A study of meteorological drought via the SPI and SPEI with special reference to groundwater depth in the drought prone area of West Bengal

Sutrisha Ghosh; Nayan Roy; Rolee Kanchan

doi:10.31893/multiscience.2026069

Keywords:

Abstract

Drought is a worldwide phenomenon that affects almost all geographical regions, causing enormous harm to both the natural environment and human life. Drought frequency and intensity vary according to natural and anthropogenic factors. It has a significant impact on agriculture, hydrology, economics, ecology and human societies. Recognizing the implications for drought preparation, mitigation, and action. Moreover, identifying and mitigating drought vulnerability is essential for decision makers. Meteorological drought analysis provides a thorough examination of all meteorological factors, such as precipitation, wind speed, relative humidity, dew point, vapor pressure and evaporation. The standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI) at different timescales were used for 1998--2018 to analyze the drought characteristics. According to SPI calculations, the months of January through July are experiencing extreme drought. According to the SPEI, the extremely dry months are from March to August and from October to December. The results of the Mann‒Kendall (M-K) test and Sen's slope analysis revealed that the postmonsoon drought tendency increased more rapidly than did the other two periods. Holt-Winter’s test calculates the trend with seasonal change. It was used to anticipate the weather for the following 10 years and indicated that the drought propensity would worsen in the next years. The groundwater level is an essential indicator of water availability for humans. In the tropics, groundwater levels respond to excessive precipitation. As drought can be observed during the postmonsoon season, the postmonsoonal water level depth tends to rise gradually.
References
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10. Ding, Y., Gong, X., Xing, Z., Cai, H., Zhou, Z., Zhang, D., ... & Shi, H. (2021). Attribution of meteorological, hydrological and agricultural drought propagation in different climatic regions of China. Agricultural Water Management, 255, 106996.
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13. Getahun, Y. S., & Li, M. H. (2023). Flash drought evaluation using evaporative stress and evaporative demand drought indices: A case study from Awash River Basin (ARB), Ethiopia. Theoretical and Applied Climatology, 1–20.
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19. Kendall, M. G. (1975). Rank correlation methods (4th ed.). Charles Griffin.
20. Kotchoni, D. V., Vouillamoz, J. M., Lawson, F. M., Adjomayi, P., Boukari, M., & Taylor, R. G. (2018). Relationships between rainfall and groundwater recharge in seasonally humid Benin: A comparative analysis of long-term hydrographs in sedimentary and crystalline aquifers. Hydrogeology Journal.
21. Kumar, K. N., 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.
22. Kundu, A., Patel, N. R., Denis, D. M., & Dutta, D. (2020). An estimation of hydrometeorological drought stress over the central part of India using geo-information technology. Journal of the Indian Society of Remote Sensing, 48, 1–9.
23. Ledger, M. E., Edwards, F. K., Brown, L. E., Milner, A. M., & Woodward, G. (2011). Impact of simulated drought on ecosystem biomass production: An experimental test in stream mesocosms. Global Change Biology, 17(7), 2288–2297.
24. Li, L., She, D., Zheng, H., Lin, P., & Yang, Z. L. (2020). Elucidating diverse drought characteristics from two meteorological drought indices (SPI and SPEI) in China. Journal of Hydrometeorology, 21(7), 1513–1530.
25. Liu, Q., Zhang, S., Zhang, H., Bai, Y., & Zhang, J. (2020). Monitoring drought using composite drought indices based on remote sensing. Science of the Total Environment, 711, 134585.
26. McGuire, J. K., & Palmer, W. C. (1957). The 1957 drought in the eastern United States. Monthly Weather Review, 85, 305–315.
27. McKee, T. B., Doesken, N. J., & Kleist, J. (1995). Drought monitoring with multiple time scales. In Proceedings of the Ninth Conference on Applied Climatology (pp. 233–236). American Meteorological Society.
28. Meyer, S. J., Hubbard, K. G., & Wilhite, D. A. (1993). A crop‐specific drought index for corn: I. Model development and validation. Agronomy Journal, 85(2), 388–395.
29. Mishra, A. K., & Desai, V. R. (2005). Drought forecasting using stochastic models. Stochastic Environmental Research and Risk Assessment, 19, 326–339.
30. Mitra, M., & Acharya, T. (2015). Study of fractures in Precambrian crystalline rocks using field technique in and around Balarampur, Purulia district, West Bengal, India. Journal of Earth System Science, 124, 1801–1812.
31. Narasimhan, B., & Srinivasan, R. (2005). Development and evaluation of Soil Moisture Deficit Index (SMDI) and Evapotranspiration Deficit Index (ETDI) for agricultural drought monitoring. Agricultural and Forest Meteorology, 133(1–4), 69–88.
32. Pai, D. S., Sridhar, L., Guhathakurta, P., & Hatwar, H. R. (2011). District-wide drought climatology of the southwest monsoon season over India based on standardized precipitation index (SPI). Natural Hazards, 59(3), 1797–1813. https://doi.org/10.1007/s11069-011-9867-8
33. Palmer, W. C. (1968). Keeping track of crop moisture conditions, nationwide: The new crop moisture index.
34. Palmer, W. C. (1965). Meteorological drought (Research Paper No. 45). Weather Bureau.
35. Panday, S. C., Kumar, A., Meena, V. S., Joshi, K., Stanley, J., & Pattanayak, A. (2020). Standardized precipitation index (SPI) for drought severity assessment of Almora, Uttarakhand, India. Journal of Agrometeorology, 22(2), 203–206.
36. Parthasarathy, B., Sontakke, N. A., Monot, A. A., & Kothawale, D. R. (1987). Droughts/floods in the summer monsoon season over different meteorological subdivisions of India for the period 1871–1984. Journal of Climatology, 7(1), 57–70.
37. Pathak, A. A., & Dodamani, B. M. (2019). Trend analysis of groundwater levels and assessment of regional groundwater drought: Ghataprabha River Basin, India. Natural Resources Research, 28, 631–643.
38. Poornima, S., & Pushpalatha, M. (2019). Drought prediction based on SPI and SPEI with varying timescales using LSTM recurrent neural network. Soft Computing, 23, 8399–8412.
39. Raha, S., & Gayen, S. K. (2019). Simulation of meteorological drought of Bankura District, West Bengal: Comparative study between exponential smoothing and machine learning procedures. Journal of Geography, Environment and Earth Science International, 22(1), 1–16.
40. Rehana, S., & Monish, N. T. (2020). Characterization of regional drought over water and energy limited zones of India using potential and actual evapotranspiration. Earth and Space Science, 7(10), e2020EA001264.
41. Roy, P., Pal, S. C., Chakrabortty, R., Chowdhuri, I., Saha, A., & Shit, M. (2022). Climate change and groundwater overdraft impacts on agricultural drought in India: Vulnerability assessment, food security measures and policy recommendation. Science of The Total Environment, 849, 157850.
42. Roy, S., Hazra, S., & Chanda, A. (2023). Changing characteristics of meteorological drought and its impact on monsoon-rice production in sub-humid red and laterite zone of West Bengal, India. Theoretical and Applied Climatology, 151(3–4), 1419–1433.
43. Vicente-Serrano, S. M., López-Moreno, J. I., Beguería, S., Lorenzo-Lacruz, J., Azorin-Molina, C., & Morán-Tejeda, E. (2012). Accurate computation of a streamflow drought index. Journal of Hydrologic Engineering, 17, 318–332.
44. Sarkar, B., Mohinuddin, S., Islam, A., Islam, A. R. M. T., Saha, U. D., Sengupta, S., Pal, S. C., Chu, H., & Huang, J. (2024). SPI-gamma random forest modelling for meteorological drought characterization and prediction in the Bengal Delta, Indo-Bangladesh region. Theoretical and Applied Climatology, 156(1). https://doi.org/10.1007/s00704-024-05293-y
45. Sen, P. K. (1968). Estimates of the regression coefficient based on Kendall's tau. Journal of the American Statistical Association, 63(324), 1379–1389.
46. 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).
47. Shah, R., Bharadiya, N., & Manekar, V. (2015). Drought index computation using standardized precipitation index (SPI) method for Surat District, Gujarat. Aquatic Procedia, 4, 1243–1249.
48. Shukla, S., & Wood, A. W. (2008). Use of a standardized runoff index for characterizing hydrologic drought. Geophysical Research Letters, 35(2).
49. Shahfahad, Talukdar, S., Ghose, B., Islam, A. R. M. T., Hasanuzzaman, M., Ahmed, I. A., & Afzal, M. (2023). Predicting long term regional drought pattern in Northeast India using advanced statistical technique and wavelet-machine learning approach. Modeling Earth Systems and Environment, 1–22.
50. Shaik, D. S., Kant, Y., Mitra, D., Singh, A., Chandola, H. C., Sateesh, M., ... & Chauhan, P. (2019). Impact of biomass burning on regional aerosol optical properties: A case study over northern India. Journal of Environmental Management, 244, 328–343.
51. Singh, R. M., & Shukla, P. (2020). Drought characterization using drought indices and El Nino effects. National Academy Science Letters, 43, 339–342.
52. Todd, C. (1888). The Australasian, 1456.
53. Tsakiris, G. (2017). Drought risk assessment and management. Water Resources Management, 31, 3083–3095.
54. Tsakiris, G., & Vangelis, H. J. (2005). Establishing a drought index incorporating evapotranspiration. European Water, 9(10), 3–11.
55. Van Rooy, M. P. (1965). A rainfall anomaly index (RAI) independent of time and space. Notos, 14, 43–48.
56. Wang, J., Lin, H., Huang, J., Jiang, C., Xie, Y., & Zhou, M. (2019). Variations of drought tendency, frequency, and characteristics and their responses to climate change under CMIP5 RCP scenarios in Huai River Basin, China. Water, 11(10), 2174.
57. Zhai, J., Mondal, S. K., Fischer, T., Wang, Y., Su, B., Huang, J., ... & Uddin, M. J. (2020). Future drought characteristics through a multi-model ensemble from CMIP6 over South Asia. Atmospheric Research, 246, 105111.

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How to cite

Ghosh, S., Roy, N., & Kanchan, R. (2025). A study of meteorological drought via the SPI and SPEI with special reference to groundwater depth in the drought prone area of West Bengal. Multidisciplinary Science Journal, 8(1), 2026069. https://doi.org/10.31893/multiscience.2026069

[1] Ahmadalipour, A., Moradkhani, H., Castelletti, A., & Magliocca, N. (2019). Future drought risk in Africa: Integrating vulnerability, climate change, and population growth. Science of the Total Environment, 662, 672–686.

[2] Ahmadi, S. H., & Sedghamiz, A. (2007). Geostatistical analysis of spatial and temporal variations of groundwater level. Environmental Monitoring and Assessment, 129, 277–294.

[3] Barrios, M., Borrego, A., Vilaginés, A., Ollé, C., & Somoza, M. (2008, September 17). A bibliometric study of psychological research on tourism. Scientometrics, 77(3), 453–467. https://doi.org/10.1007/s11192-007-1952-0

[4] Bera, B., Shit, P. K., Sengupta, N., Saha, S., & Bhattacharjee, S. (2021). Trends and variability of drought in the extended part of Chhota Nagpur Plateau (Singbhum Protocontinent), India applying SPI and SPEI indices. Environmental Challenges, 5, 100310.

[5] Bhalme, H. N., & Mooley, D. A. (1980). Large-scale droughts/floods and monsoon circulation. [Journal Name]. (Note: Add journal name and volume if available).

[6] Bhunia, P., Das, P., & Maiti, R. (2019). Meteorological drought study through SPI in three drought prone districts of West Bengal, India. Earth Systems and Environment, 4(1), 43–55. https://doi.org/10.1007/s41748-019-00137-6

[7] (Duplicate of 2020 reference; keep only one)

[8] Byun, H. R., & Wilhite, D. A. (1999). Objective quantification of drought severity and duration. Journal of Climate, 12(9), 2747–2756.

[9] 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.

[10] Ding, Y., Gong, X., Xing, Z., Cai, H., Zhou, Z., Zhang, D., ... & Shi, H. (2021). Attribution of meteorological, hydrological and agricultural drought propagation in different climatic regions of China. Agricultural Water Management, 255, 106996.

[11] District Disaster Management Plan 2021–2022, Purulia. Office of the District Magistrate, Purulia Disaster Management Section.

[12] Encik, R. (2015). Holt-Winter forecasting method to predict the number of visitors to the Riau University library. Universitas Riau.

[13] Getahun, Y. S., & Li, M. H. (2023). Flash drought evaluation using evaporative stress and evaporative demand drought indices: A case study from Awash River Basin (ARB), Ethiopia. Theoretical and Applied Climatology, 1–20.

[14] Getahun, Y. S., Li, M. H., & Pun, I. F. (2021). Trend and change-point detection analyses of rainfall and temperature over the Awash River Basin of Ethiopia. Heliyon, 7(9).

[15] Getahun, Y. S., Li, M. H., Chen, Y. Y., & Yate, T. A. (2023). Drought characterization and severity analysis using GRACE-TWS and MODIS datasets: A case study from the Awash River Basin (ARB), Ethiopia. Journal of Water and Climate Change, 14(2), 516–542.

[16] Ground Water Yearbook of West Bengal & Andaman & Nicobar Islands (2021–2022). Central Ground Water Board, Government of India.

[17] Heim Jr, R. R. (2002). A review of twentieth-century drought indices used in the United States. Bulletin of the American Meteorological Society, 83(8), 1149–1166.

[18] Jha, S., Sehgal, V. K., Raghava, R., & Sinha, M. (2013). Trend of standardized precipitation index during Indian summer monsoon season in agroclimatic zones of India. Earth System Dynamics Discussions, 4(1), 429–449.

[19] Kendall, M. G. (1975). Rank correlation methods (4th ed.). Charles Griffin.

[20] Kotchoni, D. V., Vouillamoz, J. M., Lawson, F. M., Adjomayi, P., Boukari, M., & Taylor, R. G. (2018). Relationships between rainfall and groundwater recharge in seasonally humid Benin: A comparative analysis of long-term hydrographs in sedimentary and crystalline aquifers. Hydrogeology Journal.

[21] Kumar, K. N., 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.

[22] Kundu, A., Patel, N. R., Denis, D. M., & Dutta, D. (2020). An estimation of hydrometeorological drought stress over the central part of India using geo-information technology. Journal of the Indian Society of Remote Sensing, 48, 1–9.

[23] Ledger, M. E., Edwards, F. K., Brown, L. E., Milner, A. M., & Woodward, G. (2011). Impact of simulated drought on ecosystem biomass production: An experimental test in stream mesocosms. Global Change Biology, 17(7), 2288–2297.

[24] Li, L., She, D., Zheng, H., Lin, P., & Yang, Z. L. (2020). Elucidating diverse drought characteristics from two meteorological drought indices (SPI and SPEI) in China. Journal of Hydrometeorology, 21(7), 1513–1530.

[25] Liu, Q., Zhang, S., Zhang, H., Bai, Y., & Zhang, J. (2020). Monitoring drought using composite drought indices based on remote sensing. Science of the Total Environment, 711, 134585.

[26] McGuire, J. K., & Palmer, W. C. (1957). The 1957 drought in the eastern United States. Monthly Weather Review, 85, 305–315.

[27] McKee, T. B., Doesken, N. J., & Kleist, J. (1995). Drought monitoring with multiple time scales. In Proceedings of the Ninth Conference on Applied Climatology (pp. 233–236). American Meteorological Society.

[28] Meyer, S. J., Hubbard, K. G., & Wilhite, D. A. (1993). A crop‐specific drought index for corn: I. Model development and validation. Agronomy Journal, 85(2), 388–395.

[29] Mishra, A. K., & Desai, V. R. (2005). Drought forecasting using stochastic models. Stochastic Environmental Research and Risk Assessment, 19, 326–339.

[30] Mitra, M., & Acharya, T. (2015). Study of fractures in Precambrian crystalline rocks using field technique in and around Balarampur, Purulia district, West Bengal, India. Journal of Earth System Science, 124, 1801–1812.

[31] Narasimhan, B., & Srinivasan, R. (2005). Development and evaluation of Soil Moisture Deficit Index (SMDI) and Evapotranspiration Deficit Index (ETDI) for agricultural drought monitoring. Agricultural and Forest Meteorology, 133(1–4), 69–88.

[32] Pai, D. S., Sridhar, L., Guhathakurta, P., & Hatwar, H. R. (2011). District-wide drought climatology of the southwest monsoon season over India based on standardized precipitation index (SPI). Natural Hazards, 59(3), 1797–1813. https://doi.org/10.1007/s11069-011-9867-8

[33] Palmer, W. C. (1968). Keeping track of crop moisture conditions, nationwide: The new crop moisture index.

[34] Palmer, W. C. (1965). Meteorological drought (Research Paper No. 45). Weather Bureau.

[35] Panday, S. C., Kumar, A., Meena, V. S., Joshi, K., Stanley, J., & Pattanayak, A. (2020). Standardized precipitation index (SPI) for drought severity assessment of Almora, Uttarakhand, India. Journal of Agrometeorology, 22(2), 203–206.

[36] Parthasarathy, B., Sontakke, N. A., Monot, A. A., & Kothawale, D. R. (1987). Droughts/floods in the summer monsoon season over different meteorological subdivisions of India for the period 1871–1984. Journal of Climatology, 7(1), 57–70.

[37] Pathak, A. A., & Dodamani, B. M. (2019). Trend analysis of groundwater levels and assessment of regional groundwater drought: Ghataprabha River Basin, India. Natural Resources Research, 28, 631–643.

[38] Poornima, S., & Pushpalatha, M. (2019). Drought prediction based on SPI and SPEI with varying timescales using LSTM recurrent neural network. Soft Computing, 23, 8399–8412.

[39] Raha, S., & Gayen, S. K. (2019). Simulation of meteorological drought of Bankura District, West Bengal: Comparative study between exponential smoothing and machine learning procedures. Journal of Geography, Environment and Earth Science International, 22(1), 1–16.

[40] Rehana, S., & Monish, N. T. (2020). Characterization of regional drought over water and energy limited zones of India using potential and actual evapotranspiration. Earth and Space Science, 7(10), e2020EA001264.

[41] Roy, P., Pal, S. C., Chakrabortty, R., Chowdhuri, I., Saha, A., & Shit, M. (2022). Climate change and groundwater overdraft impacts on agricultural drought in India: Vulnerability assessment, food security measures and policy recommendation. Science of The Total Environment, 849, 157850.

[42] Roy, S., Hazra, S., & Chanda, A. (2023). Changing characteristics of meteorological drought and its impact on monsoon-rice production in sub-humid red and laterite zone of West Bengal, India. Theoretical and Applied Climatology, 151(3–4), 1419–1433.

[43] Vicente-Serrano, S. M., López-Moreno, J. I., Beguería, S., Lorenzo-Lacruz, J., Azorin-Molina, C., & Morán-Tejeda, E. (2012). Accurate computation of a streamflow drought index. Journal of Hydrologic Engineering, 17, 318–332.

[44] Sarkar, B., Mohinuddin, S., Islam, A., Islam, A. R. M. T., Saha, U. D., Sengupta, S., Pal, S. C., Chu, H., & Huang, J. (2024). SPI-gamma random forest modelling for meteorological drought characterization and prediction in the Bengal Delta, Indo-Bangladesh region. Theoretical and Applied Climatology, 156(1). https://doi.org/10.1007/s00704-024-05293-y

[45] Sen, P. K. (1968). Estimates of the regression coefficient based on Kendall's tau. Journal of the American Statistical Association, 63(324), 1379–1389.

[46] 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).

[47] Shah, R., Bharadiya, N., & Manekar, V. (2015). Drought index computation using standardized precipitation index (SPI) method for Surat District, Gujarat. Aquatic Procedia, 4, 1243–1249.

[48] Shukla, S., & Wood, A. W. (2008). Use of a standardized runoff index for characterizing hydrologic drought. Geophysical Research Letters, 35(2).

[49] Shahfahad, Talukdar, S., Ghose, B., Islam, A. R. M. T., Hasanuzzaman, M., Ahmed, I. A., & Afzal, M. (2023). Predicting long term regional drought pattern in Northeast India using advanced statistical technique and wavelet-machine learning approach. Modeling Earth Systems and Environment, 1–22.

[50] Shaik, D. S., Kant, Y., Mitra, D., Singh, A., Chandola, H. C., Sateesh, M., ... & Chauhan, P. (2019). Impact of biomass burning on regional aerosol optical properties: A case study over northern India. Journal of Environmental Management, 244, 328–343.

[51] Singh, R. M., & Shukla, P. (2020). Drought characterization using drought indices and El Nino effects. National Academy Science Letters, 43, 339–342.

[52] Todd, C. (1888). The Australasian, 1456.

[53] Tsakiris, G. (2017). Drought risk assessment and management. Water Resources Management, 31, 3083–3095.

[54] Tsakiris, G., & Vangelis, H. J. (2005). Establishing a drought index incorporating evapotranspiration. European Water, 9(10), 3–11.

[55] Van Rooy, M. P. (1965). A rainfall anomaly index (RAI) independent of time and space. Notos, 14, 43–48.

[56] Wang, J., Lin, H., Huang, J., Jiang, C., Xie, Y., & Zhou, M. (2019). Variations of drought tendency, frequency, and characteristics and their responses to climate change under CMIP5 RCP scenarios in Huai River Basin, China. Water, 11(10), 2174.

[57] Zhai, J., Mondal, S. K., Fischer, T., Wang, Y., Su, B., Huang, J., ... & Uddin, M. J. (2020). Future drought characteristics through a multi-model ensemble from CMIP6 over South Asia. Atmospheric Research, 246, 105111.

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