Estimation of Runoff under changed climatic scenario of a Meso Scale River by Neural Network Based Gridded Model Approach Article Swipe
Debajit Das
,
Tilottama Chakraborty
,
Mrinmoy Majumder
,
Tarun Kanti Bandyopadhyay
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-223790/v1
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-223790/v1
As climate change is linked with changes in precipitation, evapotranspiration and changes in other climatological parameters, these changes will be affected runoff of a river basin. Gomati River basin is the largest river basin among all the river basin of Tripura. Due to the increase in settlement in the Gomati river basin and climate change may threaten natural flow patterns that endure its diversity. This study assesses the impact of climate change on total flow of a catchment in North East India (Gomati River catchment). For this assessment, the Group Method of Data Handling Modeling System (GMDH) model was used to simulate the rainfall-runoff relationship of the catchment, with respect to the observed data during the period of 2008–2009. The statistically downscaled outputs of HadGEM2-ES (Hadley Centre Global Environment Model version 2), general circulation models (GCMs) 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 projections, respectively. The results from the present study can contribute to the development of adaptive strategies and future policies for the sustainable management of water resources in North East, Tripura.
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- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-223790/v1
- https://www.researchsquare.com/article/rs-223790/v1.pdf?c=1631873297000
- OA Status
- gold
- Cited By
- 3
- References
- 20
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3138774087
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https://openalex.org/W3138774087Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.21203/rs.3.rs-223790/v1Digital Object Identifier
- Title
-
Estimation of Runoff under changed climatic scenario of a Meso Scale River by Neural Network Based Gridded Model ApproachWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-03-12Full publication date if available
- Authors
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Debajit Das, Tilottama Chakraborty, Mrinmoy Majumder, Tarun Kanti BandyopadhyayList of authors in order
- Landing page
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https://doi.org/10.21203/rs.3.rs-223790/v1Publisher landing page
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https://www.researchsquare.com/article/rs-223790/v1.pdf?c=1631873297000Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.researchsquare.com/article/rs-223790/v1.pdf?c=1631873297000Direct OA link when available
- Concepts
-
Scale (ratio), Surface runoff, Artificial neural network, Environmental science, Estimation, Climatology, Meteorology, Computer science, Hydrology (agriculture), Geography, Artificial intelligence, Cartography, Geology, Engineering, Ecology, Geotechnical engineering, Systems engineering, BiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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3Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2, 2023: 1Per-year citation counts (last 5 years)
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20Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.Model | 130 |
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| abstract_inverted_index.River | 28, 84, 150 |
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| abstract_inverted_index.basin | 29, 34, 39, 52 |
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| abstract_inverted_index.other | 14 |
| abstract_inverted_index.river | 25, 33, 38, 51 |
| abstract_inverted_index.study | 66, 169 |
| abstract_inverted_index.these | 17 |
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| abstract_inverted_index.(GMDH) | 97 |
| abstract_inverted_index.2030s, | 158 |
| abstract_inverted_index.Basin. | 151 |
| abstract_inverted_index.Centre | 127 |
| abstract_inverted_index.Future | 152 |
| abstract_inverted_index.Global | 128 |
| abstract_inverted_index.Gomati | 27, 50, 149 |
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| abstract_inverted_index.System | 96 |
| abstract_inverted_index.assess | 141 |
| abstract_inverted_index.basin. | 26 |
| abstract_inverted_index.change | 3, 55, 72, 146 |
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| abstract_inverted_index.endure | 62 |
| abstract_inverted_index.future | 179 |
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| abstract_inverted_index.(Hadley | 126 |
| abstract_inverted_index.changes | 7, 12, 18 |
| abstract_inverted_index.climate | 2, 54, 71, 145 |
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| abstract_inverted_index.impacts | 143 |
| abstract_inverted_index.largest | 32 |
| abstract_inverted_index.natural | 58 |
| abstract_inverted_index.outputs | 123 |
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| abstract_inverted_index.respect | 110 |
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| abstract_inverted_index.Modeling | 95 |
| abstract_inverted_index.Tripura. | 41, 191 |
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| abstract_inverted_index.affected | 21 |
| abstract_inverted_index.assesses | 67 |
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| abstract_inverted_index.diversity. | 64 |
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| abstract_inverted_index.settlement | 47 |
| abstract_inverted_index.strategies | 177 |
| abstract_inverted_index.Environment | 129 |
| abstract_inverted_index.assessment, | 88 |
| abstract_inverted_index.catchment). | 85 |
| abstract_inverted_index.circulation | 134 |
| abstract_inverted_index.development | 174 |
| abstract_inverted_index.parameters, | 16 |
| abstract_inverted_index.projections | 153 |
| abstract_inverted_index.sustainable | 183 |
| abstract_inverted_index.2008–2009. | 119 |
| abstract_inverted_index.projections, | 162 |
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| abstract_inverted_index.evapotranspiration | 10 |
| abstract_inverted_index.<title>Abstract</title> | 0 |
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| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5011340449 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| corresponding_institution_ids | https://openalex.org/I196486160 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/6 |
| sustainable_development_goals[0].score | 0.4399999976158142 |
| sustainable_development_goals[0].display_name | Clean water and sanitation |
| citation_normalized_percentile.value | 0.42437696 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |