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Linking river flow modification with wetland hydrological instability, habitat condition, and ecological responses

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Abstract

Flow modification pursuing dams is widely found. Some works also focused on its impact on floodplain wetland hydrology. However, how this change can pose an impact on habitat conditions, ecological conditions, and trophic state is also a matter of investigation. The very least attention has been paid to this so far. Therefore, the present study focused on these, taking the dam-induced Lower Tangon river basin of India and Bangladesh as a case. The degree of flow alteration in the river was presented in a heat map. Multi-parametric machine learning (ML) approaches were applied to model hydrological instability and habitat condition. The ecological consequences like evaluating eco-deficit using flow duration curve (FDC) approach, trophic state using trophic state index (TSI), fish habitat zone using image-based hydrological parameters, etc. were measured. The study exhibited that after damming, the degree of river flow modification was about 41%. Consequently, the wetland hydrological instability and habitat conditions were degraded. In the post-dam period, > 50% of wetland area was lost, and hydrological instability was enhanced considerably over wider parts of the wetland. Habitat conditions of the existing wetland also witnessed fragility (poor and very poor areas increased by about 22.23 and 9.34%). As a result of this, adverse ecological responses were found. For instance, the eco-deficit area was increased by 36.19%, a good proportion (100%) of wetlands was witnessed the transformation of TSI from oligotrophic to mesotrophic state, and optimum fish habitat area was declined. The ecological strength map integrating all the cause-effect model parameters showed that good ecological strength was reduced from 49 to 2% in the post-dam. The result of the study would be very useful for wetland restoration for ecological and human well-being.

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Data availability

All the data and materials related to the manuscript are published with the paper and available from the corresponding author upon request (pankajsingha2014@gmail.com).

Material availability

All the data and materials related to the manuscript are published with the paper and available from the corresponding author upon request (pankajsingha2014@gmail.com).

References

  • Adel MM (2001) Effect on water resources from upstream water diversion in the Ganges basin. J Environ Qual 30(2):356–368

    Article  CAS  Google Scholar 

  • Adeyeye K, Gibberd J, Chakwizira J (2020) Water marginality in rural and peri-urban communities. J Clean Prod 273:122594

    Article  Google Scholar 

  • Adugna T, Xu W, Fan J (2022) Comparison of random forest and support vector machine classifiers for regional land cover mapping using coarse resolution FY-3C images. Remote Sens 14(3):574

    Article  Google Scholar 

  • Ali R, Kuriqi A, Abubaker S, Kisi O (2019) Hydrologic alteration at the upper and middle part of the Yangtze river, China: towards sustainable water resource management under increasing water exploitation. Sustainability 11(19):5176

    Article  Google Scholar 

  • Allawi MF, Jaafar O, Hamzah FM, Koting SB, Mohd NSB, El-Shafie A (2019) Forecasting hydrological parameters for reservoir system utilizing artificial intelligent models and exploring their influence on operation performance. Knowl-Based Syst 163:907–926

    Article  Google Scholar 

  • Amenuvor M, Gao W, Li D, Shao D (2020) Effects of dam regulation on the hydrological alteration and morphological evolution of the Volta River Delta. Water 12(3):646

    Article  Google Scholar 

  • Arantes CC, Fitzgerald DB, Hoeinghaus DJ, Winemiller KO (2019) Impacts of hydroelectric dams on fishes and fisheries in tropical rivers through the lens of functional traits. Curr Opin Environ Sustain 37:28–40

    Article  Google Scholar 

  • Atta-Darkwa T, Asomaning SK, Agbeshie AA, Danso EO, Akolgo GA, Amankwah E, Owusu PA (2020) Assessment of physicochemical properties of Besease wetland soils, Ghana. Afr J Agric Res 15(4):509–523

    Article  Google Scholar 

  • Bera B, Bhattacharjee S, Shit PK, Sengupta N, Saha S (2021) Anthropogenic stress on a Ramsar site, India: study towards rapid transformation of the health of aquatic environment. Environ Challenges 4:100158

    Article  CAS  Google Scholar 

  • Bertassello LE, Jawitz JW, Aubeneau AF, Botter G, Rao PSC (2019) Stochastic dynamics of wetlandscapes: ecohydrological implications of shifts in hydro-climatic forcing and landscape configuration. Sci Total Environ 694:133765

    Article  CAS  Google Scholar 

  • Bian Y, Zhao Y, Lyu H, Guo F, Li Y, Xu J, ... & Ni S (2021). Nineteen years of trophic state variation in large lakes of the Yangtze River Delta Region derived from MODIS images. Remote Sens 13(21), 4322

  • Borro M, Morandeira N, Salvia M, Minotti P, Perna P, Kandus P (2014) Mapping shallow lakes in a large South American floodplain: a frequency approach on multitemporal Landsat TM/ETM data. J Hydrol 512:39–52

    Article  Google Scholar 

  • Carlson RE (1977) A trophic state index for lakes 1. Limnol Oceanogr 22(2):361–369

    Article  CAS  Google Scholar 

  • Chakraborty R, Talukdar S, Basu T, Pal S (2018) Habitat identity crisis caused by the riparian wetland squeeze in Tangon River Basin, Barind Region, India. Spat Inf Res 26(5):507–516

    Article  Google Scholar 

  • Chattaraj, D., Paul, B., & Sarkar, S. (2021). Integrated multi-parametric analytic hierarchy process (AHP) and geographic information system (GIS) based spatial modelling for flood and water logging susceptibility mapping: a case study of English Bazar Municipality of Malda, West Bengal, India. Nat Hazards Earth Syst Sci Discuss, 1–20

  • Chen J, Deng S, Jia W, Li X, Chang J (2021) Removal of multiple heavy metals from mining-impacted water by biochar-filled constructed wetlands: adsorption and biotic removal routes. Bioresour Technol 331:125061

  • Cheng L, Ramchandran S, Vatanen T, Lietzén N, Lahesmaa R, Vehtari A, Lähdesmäki H (2019) An additive Gaussian process regression model for interpretable non-parametric analysis of longitudinal data. Nat Commun 10(1):1–11

    Article  Google Scholar 

  • Chicco D, Jurman G (2020) The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genomics 21(1):1–13

    Article  Google Scholar 

  • Chicco D, Warrens MJ, Jurman G (2021) The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ Comp Sci 7:e623

    Article  Google Scholar 

  • Choubin B, Moradi E, Golshan M, Adamowski J, Sajedi-Hosseini F, Mosavi A (2019) An ensemble prediction of flood susceptibility using multivariate discriminant analysis, classification and regression trees, and support vector machines. Sci Total Environ 651:2087–2096

    Article  CAS  Google Scholar 

  • Choudhury M, Sharma A, Singh P, Kumar D (2021) Impact of climate change on wetlands, concerning Son Beel, the largest wetland of North East, India. In: In Global Climate Change. Elsevier, Amsterdam, pp 393–414

    Chapter  Google Scholar 

  • Colvin SA, Sullivan SMP, Shirey PD, Colvin RW, Winemiller KO, Hughes RM, ... & Eby L (2019). Headwater streams and wetlands are critical for sustaining fish, fisheries, and ecosystem services. Fisheries, 44(2), 73-91

  • Cordão MJDS, Rufino IAA, Barros Ramalho Alves P, Barros Filho MNM (2020) Water shortage risk mapping: a GIS-MCDA approach for a medium-sized city in the Brazilian semi-arid region. Urban Water J 17(7):642–655

    Article  Google Scholar 

  • CWC (Central Water Commission) (2016) National register of large dams, 2016. http://www.cwc.nic.in/main/downloads/New%20NRLD.pdf. (Accessed 16 August 2017)

  • Dar SA, Bhat SU, Rashid I (2021) Landscape transformations, morphometry, and trophic status of Anchar Wetland in Kashmir Himalaya: implications for urban wetland management. Water Air Soil Pollut 232(11):1–19

    Article  Google Scholar 

  • Das Sarkar S, Sarkar UK, Lianthuamluaia L, Ghosh BD, Roy K, Mishal P, Das BK (2020) Pattern of the state of eutrophication in the floodplain wetlands of eastern India in context of climate change: a comparative evaluation of 27 wetlands. Environ Monit Assess 192(3):1–12

    Article  Google Scholar 

  • Debanshi S, Pal S (2020) Wetland delineation simulation and prediction in deltaic landscape. Ecol Indic 108:105757

    Article  Google Scholar 

  • do Vasco AN, Netto ADOA, da Silva MG (2019) The influence of dams on ecohydrological conditions in the São Francisco River Basin,Brazil. Ecohydrol Hydrobiol 19(4):556–565

    Article  Google Scholar 

  • Ely P, Fantin-Cruz I, Tritico HM, Girard P, Kaplan D (2020) Dam-induced hydrologic alterations in the rivers feeding the Pantanal. Front Environ Sci 8:579031

    Article  Google Scholar 

  • Feng D (2012) A new method for fast information extraction of water bodies using remotely sensed data. Remote Sens Technol Appl 24(2):167–171

    Google Scholar 

  • Feyisa GL, Meilby H, Fensholt R, Proud SR (2014) Automated water extraction index: a new technique for surface water mapping using Landsat imagery. Remote Sens Environ 140:23–35

    Article  Google Scholar 

  • Gao BC (1996) NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens Environ 58(3):257–266

    Article  Google Scholar 

  • George R, McManamay R, Perry D, Sabo J, Ruddell BL (2021) Indicators of hydro-ecological alteration for the rivers of the United States. Ecol Indic 120:106908

    Article  Google Scholar 

  • Ghosh S, Das A (2020) Wetland conversion risk assessment of East Kolkata Wetland: a Ramsar site using random forest and support vector machine model. J Clean Prod 275:123475

    Article  Google Scholar 

  • Ghosh S, Sivasankar T, Anand G (2021) Performance evaluation of multi-parametric synthetic aperture radar data for geological lineament extraction. Int J Remote Sens 42(7):2574–2593

    Article  Google Scholar 

  • Guo H, Hu Q, Zhang Q, Feng S (2012) Effects of the three gorges dam on Yangtze River flow and river interaction with Poyang Lake, China: 2003–2008. J Hydrol 416:19–27

    Article  Google Scholar 

  • Hastie T, Tibshirani R (1995) Generalized additive models for medical research. Stat Methods Med Res 4(3):187–196

    Article  CAS  Google Scholar 

  • He L, Zhu T, Wu Y, Li W, Zhang H, Zhang X et al (2019) Littoral slope, water depth and alternative response strategies to light attenuation shape the distribution of submerged macrophytes in a mesotrophic lake. Front Plant Sci 10:169

    Article  Google Scholar 

  • Hecht JS, Lacombe G, Arias ME, Dang TD, Piman T (2019) Hydropower dams of the Mekong River basin: a review of their hydrological impacts. J Hydrol 568:285–300

    Article  Google Scholar 

  • Hecht JS, Barth NA, Ryberg KR, Gregory AE (2021) Simulation experiments comparing nonstationary design-flood adjustments based on observed annual peak flows in the conterminous United States. J Hydrol X 100115

  • Hesamian G, Akbari MG (2020) A fuzzy additive regression model with exact predictors and fuzzy responses. Appl Soft Comput 95:106507

    Article  Google Scholar 

  • Hill J, Linero A, Murray J (2020) Bayesian additive regression trees: a review and look forward. Ann Rev Stat Appl 7(1):251–278

    Article  Google Scholar 

  • Ho TK (1998) The random subspace method for constructing decision forests. IEEE Trans Pattern Anal Mach Intell 20(8):832–844

    Article  Google Scholar 

  • Hosen MHA, Sarker K, Chhanda MS, Gupta N (2019) Effects of water depth on growth performance of Indian major carps at a poly culture system in Bangladesh. Int J Aquac Fish Sci 5(3):014–021

    Article  Google Scholar 

  • Islam ARM, Talukdar S, Mahato S, Ziaul S, Eibek KU, Akhter S, ... & Linh NTT (2021). Machine learning algorithm-based risk assessment of riparian wetlands in Padma River Basin of Northwest Bangladesh. Environ Sci Pollut Res 28(26), 34450-34471

  • Jafary F, Bradley C (2018) Groundwater irrigation management and the existing challenges from the farmers’ perspective in central Iran. Land 7(1):15

    Article  Google Scholar 

  • Jain G, Mittal D, Thakur D, Mittal MK (2020) A deep learning approach to detect Covid-19 coronavirus with X-ray images. Biocybern Biomed Eng 40(4):1391–1405

    Article  Google Scholar 

  • Kakade A, Salama ES, Han H, Zheng Y, Kulshrestha S, Jalalah M et al (2021) World eutrophic pollution of lake and river: biotreatment potential and future perspectives. Environ Technol Innov 23:101604

    Article  CAS  Google Scholar 

  • Kaplan G, Avdan ZY, Avdan U (2019) Mapping and monitoring wetland dynamics using thermal, optical, and SAR remote sensing data. Wetlands management: Assessing risk and sustainable solutions, Wetlands, p 87

    Google Scholar 

  • Khan I, Zhao M (2019) Water resource management and public preferences for water ecosystem services: a choice experiment approach for inland river basin management. Sci Total Environ 646:821–831

    Article  CAS  Google Scholar 

  • Khatun R, Pal S (2021) Effects of hydrological modification on fish habitability in riparian flood plain river basin. Ecol Inform 65:101398

    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 

  • Kim JY, Atique U, Mamun M, An KG (2021) Long-term interannual and seasonal links between the nutrient regime, sestonic chlorophyll and dominant bluegreen algae under the varying intensity of monsoon precipitation in a drinking water reservoir. Int J Environ Res Public Health 18(6):2871

    Article  CAS  Google Scholar 

  • Kumari S, Lianthuamluaia L, Sarkar UK, Puthiyottil M, Karnatak G, Meena DK et al (2022) Environmental characterization of two ecologically distinct Gangetic oxbow lakes using zooplankton taxonomic indices through comparative approach for wetland monitoring

  • Kuncheva LI, Rodríguez JJ, Plumpton CO, Linden DE, Johnston SJ (2010) Random subspace ensembles for fMRI classification. IEEE Trans Med Imaging 29(2):531–542

    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 Pollut Res 28(36):50266–50285

    Article  Google Scholar 

  • Kundu S, Pal S, Talukdar S, Mahato S, Singha P (2022) Integration of satellite image–derived temperature and water depth for assessing fish habitability in dam controlled flood plain wetland. Environ Sci Pollut Res 29(19):28083–28097

    Article  Google Scholar 

  • Larsen A, Larsen JR, Lane SN (2021) Dam builders and their works: beaver influences on the structure and function of river corridor hydrology, geomorphology, biogeochemistry and ecosystems. Earth Sci Rev 218:103623

    Article  CAS  Google Scholar 

  • Lee Z, Carder KL, Mobley CD, Steward RG, Patch JS (1999) Hyperspectral remote sensing for shallow waters: 2. Deriving bottom depths and water properties by optimization. Appl Opt 38(18):3831–3843

    Article  CAS  Google Scholar 

  • Li C, Wu Y, Gao B, Zheng K, Wu Y, Li C (2021) Multi-scenario simulation of ecosystem service value for optimization of land use in the Sichuan-Yunnan ecological barrier, China. Ecol Indic 132:108328

    Article  Google Scholar 

  • Liu Y, Chen H, Zhang L, Wu X, Wang XJ (2020) Energy consumption prediction and diagnosis of public buildings based on support vector machine learning: a case study in China. J Clean Prod 272:122542

    Article  Google Scholar 

  • Lopes OF, Rocha FA, de Sousa LF, da Silva DML, Amorim AF, Gomes RL, ... & de Jesus RM (2019). Influence of land use on trophic state indexes in northeast Brazilian river basins. Environ Monit Assess 191(2):1-14

  • McFeeters SK (1996) The use of the normalized difference water index (NDWI) in the delineation of open water features. Int J Remote Sens 17(7):1425–1432

    Article  Google Scholar 

  • Medinas de Campos M, Tritico HM, Girard P, Zeilhofer P, Hamilton SK, Fantin-Cruz I (2020) Predicted impacts of proposed hydroelectric facilities on fish migration routes upstream from the Pantanal wetland (Brazil). River Res Appl 36(3):452–464

    Article  Google Scholar 

  • Meng F, Li Z, Li L, Lu F, Liu Y, Lu X, Fan Y (2020) Phytoplankton alpha diversity indices response the trophic state variation in hydrologically connected aquatic habitats in the Harbin Section of the Songhua River. Sci Rep 10(1):1–13

    Article  Google Scholar 

  • Mezger G, del Tánago MG, De Stefano L (2021) Environmental flows and the mitigation of hydrological alteration downstream from dams: the Spanish case. J Hydrol 598:125732

    Article  Google Scholar 

  • Momblanch A, Papadimitriou L, Jain SK, Kulkarni A, Ojha CS, Adeloye AJ, Holman IP (2019) Untangling the water-food-energy-environment nexus for global change adaptation in a complex Himalayan water resource system. Sci Total Environ 655:35–47

    Article  CAS  Google Scholar 

  • Mondal D, Pal S (2018) Monitoring dual-season hydrological dynamics of seasonally flooded wetlands in the lower reach of Mayurakshi River, Eastern India. Geocarto Int 33(3):225–239

    Article  Google Scholar 

  • Moniruzzaman M, Siddik MA (n.d.) PRoject, Thakurgaon

  • Mukherjee K, Pal S (2021) Hydrological and landscape dynamics of floodplain wetlands of the Diara region, Eastern India. Ecol Indic 121:106961

    Article  Google Scholar 

  • Naghibi SA, Dolatkordestani M, Rezaei A, Amouzegari P, Heravi MT, Kalantar B, Pradhan B (2019) Application of rotation forest with decision trees as base classifier and a novel ensemble model in spatial modeling of groundwater potential. Environ Monit Assess 191(4):1–20

    Article  Google Scholar 

  • Naghibi SA, Vafakhah M, Hashemi H, Pradhan B, Alavi SJ (2020) Water resources management through flood spreading project suitability mapping using frequency ratio, k-nearest neighbours, and random forest algorithms. Nat Resour Res 29(3):1915–1933

    Article  Google Scholar 

  • Nielsen DL, Merrin LE, Pollino CA, Karim F, Stratford D, O'Sullivan J (2020) Climate change and dam development: effects on wetland connectivity and ecological habitat in tropical wetlands. Ecohydrology 13(6):e2228

    Article  Google Scholar 

  • Noori R, Ansari E, Jeong YW, Aradpour S, Maghrebi M, Hosseinzadeh M, Bateni SM (2021) Hyper-nutrient enrichment status evaluation based on the trophic state index: a case study of the Sabalan Lake, Iran. Water 2021(13):2874

    Article  Google Scholar 

  • Pal S (2016) Impact of water diversion on hydrological regime of the Atreyee river of Indo-Bangladesh. Int J River Basin Manag 14(4):459–475

    Article  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, Khatun R (2022) Image driven hydrological components-based fish habitability modeling in Riparian wetlands triggered by damming. Wetlands 42(1):1–13

    Article  Google Scholar 

  • Pal S, Paul S (2020) Assessing wetland habitat vulnerability in moribund Ganges delta using bivariate models and machine learning algorithms. Ecol Indic 119:106866

    Article  Google Scholar 

  • Pal S, Paul S (2021) Stability consistency and trend mapping of seasonally inundated wetlands in Moribund deltaic part of India. Environ Dev Sustain 23(9):12925–12953

    Article  Google Scholar 

  • Pal S, Saha TK (2018) Identifying dam-induced wetland changes using an inundation frequency approach: the case of the Atreyee River basin of Indo-Bangladesh. Ecohydrol Hydrobiol 18(1):66–81

    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 Indic 116:106542

    Article  Google Scholar 

  • Pal S, Sarda R (2021a). Modeling riparian flood plain wetland water richness in pursuance of damming and linking it with a methane emission rate. Geocarto Int 1–29

  • Pal S, Sarda R (2021b). Modelling water richness in riparian flood plain wetland using bivariate statistics and machine learning algorithms and figuring out the role of damming. Geocarto Int 1–24

  • Pal S, Sarda R (2021c) Measuring the degree of hydrological variability of riparian wetland using hydrological attributes integration (HAI) histogram comparison approach (HCA) and range of variability approach (RVA). Ecol Indic 120:106966

    Article  Google Scholar 

  • Pal S, & Singha P (2021). Analyzing sensitivity of flood susceptible model in a flood plain river basin. Geocarto Int 1-34

  • Pal S, Singha P (2022) Image-driven hydrological parameter coupled identification of flood plain wetland conservation and restoration sites. J Environ Manag 318:115602

    Article  Google Scholar 

  • Pal S, Talukdar S (2018) Drivers of vulnerability to wetlands in Punarbhaba river basin of India-Bangladesh. Ecol Indic 93:612–626

    Article  Google Scholar 

  • Pal S, Talukdar S (2019) Impact of missing flow on active inundation areas and transformation of parafluvial wetlands in Punarbhaba-Tangon river basin of Indo-Bangladesh. Geocarto International 34(10):1055–1074

    Article  Google Scholar 

  • Pal S, Talukdar S (2020) Modelling seasonal flow regime and environmental flow in Punarbhaba river of India and Bangladesh. J Clean Prod 252:119724

    Article  Google Scholar 

  • Pal S, Saha A, Das T (2019) Analysis of flow modifications and stress in the Tangon river basin of the Barind tract. Int J River Basin Manag 17(3):301–321

    Article  Google Scholar 

  • Pal S, Sarkar R, Saha TK (2022) Exploring the forms of wetland modifications and investigating the causes in lower Atreyee river floodplain area. Ecol Inform 67:101494

    Article  Google Scholar 

  • Park E, Ho HL, Tran DD, Yang X, Alcantara E, Merino E, Son VH (2020) Dramatic decrease of flood frequency in the Mekong Delta due to river-bed mining and dyke construction. Sci Total Environ 723:138066

    Article  CAS  Google Scholar 

  • Paula Filho FJ, Marins RV, Chicharo L, Souza RB, Santos GV, Braz EMA (2020) Evaluation of water quality and trophic state in the Parnaíba River Delta, northeast Brazil. Reg Stud Mar Sci 34:101025

    Google Scholar 

  • Pham BT, Tien Bui D, Pham HV, Le HQ, Prakash I, Dholakia MB (2017) Landslide hazard assessment using random subspace fuzzy rules based classifier ensemble and probability analysis of rainfall data: a case study at Mu Cang Chai District, Yen Bai Province (Viet Nam). J Indian Soc Remote Sens 45(4):673–683

    Article  Google Scholar 

  • Ranjan RK, Kumari P (2018) Impact of land use and land cover changes on nutrients concentration in and around Kabar tal wetland, Begusarai (Bihar), India. Geosptaial Appl Nat Resource Manag 2018:243–250

    Google Scholar 

  • Rashid KM, Louis J (2019) Times-series data augmentation and deep learning for construction equipment activity recognition. Adv Eng Inform 42:100944

    Article  Google Scholar 

  • Ren K, Huang S, Huang Q, Wang H, Leng G, Cheng L, ... & Li P (2019). A nature-based reservoir optimization model for resolving the conflict in human water demand and riverine ecosystem protection. J Clean Prod 231:406-418

  • 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 

  • Rideout NK, Lapen DR, Peters DL, Baird DJ (2021) Ditch the low flow: agricultural impacts on flow regimes and consequences for aquatic ecosystem functions. Ecohydrology 2021:e2364

    Google Scholar 

  • Saetang S, & Jakmunee J (2021). Evaluation of eutrophication state of Mae Kuang Reservoir, Chiang Mai, Thailand by Using Carlson’s Trophic State Index

  • Saha TK, Pal S (2019a) Emerging conflict between agriculture extension and physical existence of wetland in post-dam period in Atreyee River basin of Indo-Bangladesh. Environ Dev Sustain 21(3):1485–1505

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Saha TK, Pal S, Sarda R (2022) Impact of river flow modification on wetland hydrological and morphological characters. Environ Sci Pollut Res 1–21

  • Shen L, Li C (2010) Water body extraction from Landsat ETM+ imagery using adaboost algorithm. In: 2010 18th International Conference on Geoinformatics. IEEE, pp 1–4

  • Shrivastava NG (2021) Assessment of lake water quality by using trophic state index indicators: a case study of Nainital, Kumaun Region. AJES, Uttrakhand

    Google Scholar 

  • Singha P, Pal S (2022). Predicting wetland area and water depth in Barind plain of India. Environ Sci Pollut Res 1–17

  • Singha P, Das P, Talukdar S, Pal S (2020) Modeling livelihood vulnerability in erosion and flooding induced river island in Ganges riparian corridor. India Ecol Indic 119:106825

    Article  Google Scholar 

  • Song C, Cao X, Zhou Y, Azzaro M, Monticelli LS, Leonardi M et al (2018) Different pathways of nitrogen and phosphorus regeneration mediated by extracellular enzymes in temperate lakes under various trophic state. Environ Sci Pollut Res 25(31):31603–31615

    Article  CAS  Google Scholar 

  • Subasi A, Yaman E (2021) Diagnosis of neuromuscular disorders using TQWT and random subspace ensemble classifier. International Conference on Medical and Biological Engineering. Springer, Cham, pp 10–19

    Google Scholar 

  • Suwal N, Kuriqi A, Huang X, Delgado J, Młyński D, Walega A (2020) Environmental flows assessment in Nepal: the case of Kaligandaki River. Sustainability 12(21):8766

    Article  Google Scholar 

  • Svozil D, Ševčík JGK, Kvasnicka V (1997) Neural network prediction of the solvatochromic polarity/polarizability parameter. J Chem Inf Comput Sci 37(2):338–342

    Article  CAS  Google Scholar 

  • Talukdar S, Pal S (2019) Effects of damming on the hydrological regime of Punarbhaba river basin wetlands. Ecol Eng 135:61–74

    Article  Google Scholar 

  • Talukdar S, Pal S (2020) Modeling flood plain wetland transformation in consequences of flow alteration in Punarbhaba river in India and Bangladesh. J Clean Prod 261:120767

    Article  Google Scholar 

  • Talukdar S, Singha P, Mahato S, Pal S, Liou YA, Rahman A (2020) Land-use land-cover classification by machine learning classifiers for satellite observations—a review. Remote Sens 12(7):1135

    Article  Google Scholar 

  • Talukdar S, Eibek KU, Akhter S, Ziaul SK, Islam ARMT, Mallick J (2021) Modeling fragmentation probability of land-use and land-cover using the bagging, random forest and random subspace in the Teesta River Basin, Bangladesh. Ecol Indic 126:107612

  • Tealdi S, Camporeale C, Ridolfi L (2011) Modeling the impact of river damming on riparian vegetation. J Hydrol 396(3–4):302–312

    Article  Google Scholar 

  • Tehrany MS, Pradhan B, Mansor S, Ahmad N (2015) Flood susceptibility assessment using GIS-based support vector machine model with different kernel types. Catena 125:91–101

    Article  Google Scholar 

  • Tian Y, Wang L (2020) The effect of urban-suburban interaction on urbanization and suburban ecological security: a case study of suburban Wuhan, Central China. Sustainability 12(4):1600

    Article  Google Scholar 

  • Tian X, Zhao G, Mu X, Zhang P, Tian P, Gao P, Sun W (2019) Hydrologic alteration and possible underlying causes in the Wuding River, China. Sci Total Environ 693:133556

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  • Vogel RM, Sieber J, Archfield SA, Smith MP, Apse CD, Huber-Lee A (2007) Relations among storage, yield, and instream flow. Water Resour Res 43(5)

  • Vörösmarty G, Dobos I (2020) Green purchasing frameworks considering firm size: a multicollinearity analysis using variance inflation factor. In: In Supply Chain Forum: An International Journal, 21, 4 edn. Taylor & Francis, London, pp 290–301

    Google Scholar 

  • Walls M, Hvidberg M, Kleist M, Knudsen P, Mørch P, Egede P et al (2020). Hydrological instability and archaeological impact in Northwest Greenland: Sudden mass movement events signal new concerns for circumpolar archaeology. Quat Sci Rev 248:106600

  • Wang X, Tang X (2006) Random sampling for subspace face recognition. Int J Comput Vis 70(1):91–104

    Article  Google Scholar 

  • Wang Q, Xu Y, Xu Y, Wu L, Wang Y, Han L (2018) Spatial hydrological responses to land use and land cover changes in a typical catchment of the Yangtze River Delta region. Catena 170:305–315

    Article  Google Scholar 

  • Wang Y, Tao Y, Sheng D, Zhou Y, Wang D, Shi X et al (2020) Quantifying the change in streamflow complexity in the Yangtze River. Environ Res 180:108833

    Article  CAS  Google Scholar 

  • World Commission on Dams (2000) Dams and development: a new framework for decision-making: the report of the world commission on dams. Earthscan

  • Wu H, Chen J, Xu J, Zeng G, Sang L, Liu Q et al (2019) Effects of dam construction on biodiversity: a review. J Clean Prod 221:480–489

    Article  Google Scholar 

  • Xu H (2006) Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. Int J Remote Sens 27(14):3025–3033

    Article  Google Scholar 

  • Xu D, Lyon SW, Mao J, Dai H, Jarsjö J (2020) Impacts of multi-purpose reservoir construction, land-use change and climate change on runoff characteristics in the Poyang Lake basin, China. J Hydrol: Reg Stud 29:100694

    Google Scholar 

  • Xue L, Yang F, Yang C, Chen X, Zhang L, Chi Y, Yang G (2017) Identification of potential impacts of climate change and anthropogenic activities on streamflow alterations in the Tarim River Basin, China. Sci Rep 7(1):1–12

    Article  Google Scholar 

  • Yang T, Zhang Q, Chen YD, Tao X, Xu CY, Chen X (2008) A spatial assessment of hydrologic alteration caused by dam construction in the middle and lower Yellow River, China. Hydrol Processes: An Int J 22(18):3829–3843

    Article  Google Scholar 

  • Yang W, Yang H, Yang D, Hou A (2021) Causal effects of dams and land cover changes on flood changes in mainland China. Hydrol Earth Syst Sci 25(5):2705–2720

    Article  Google Scholar 

  • Yao J, Shepperd M (2020) Assessing software defection prediction performance: why using the Matthews correlation coefficient matters. Proc Eval Assess Softw Eng:120–129

  • Yuqin G, Pandey KP, Huang X, Suwal N, Bhattarai KP (2019) Estimation of hydrologic alteration in Kaligandaki River using representative hydrologic indices. Water 11(4):688

    Article  Google Scholar 

  • Zangeneh H, Omidi M, Hadaavi M, Seidekhani H, Sayehmiri K (2021) Predictive factors of glycosylated hemoglobin using additive regression model

  • Zhang K, Batterman S (2010) Near-road air pollutant concentrations of CO and PM2. 5: a comparison of MOBILE6. 2/CALINE4 and generalized additive models. Atmos Environ 44(14):1740–1748

    Article  CAS  Google Scholar 

  • Zhang Y, Zhai X, Zhao T (2018) Annual shifts of flow regime alteration: new insights from the Chaishitan Reservoir in China. Sci Rep 8(1):1–11

    Google Scholar 

  • Zheng H, Wang L, Peng W, Zhang C, Li C, Robinson BE, ... & Daily GC (2019). Realizing the values of natural capital for inclusive, sustainable development: informing China’s new ecological development strategy. Proc Natl Acad Sci 116(17):8623-8628

  • Zhou G, Moayedi H, Bahiraei M, Lyu Z (2020) Employing artificial bee colony and particle swarm techniques for optimizing a neural network in prediction of heating and cooling loads of residential buildings. J Clean Prod 254:120082

    Article  Google Scholar 

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Acknowledgements

The second author of the article would like to thank the University Grants Commission (UGC ref. no. 3267/(SC)(NET-JAN.2017), New Delhi, India, for providing financial support as a Junior Research Fellowship (JRF) to conduct the research work presented in this paper. We are also thankful to Dr. Alexandros Stefanakis (Editor), Environmental Science and Pollution Research, and three anonymous reviewers for their highly constructive suggestions for improving the manuscript.

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  1. Department of Geography, University of Gour Banga, Malda, India

    Swades Pal & Pankaj Singha

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  1. Swades Pal
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Conceptualization, Swades Pal and Pankaj Singha; formal analysis, Swades Pal and Pankaj Singha; methodology, Swades Pal and Pankaj Singha; software, Pankaj Singha; supervision, Swades Pal; validation: Pankaj Singha; writing—original draft, Swades Pal and Pankaj Singha; writing—review and editing, Swades Pal and Pankaj Singha.

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Correspondence to Pankaj Singha.

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Pal, S., Singha, P. Linking river flow modification with wetland hydrological instability, habitat condition, and ecological responses. Environ Sci Pollut Res 30, 11634–11660 (2023). https://doi.org/10.1007/s11356-022-22761-y

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