Assessing the impact of climate change on streamflow in the Tamor River Basin, Nepal: an analysis using SWAT and CMIP6 scenarios
- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- Extent
-
1 Online-Ressource.
- Language
-
Englisch
- Bibliographic citation
-
Assessing the impact of climate change on streamflow in the Tamor River Basin, Nepal: an analysis using SWAT and CMIP6 scenarios ; volume:1 ; number:1 ; day:2 ; month:12 ; year:2024 ; pages:1-20 ; date:12.2024
Discover civil engineering ; 1, Heft 1 (2.12.2024), 1-20, 12.2024
- Creator
-
Subedi, Suresh Raj
Lamichhane, Manoj
Dhungana, Susan
Chalise, Bibek
Bhattarai, Shishir
Chaulagain, Upendra
Khatiwada, Rakesh
- Contributor
-
SpringerLink (Online service)
- DOI
-
10.1007/s44290-024-00143-2
- URN
-
urn:nbn:de:101:1-2502162231023.384555117491
- Rights
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
- 15.08.2025, 7:20 AM CEST
Data provider
This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Subedi, Suresh Raj
- Lamichhane, Manoj
- Dhungana, Susan
- Chalise, Bibek
- Bhattarai, Shishir
- Chaulagain, Upendra
- Khatiwada, Rakesh
- SpringerLink (Online service)
Other Objects (12)
Machine learning based streamflow prediction in a hilly catchment for future scenarios using CMIP6 data
Machine-learning- and deep-learning-based streamflow prediction in a hilly catchment for future scenarios using CMIP6 GCM data
Quantifying the streamflow response to groundwater abstractions for irrigation or drinking water at catchment scale using SWAT and SWAT–MODFLOW
Intercomparison of deep learning models in predicting streamflow patterns: insight from CMIP6
Sensitivity of SWAT simulated streamflow to climatic changes within the Eastern Nile River basin
SWAt
SWAT
swat
SWAT
Swat
SWAT
SWAT
Machine learning based streamflow prediction in a hilly catchment for future scenarios using CMIP6 data
Machine-learning- and deep-learning-based streamflow prediction in a hilly catchment for future scenarios using CMIP6 GCM data
Quantifying the streamflow response to groundwater abstractions for irrigation or drinking water at catchment scale using SWAT and SWAT–MODFLOW
Intercomparison of deep learning models in predicting streamflow patterns: insight from CMIP6
Sensitivity of SWAT simulated streamflow to climatic changes within the Eastern Nile River basin
SWAt
SWAT
swat
SWAT
Swat
SWAT
SWAT
Machine learning based streamflow prediction in a hilly catchment for future scenarios using CMIP6 data
Machine-learning- and deep-learning-based streamflow prediction in a hilly catchment for future scenarios using CMIP6 GCM data
Quantifying the streamflow response to groundwater abstractions for irrigation or drinking water at catchment scale using SWAT and SWAT–MODFLOW
Intercomparison of deep learning models in predicting streamflow patterns: insight from CMIP6
Sensitivity of SWAT simulated streamflow to climatic changes within the Eastern Nile River basin
SWAt
SWAT
swat
SWAT
Swat
SWAT