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Measuring the degree of rainfall alteration and eco-deficit/eco-surplus of rainfall using indicators of rainfall alteration approach

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Abstract

The present study explored district-wise monthly rainfall of two climatic regimes (1960–1990 and 1991–2020) of the West Bengal state of India, which receives an annual rainfall of about 1500 mm with huge seasonal rainfall variability. A simple time series analysis does not show any noticeable trend. However, decomposing rainfall parameters into several indicators may explore the change. From this perspective, the present paper intends to explore the nature, degree, and direction of rainfall alteration. The range of variability approach (RVA) considered 29 indicators was adopted for unveiling it. RVA explored that in the 1991–2017 phase, the rainfall failure rate above the upper threshold (75th percentile) is 33% which is 10% greater than in 1961–1990. Seasonal variation in failure rate was detected, but overall larger area of the state recorded failure rate (FR) above the threshold. This result leads to increasing wetness conditions. Very low to moderate (< 0.4) degree of impact of rainfall alteration (DIRA) was found in the recent period. The areal occupation of moderate DIRA was considerable in pre-monsoon, post-monsoon, and winter seasons. Time series rainfall data-based flow duration curves (FDCs) indicated the dominance of the eco-surplus rainfall state over greater parts of the state (72–84%) in all the seasons except winter (49%). Such surplus rainfall may be beneficial to the ecological and agricultural perspective of the state.

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Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

References

  • Abedin MA, Collins AE, Habiba U, Shaw R (2019) Climate change, water scarcity, and health adaptation in southwestern coastal Bangladesh. Int J Disaster Risk Sci 10(1):28–42

    Article  Google Scholar 

  • Arya S, Ahmed M, Bardhan Roy SK, Kadian MS, Quiroz R (2015). Sustainable intensification of potato in rice based system for increased productivity and income of resource poor farmers in West Bengal, India. International Journal of Tropical Agriculture.

  • Bardhan Roy SK (2007) Improving the livelihood of farmers by intensifying the rice-potato-rice system through double-transplanting of rice in West Bengal. International Potato Center, India

    Google Scholar 

  • Belmar O, Velasco J, Martinez-Capel F (2011) Hydrological classification of natural flow regimes to support environmental flow assessments in intensively regulated Mediterranean rivers, Segura River Basin (Spain). Environ Manage 47(5):992

    Article  PubMed  ADS  Google Scholar 

  • Birara H, Pandey RP, Mishra SK (2018) Trend and variability analysis of rainfall and temperature in the Tana basin region, Ethiopia. J Water Clim Change 9(3):555–569

    Article  Google Scholar 

  • Bond NR, Lake PS, Arthington AH (2008) The impacts of drought on freshwater ecosystems: an Australian perspective. Hydrobiologia 600(1):3–16

    Article  Google Scholar 

  • Bond NR, Burrows RM, Kennard MJ, Bunn SE (2019) Water scarcity as a driver of multiple stressor effects. Multiple stressors in river ecosystems. Elsevier, pp 111–129

    Chapter  Google Scholar 

  • Chakraborty S, Pandey RP, Chaube UC, Mishra SK (2013) Trend and variability analysis of rainfall series at Seonath River Basin, Chhattisgarh (India). Int J Appl Sci Eng Res 2(4):425–434

    Google Scholar 

  • Chattopadhyay S, Chattopadhyay M (2007). A Soft computing technique in rainfall forecasting. ar**v preprint nlin/0703042

  • Climate of west bengal (2008). Issued by national climate centre, office of the additional director general of meteorology (research), Controller of Publications Government of India. https://imdpune.gov.in/library/public/Climate%20of%20WestBengal.pdf

  • Cui L, Wang L, Lai Z, Tian Q, Liu W, Li J (2017) Innovative trend analysis of annual and seasonal air temperature and rainfall in the Yangtze River Basin, China during 1960–2015. J Atmos Solar Terr Phys 164:48–59

    Article  ADS  Google Scholar 

  • Darji MP, Dabhi VK, Prajapati HB (2015). Rainfall forecasting using neural network: A survey. In: 2015 International conference on advances in computer engineering and applications. IEEE, 706–713

  • de Castro-Pardo M, Fernández Martínez P, Pérez Zabaleta A, Azevedo JC (2021) Dealing with water conflicts: a comprehensive review of MCDM approaches to manage freshwater ecosystem services. Land 10(5):469

    Article  Google Scholar 

  • Directorate of Economics and Statistics (2002) Agricultural statistics at a glance. Directorate of Economics and statistics, Department of Agriculture and Cooperation, Ministry of Agriculture, Government of India: New Delhi.

  • Dyer F, ElSawah S, Croke B, Griffiths R, Harrison E, Lucena-Moya P, Jakeman A (2014) The effects of climate change on ecologically-relevant flow regime and water quality attributes. Stoch Env Res Risk Assess 28(1):67–82

    Article  Google Scholar 

  • Eshetu G, Johansson T, Garedew W (2016) Rainfall trend and variability analysis in Setema-Gatira area of Jimma, Southwestern Ethiopia. Afr J Agric Res 11(32):3037–3045

    Article  Google Scholar 

  • Gao Y, Vogel RM, Kroll CN, Poff NL, Olden JD (2009) Development of representative indicators of hydrologic alteration. J Hydrol 374(1–2):136–147

    Article  Google Scholar 

  • Gatto M, Petsakos A, Hareau G (2020) Sustainable intensification of rice-based systems with potato in Eastern Indo-Gangetic plains. Am J Potato Res 97(2):162–174

    Article  Google Scholar 

  • Geetha G, Selvaraj RS (2011). Prediction of monthly rainfall in Chennai using back propagation neural network model. International Journal of Engineering Science and Technology, 3(1).

  • Gholami V, Darvari Z, Mohseni Saravi M (2015) Artificial neural network technique for rainfall temporal distribu-tion simulation (case study: Kechik region). Casp J Environ Sci 13(1):53–60

    Google Scholar 

  • Ghosh KG (2018) Analysis of rainfall trends and its spatial patterns during the last century over the Gangetic West Bengal, Eastern India. J Geovisualization Sp Anal 2(2):1–18

    Google Scholar 

  • Greenville AC, Wardle GM, Dickman CR (2012) Extreme climatic events drive mammal irruptions: regression analysis of 100-year trends in desert rainfall and temperature. Ecol Evol 2(11):2645–2658

    Article  PubMed  PubMed Central  Google Scholar 

  • Guhathakurta P (2008) Long lead monsoon rainfall prediction for meteorological sub-divisions of India using deterministic artificial neural network model. Meteorol Atmos Phys 101(1):93–108

    Article  ADS  Google Scholar 

  • Guhathakurta P, Rajeevan M (2006) Trends in the rainfall pattern over India. NCC Res. Rep 2:1–23

    Google Scholar 

  • Guhathakurta P, Rajeevan M (2008) Trends in the rainfall pattern over India. Int J Climatol: J R Meteorol Soc 28(11):1453–1469

    Article  Google Scholar 

  • Jena P, Azad S, Rajeevan MN (2016) CMIP5 projected changes in the annual cycle of Indian monsoon rainfall. Climate 4(1):14

    Article  Google Scholar 

  • Jowett IG (1997) Instream flow methods: a comparison of approaches. Regul Rivers: Res Manag: Int J Devot River Res Manag 13(2):115–127

    Article  Google Scholar 

  • Khatun R, Talukdar S, Pal S, Kundu S (2021) Measuring dam induced alteration in water richness and eco-hydrological deficit in flood plain wetland. J Environ Manag 285:112157

    Article  Google Scholar 

  • Krishnakumar KN, Prasad Rao GSLHV (2008) Trends and variability in northeast monsoon rainfall over Kerala. J Agromet 10(2):123–126

    Article  Google Scholar 

  • Kumar KK, Kumar KR, Pant GB (1997) Pre-monsoon maximum and minimum temperatures over India in relation to the summer monsoon rainfall. Int J Climatol: J R Meteorol Soc 17(10):1115–1127

    Article  Google Scholar 

  • Kumar V, Jain SK, Singh Y (2010) Analysis of long-term rainfall trends in India. Hydrol Sci J-J Des Sci Hydrol 55(4):484–496

    Article  Google Scholar 

  • Kundu S, Pal S, Talukdar S, Mandal I (2021) Impact of wetland fragmentation due to damming on the linkages between water richness and ecosystem services. Environ Sci Pollu Res 1–20:50266–50285

    Article  Google Scholar 

  • Lawin AE, Manirakiza C, Lamboni B (2019) Trends and changes detection in rainfall, temperature and wind speed in Burundi. J Water Clim Change 10(4):852–870

    Article  Google Scholar 

  • Mahato S, Pal S, Talukdar S, Saha TK, Mandal P (2021) Field based index of flood vulnerability (IFV): a new validation technique for flood susceptible models. Geosci Front 12(5):101175

    Article  Google Scholar 

  • Narayanan P, Basistha A, Sarkar S, Kamna S (2013) Trend analysis and ARIMA modelling of pre-monsoon rainfall data for western India. CR Geosci 345(1):22–27

    Article  Google Scholar 

  • Niyogi D, Kishtawal C, Tripathi S, Govindaraju RS (2010) Observational evidence that agricultural intensification and land use change may be reducing the Indian summer monsoon rainfall. Water Res Res. https://doi.org/10.1029/2008WR007082

    Article  Google Scholar 

  • Ormerod SJ, Dobson M, Hildrew AG, Townsend C (2010) Multiple stressors in freshwater ecosystems. Freshwater Biol 55:1–4

    Article  Google Scholar 

  • Padilla FM, Mommer L, de Caluwe H, Smit-Tiekstra AE, Visser EJ, de Kroon H (2019) Effects of extreme rainfall events are independent of plant species richness in an experimental grassland community. Oecologia 191(1):177–190

    Article  PubMed  PubMed Central  ADS  Google Scholar 

  • Pal S, Debanshi S (2021) Machine learning models for wetland habitat vulnerability in mature Ganges delta. Environ Sci Pollut Res 28(15):19121–19146

    Article  Google Scholar 

  • Pal S, Sarda R (2020) Damming effects on the degree of hydrological alteration and stability of wetland in lower Atreyee River basin. Ecol Ind 116:106542

    Article  Google Scholar 

  • Parthasarathy B, Munot AA, Kothawale DR (1994) All-India monthly and seasonal rainfall series: 1871–1993. Theoret Appl Climatol 49(4):217–224

    Article  ADS  Google Scholar 

  • Pascale S, Lucarini V, Feng X, Porporato A, Hasson SU (2015) Analysis of rainfall seasonality from observations and climate models. Clim Dyn 44(11–12):3281–3301

    Article  Google Scholar 

  • Patakamuri SK, Muthiah K, Sridhar V (2020) Long-term homogeneity, trend, and change-point analysis of rainfall in the arid district of ananthapuramu, Andhra Pradesh State. India Water 12(1):211

    Article  Google Scholar 

  • Paul S, Pal S (2020) Exploring wetland transformations in moribund deltaic parts of India. Geocarto Int 35(16):1873–1894

    Article  ADS  Google Scholar 

  • Peñas FJ, Barquín J (2019) Assessment of large-scale patterns of hydrological alteration caused by dams. J Hydrol 572:706–718

    Article  Google Scholar 

  • Philip NS, Joseph KB (2003) A neural network tool for analyzing trends in rainfall. Comput Geosci 29(2):215–223

    Article  ADS  Google Scholar 

  • **ale SM, Khare D, Jat MK, Adamowski J (2014) Spatial and temporal trends of mean and extreme rainfall and temperature for the 33 urban centers of the arid and semi-arid state of Rajasthan, India. Atmos Res 138:73–90

    Article  Google Scholar 

  • Planning Commission (2012) Twelfth Five Year Plan (2012–2017). Faster, more inclusive and sustainable growth. Vol. I

  • Poff NL, Richter BD, Arthington AH, Bunn SE, Naiman RJ, Kendy EW, A (2010) The ecological limits of hydrologic alteration (ELOHA): a new framework for develo** regional environmental flow standards. Freshwater Biol 55(1):147–170

    Article  Google Scholar 

  • Praveen B, Talukdar S, Mahato S, Mondal J, Sharma P, Islam ARMT, Rahman A (2020) Analyzing trend and forecasting of rainfall changes in India using non-parametrical and machine learning approaches. Sci Rep 10(1):1–21

    Article  Google Scholar 

  • Radhakrishnan K, Sivaraman I, Jena SK, Sarkar S, Adhikari S (2017) A climate trend analysis of temperature and rainfall in India. Clim Change Environ Sustain 5(2):146–153

    Article  Google Scholar 

  • RAMANA GV (2014). Regression analysis of rainfall and runoff process of a typical watershed. International Journal. 3(1)

  • Rangarajan S, Thattai D, Yellasiri SRR, Vytla R, Tedla N, Mandalemula B (2018) Detecting changes in annual and seasonal rainfall patterns for Chennai. India J Hydrol Eng 23(4):05018001

    Article  Google Scholar 

  • Richter B, Baumgartner J, Wigington R, Braun D (1997) How much water does a river need? Freshw Biol 37(1):231–249

    Article  Google Scholar 

  • Saha TK, Pal S (2019) Exploring physical wetland vulnerability of Atreyee river basin in India and Bangladesh using logistic regression and fuzzy logic approaches. Ecol Ind 98:251–265

    Article  Google Scholar 

  • Saha A, Pal SC, Arabameri A, Blaschke T, Panahi S, Chowdhuri I, Arora A (2021) Flood susceptibility assessment using novel ensemble of hyperpipes and support vector regression algorithms. Water 13(2):241

    Article  Google Scholar 

  • Sarda R, Pal S (2022) Evaluating damming effect on eco-hydrological alteration in river and wetland using indicators of hydrological alteration. Geo Int 1–25:16132–16156

    Google Scholar 

  • Sathyan AR, Funk C, Aenis T, Breuer L (2018) Climate vulnerability in rainfed farming: analysis from Indian watersheds. Sustainability 10(9):3357

    Article  Google Scholar 

  • Sharma S, Singh PK (2017) Long term spatiotemporal variability in rainfall trends over the state of Jharkhand. India Clim 5(1):18

    Google Scholar 

  • Stagl J, Mayr E, Koch H, Hattermann FF, Huang S (2014) Effects of climate change on the hydrological cycle in central and eastern Europe. Managing protected areas in central and eastern Europe under climate change. Springer, Dordrecht, pp 31–43

    Chapter  Google Scholar 

  • Talukdar S, Pal S, Chakraborty A, Mahato S (2020) Damming effects on trophic and habitat state of riparian wetlands and their spatial relationship. Ecol Ind 118:106757

    Article  Google Scholar 

  • Taxak AK, Murumkar AR, Arya DS (2014) Long term spatial and temporal rainfall trends and homogeneity analysis in Wainganga basin, central India. Weather Clim Extremes 4:50–61

    Article  Google Scholar 

  • Thomas J, Prasannakumar V (2016) Temporal analysis of rainfall (1871–2012) and drought characteristics over a tropical monsoon-dominated state (Kerala) of India. J Hydrol 534:266–280

    Article  Google Scholar 

  • Uddin K, Matin MA (2021) Potential flood hazard zonation and flood shelter suitability map** for disaster risk mitigation in Bangladesh using geospatial technology. Prog Disaster Sci 11:100185

    Article  Google Scholar 

  • USDA (1994) Major world crop areas and climatic profiles. Joint Agricultural Weather Facility, USDA, Washington

    Google Scholar 

  • Vogel RM, Fennessey NM (1995) Flow duration curves II: a review of applications in water resources planning 1. JAWRA J Am Water Res Assoc 31(6):1029–1039

    Article  ADS  Google Scholar 

  • Vogel RM, Sieber J, Archfield SA, Smith MP, Apse CD, Huber-Lee A (2007) Relations among storage, yield, and instream flow. Water Res Res. https://doi.org/10.1029/2006WR005226

    Article  Google Scholar 

  • Wu CL, Chau KW, Fan C (2010) Prediction of rainfall time series using modular artificial neural networks coupled with data-preprocessing techniques. J Hydrol 389(1–2):146–167

    Article  Google Scholar 

  • Xue L, Zhang H, Yang C, Zhang L, Sun C (2017) Quantitative assessment of hydrological alteration caused by irrigation projects in the Tarim river basin. China Sci Rep 7(1):1–13

    ADS  Google Scholar 

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Acknowledgements

The authors would like to convey their gratitude to the Indian Meteorological Department (IMD) for delivering rainfall data. This study received no funding.

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No funding was received for the study to be conducted.

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RK was involved in methodology, software, formal analysis, visualization, data curation, writing—original draft preparation, and writing—reviewing and editing. SD was responsible for conceptualization, methodology, software, formal analysis, visualization, data curation, writing—original draft preparation, and writing—reviewing and editing. SP contributed to conceptualization, methodology, writing—original draft, investigation, writing—reviewing and editing, and supervision. RS assisted with software, and reviewing and editing. All authors reviewed the manuscript.

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Correspondence to Rajesh Sarda.

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Edited by Prof. Ewa Bednorz (ASSOCIATE EDITOR) / Prof. Theodore Karacostas (CO-EDITOR-IN-CHIEF).

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Pal, S., Khatun, R., Debanshi, S. et al. Measuring the degree of rainfall alteration and eco-deficit/eco-surplus of rainfall using indicators of rainfall alteration approach. Acta Geophys. (2024). https://doi.org/10.1007/s11600-024-01288-5

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