizing multiple aspects of model performance in a single diagram. Journal of Geophysical Research: Atmospheres, 106(D7), 7183–7192. https://doi.org/10.1029/2000JD900719
  69. Then, Y. J., & Abdul Halim, S. (2025). Modified Mann-Kendall with higher-order statistics for trend analysis. Scientific Reports, 16(1), 500. https://doi.org/10.1038/s41598-025-30034-0
  70. Thornthwaite, C. W. (1948). An approach toward a rational classification of climate. Geographical Review, 38(1), 55–94. https://doi.org/10.2307/210739
  71. Tirivarombo, S., Osupile, D., & Eliasson, P. (2018). Drought monitoring and analysis: Standardised Precipitation Evapotranspiration Index (SPEI) and Standardised Precipitation Index (SPI). Physics and Chemistry of the Earth, Parts A/B/C, 106, 1–10. https://doi.org/10.1016/j.pce.2018.07.001
  72. Tirkey, A. S., Ghosh, M., Pandey, A. C., & Shekhar, S. (2018). Assessment of climate extremes and its long term spatial variability over the Jharkhand state of India. The Egyptian Journal of Remote Sensing and Space Science, 21(1), 49–63. https://doi.org/10.1016/j.ejrs.2016.12.007
  73. Torrence, C., & Compo, G. P. (1998). A practical guide to wavelet analysis. Bulletin of the American Meteorological Society, 79(1), 61–78. https://doi.org/10.1175/1520-0477(1998)079 2.0.CO;2
  74. Tsakiris, G., & Vangelis, H. (2005). Establishing a drought index incorporating evapotranspiration. European Water, (9/10), 3–11.
  75. Tu, G., Lu, Q., Zhang, F., Xia, Y., Liao, J., Hong, Q., You, X., Chen, W., Zhao, D., & Yan, B. (2026). Spatiotemporal variability and multidimensional driving mechanisms of drought-flood abrupt alternation in the Poyang Lake basin. Atmospheric Research, 336, 108887. https://doi.org/10.1016/j.atmosres.2026.108887
  76. 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
  77. Wilhite, D. A., & Glantz, M. H. (1985). Understanding the drought phenomenon: The role of definitions. Water International, 10(3), 111–120. https://doi.org/10.1080/02508068508686328
  78. Wilhite, D. A., Sivakumar, M. V. K., & Pulwarty, R. (2014). Managing drought risk in a changing climate: The role of national drought policy. Weather and Climate Extremes, 3, 4–13. https://doi.org/10.1016/j.wace.2014.01.002
  79. Wilks, D. S. (2011). Empirical distributions and exploratory data analysis. In Statistical methods in the atmospheric sciences (Vol. 100, pp. 23–70). Academic Press. https://doi.org/10.1016/B978-0-12-385022-5.00003-8
  80. Yadav, S., Yadav, R. K., & Sinha, R. K. (2023). Elucidating revival measures to extenuate expanse of fallow lands and climate change: An empirical analysis of Jharkhand. In C. B. Pande, K. N. Moharir, & A. Negm (Eds.), Climate change impacts in India (pp. 297–321). Springer International Publishing. https://doi.org/10.1007/978-3-031-42056-6_13
  81. Yagbasan, O., Demir, V., & Yazicigil, H. (2020). Trend analyses of meteorological variables and lake levels for two shallow lakes in Central Turkey. Water, 12(2), 414. https://doi.org/10.3390/w12020414
  82. Yildirim, G., Rahman, A., & Singh, V. P. (2022). Meteorological and hydrological drought hazard, frequency and propagation analysis: A case study in southeast Australia. Journal of Hydrology: Regional Studies, 44, 101229. https://doi.org/10.1016/j.ejrh.2022.101229
  83. Yue, S., & Pilon, P. (2004). A comparison of the power of the t test, Mann-Kendall and bootstrap tests for trend detection / Une comparaison de la puissance des tests t de Student, de Mann-Kendall et du bootstrap pour la détection de tendance. Hydrological Sciences Journal, 49(1), 21–37. https://doi.org/10.1623/hysj.49.1.21.53996
  84. Yue, S., Pilon, P., Phinney, B., & Cavadias, G. (2002). The influence of autocorrelation on the ability to detect trend in hydrological series. Hydrological Processes, 16(9), 1807–1829. https://doi.org/10.1002/hyp.1095
  85. Zhao, Z., Wang, Y., Chang, J., Guo, A., & Peng, Z. (2026). Quantitative assessment of risk evolution, potential vulnerability thresholds, and drought resistance in socioeconomic drought under a changing environment. Water Resources Management, 40(7), 303. https://doi.org/10.1007/s11269-026-04537-y