Abstract
Climate change is linked with change in precipitation, evapotranspiration, and other climatological parameters, and therefore the runoff of a river basin will be affected. The Gomati River basin is the largest in Tripura. The increased settlement in the Gomati River basin and climate change may threaten the natural flow patterns that enable its diversity. This study assesses the impact of climate change on total flow from a catchment in northeast India (the Gomati River catchment). For this assessment, the Group Method of Data Handling (GMDH) model was used to simulate the rainfall–runoff relationship in the catchment with respect to the observed data during 2008–2009. The statistically downscaled outputs of the Hadley Centre Global Environment Model version 2 (HadGEM2-ES) general circulation model scenario was used to assess the impacts of climate change on the Gomati River basin. Future projections were developed for the 2030s, 2040s, and 2050s. The results of this study may contribute to the development of adaptive strategies and future policies for the sustainable management of water resources in northeast Tripura.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Availability of Data and Materials
Source of data has been clearly mentioned in the manuscript.
References
Abram NJ, Henley BJ, Gupta AS, Lippmann TJR, Clarke H, Dowdy AJ, Sharples JJ, Nolan RH, Zhang T, Wooster MJ, Wurtzel JB, Meissner KJ, Pitman AJ, Ukkola AM, Murphy BP, Tapper NJ, Boer MM (2021) Connections of climate change and variability to large and extreme forest fires in southeast Australia. Commun Earth Environ 2(1):1–17. https://doi.org/10.1038/s43247-020-00065-8
AI_Duais MS, Mohamad FS (2016) A review on enhancements to speed up training of the batch back propagation algorithm. Indian J Sci Technol 46:1–10. https://doi.org/10.17485/ijst/2016/v9i46/91755
Anastasakis L, Mort N (2001) The development of self-organization techniques in modelling: a review of the group method of data handling (GMDH). Research report, University of Sheffield Department of Automatic Control and Systems Engineering. https://gmdhsoftware.com/GMDH_%20Anastasakis_and_Mort_2001.pdf. Accessed 25 Dec 2019
Collins WJ, Bellouin N, Doutriaux-Boucher M, Gedney N, Halloran P, Hinton T, Hughes J, Jones CD, Joshi M, Liddicoat S, Martin G, O’Connor F, Rae J, Senior C, Sitch S, Totterdell I, Wiltshire A, Woodward S (2011) Development and evaluation of an Earth-System model–HadGEM2. Geosci Model Dev Discuss 4(2):997–1062. https://doi.org/10.5194/gmd-4-1051-2011
Dessu SB, Melesse AM (2013) Evaluation and comparison of satellite and GCM rainfall estimates for the Mara River Basin, Kenya/Tanzania. In: Younos T, Grady C (eds) Climate Change and Water Resources. The Handbook of Environmental Chemistry, vol 25. Springer, Berlin, Heidelberg 29–45. https://doi.org/10.1007/698_2013_219
Forest Survey of India (2017) ISFR report of Tripura. http://fsi.nic.in/isfr2017/tripura-isfr-2017.pdf. Accessed 25 Jun 2020
Freedman DA (2009) Statistical models: theory and practice. Cambridge University Press
Fu LM, Hsu H-H, Principe JC (1996) Incremental backpropagation learning networks. IEEE Trans Neural Networks 7(3):757–761. https://doi.org/10.1109/72.501732
Gunathilake MB, Karunanayake C, Gunathilake AS, Marasingha N, Samarasinghe JT, Bandara IM, Rathnayake U (2021) Hydrological models and Artificial Neural Networks (ANNs) to simulate streamflow in a tropical catchment of Sri Lanka. Appl Comput Intell Soft Comput. https://doi.org/10.1155/2021/6683389
Gyamfi C, Tindan J-O, Kifanyi GE (2021) Evaluation of CORDEX Africa multi-model precipitation simulations over the Pra River Basin, Ghana. J Hydrol Reg Stud 35:100815. https://doi.org/10.1016/j.ejrh.2021.100815
Ibrahim KSMH, Huang YF, Ahmed AN, Koo CH, El-Shafie A (2022) A review of the hybrid artificial intelligence and optimization modelling of hydrological streamflow forecasting. Alex Eng J 61(1):279–303. https://doi.org/10.1016/j.aej.2021.04.100
Jana BK, Majumder M (2010) Impact of climate change on natural resource management. Springer Science & Business Media
Krishnan R, Sanjay J, Gnanaseelan C, Mujumdar M, Kulkarni A, Chakraborty S (2020) Assessment of climate change over the Indian region: a report of the ministry of earth sciences (MoES), Government of India. https://doi.org/10.1007/978-981-15-4327-2
Kumar D, Roshni T, Singh A, Himayoun D, Samui P (2021) A Simplified Approach for Rainfall-runoff Modeling Using Advanced Soft-computing Methods. Jordan J Civ Eng 15(3)
Mali SS, Shirsath PB, Islam A (2021) A high-resolution assessment of climate change impact on water footprints of cereal production in India. Sci Rep 11(1):1–13. https://doi.org/10.1038/s41598-021-88223-6
Marquardt DW (1963) An algorithm for least-squares estimation of nonlinear parameters. J Soc Ind Appl Math 11(2):431–441. https://doi.org/10.1137/0111030
Mathur I, Bhattacharya P (2022) Transition from shifting cultivation to agroforestry: A case study of regrouped villages in Tripura, India. Environmental Challenges 1(7):100471. https://www.sciencedirect.com/science/article/pii/S2667010022000312
Mehdi B, Dekens J, Herrnegger M (2021) Climatic impacts on water resources in a tropical catchment in Uganda and adaptation measures proposed by resident stakeholders. Clim Change 164(1):1–22. https://doi.org/10.1007/s10584-021-02958-9
Moriasi DN, Arnold JG, Van Liew MW, Bingner RL, Harmel RD, Veith TL (2007) Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans ASABE 50(3):885–900. https://doi.org/10.13031/2013.23153
Oscar-Junior AC (2021) Precipitation trends and variability in river basins in urban expansion areas. Water Resour Manag 35(2):661–674. https://doi.org/10.1007/s11269-020-02749-4
Rodriguez E, Sánchez I, Duque N, Arboleda P, Vega C, Zamora D, López P et al (2020) Combined use of local and global hydro meteorological data with hydrological models for water resources management in the Magdalena-Cauca Macro Basin-Colombia. Water Resour Manage 34(2020):2179–2199. https://doi.org/10.1007/s11269-019-02236-5
Rustad L, Campbell J, Marion G, Norby R, Mitchell M, Hartley A, Cornelissen J, Gurevitch J, GCTE-NEWS (2001) A meta-analysis of the response of soil respiration, net nitrogen mineralization, and aboveground plant growth to experimental ecosystem warming. Oecologia 126(4):543–562. https://doi.org/10.1007/s004420000544
Shin S, Pokhrel Y, Talchabhadel R, Panthi J (2021) Spatio-temporal dynamics of hydrologic changes in the Himalayan river basins of Nepal using high-resolution hydrological-hydrodynamic modeling. J Hydrol 598:126209. https://doi.org/10.1016/j.jhydrol.2021.126209
Strohmeier S, López PL, Haddad M, Nangia V, Karrou M, Montanaro G, Boudhar A, Linés C, Veldkamp T, Sterk G (2020) Surface runoff and drought assessment using global water resources datasets-from Oum Er Rbia Basin to the Moroccan country scale. Water Resour Manage 34(7):2117–2133. https://doi.org/10.1007/s11269-019-02251-6
Subramanya K (2013) Engineering Hydrology, 4e. Tata McGraw-Hill Education
Velasco LCP, Arnejo KAS, Macarat JSS (2022) Performance analysis of artificial neural network models for hour-ahead electric load forecasting. Procedia Comput Sci 197:16–24. https://doi.org/10.1016/j.procs.2021.12.113
Waziri MY, Ahmed K (2022) Two descent Dai-Yuan conjugate gradient methods for systems of monotone nonlinear equations. J Sci Comput 90(1):1–5. https://doi.org/10.1007/s10915-021-01713-7
Wootten AM, Dixon KW, Adams-Smith DJ, McPherson RA (2021) Statistically downscaled precipitation sensitivity to gridded observation data and downscaling technique. Int J Climatol 41:980–1001. https://doi.org/10.1002/joc.6716
Zotou I, Bellos V, Gkouma A, Karathanassi V, Tsihrintzis VA (2020) Using Sentinel-1 imagery to assess predictive performance of a hydraulic model. Water Resour Manage 34:4415–4430. https://doi.org/10.1007/s11269-020-02592-7
Author information
Authors and Affiliations
Contributions
The study, concept and design, material preparation, data collection, analysis, writing of manuscript were performed by Debajit Das, Tilottama Chakraborty, Mrinmoy Majumder and Tarun Kanti Bandyopadhyay. All authors read and approved the final manuscript.
Corresponding author
Ethics declarations
Ethical Approval
-
The manuscript should not be submitted to more than one journal for simultaneous consideration.
Answer: Not submitted.
-
The submitted work should be original and should not have been published elsewhere in any form or language (partially or in full), unless the new work concerns an expansion of previous work. (Please provide transparency on the re-use of material to avoid the concerns about text-recycling (‘self-plagiarism’).
Answer: The submitted work is original and the work not has been published elsewhere.
-
A single study should not be split up into several parts to increase the quantity of submissions and submitted to various journals or to one journal over time (i.e. ‘salami-slicing/publishing’).
Answer: Not done.
-
Concurrent or secondary publication is sometimes justifiable, provided certain conditions are met. Examples include: translations or a manuscript that is intended for a different group of readers.
Answer: Not applicable.
-
Results should be presented clearly, honestly, and without fabrication, falsification or inappropriate data manipulation (including image based manipulation). Authors should adhere to discipline-specific rules for acquiring, selecting and processing data.
Answer: Results has been presented clearly, honestly, and without fabrication, falsification or inappropriate data manipulation (including image based manipulation).
-
No data, text, or theories by others are presented as if they were the author’s own (‘plagiarism’). Proper acknowledgements to other works must be given (this includes material that is closely copied (near verbatim), summarized and/or paraphrased), quotation marks (to indicate words taken from another source) are used for verbatim copying of material, and permissions secured for material that is copyrighted.
Answer: Not applicable.
-
Authors should make sure they have permissions for the use of software, questionnaires/ (web) surveys and scales in their studies (if appropriate).
Answer: Institute facility has been utilized with proper permission.
-
Research articles and non-research articles (e.g. Opinion, Review, and Commentary articles) must cite appropriate and relevant literature in support of the claims made. Excessive and inappropriate self-citation or coordinated efforts among several authors to collectively self-cite is strongly discouraged.
Answer: All the above mentioned things has been done appropriately.
-
Authors should avoid untrue statements about an entity (who can be an individual person or a company) or descriptions of their behaviour or actions that could potentially be seen as personal attacks or allegations about that person.
Answer: Not applicable.
-
Research that may be misapplied to pose a threat to public health or national security should be clearly identified in the manuscript (e.g. dual use of research). Examples include creation of harmful consequences of biological agents or toxins, disruption of immunity of vaccines, unusual hazards in the use of chemicals, weaponization of research/technology (amongst others).
Answer: Not applicable.
-
Authors are strongly advised to ensure the author group, the Corresponding Author, and the order of authors are all correct at submission. Adding and/or deleting authors during the revision stages is generally not permitted, but in some cases may be warranted. Reasons for changes in authorship should be explained in detail. Please note that changes to authorship cannot be made after acceptance of a manuscript.
Answer: All the above mentioned things has been done appropriately.
Consent to Participate
All the authors has given their consent for participating in this research work.
Consent to Publish
All the authors has given their consent for publishing this research work.
Competing Interests
No conflict of interest for this present study.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Das, D., Chakraborty, T., Majumder, M. et al. Estimation of Runoff Under Changed Climatic Scenario of a Meso Scale River by Neural Network Based Gridded Model Approach. Water Resour Manage 37, 2891–2907 (2023). https://doi.org/10.1007/s11269-022-03211-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11269-022-03211-3