Innovative trend analysis for the streamflow sub-time series of the source Region of the Yangtze River
| dc.contributor.author | Ahmed, Naveed | en |
| dc.contributor.author | Lu, Haishen | en |
| dc.contributor.author | Adeyeri, Oluwafemi E. | en |
| dc.date.accessioned | 2025-12-16T21:41:04Z | |
| dc.date.available | 2025-12-16T21:41:04Z | |
| dc.date.issued | 2024-06-03 | en |
| dc.description.abstract | The Mann-Kendall (MK) trend test, Innovative Trend Analysis (ITA), double-ITA (D-ITA), triple-ITA (T-ITA), and Innovative Triangular Trend Analysis (ITTA) were used to analyze long-term trends in the annual and seasonal streamflow of the Tuotuohe and Zhimenda hydrological gauging stations in the Source Region of the Yangtze River (SRYR). The traditional MK test provides the average trends, while the other methods used in this study provide the graphical illustrations and trend stability (monotonic/non-monotonic). For example, the Tuotuohe station during summer using MK showed (0.05 m3/sec/year), and the ITA showed the monotonic increasing trend (1.12 m3/sec/year). In contrast, the ITTA showed unstable (monotonic and non-monotonic) trends while the highest trend magnitude was found from the 2nd to 5th sub-time series (6.6 m3/sec/year) followed by 1st to 5th sub-time series (5.51 m3/sec/year). The ITTA also showed the non-monotonic decreasing trend (-1.09 m3/sec/year) from 1st to 2nd sub-time series. The D-ITA showed the monotonic increasing trend from 1st to 2nd sub-time series (0.83 m3/sec/year). The non-monotonic increasing trend from 2nd to 3rd sub-time series (0.4 m3/sec/year) and T-ITA showed the non-monotonic trend for 1st to 2nd and 2nd to 3rd sub-time series (0.62 and 1.45 m3/sec/year, respectively), whereas the monotonic trend for 3rd to 4th sub-time series (14.94 m3/sec/year). Similarly, there are more instabilities and fluctuations in trend magnitudes found in the ITTA compared to D-ITA, T-ITA, and ITA. At the same time, the MK only provides the average trend values for a given time series. This showed that the ITTA method is better for understanding the trends and fluctuations in any basin, and the traditional MK test cannot detect these fluctuations. | en |
| dc.description.sponsorship | The author acknowledged the Colege of Hydrology and Water Resources, Hohai University, for providing the flow data used in this study. | en |
| dc.description.status | Peer-reviewed | en |
| dc.format.extent | 20 | en |
| dc.identifier.issn | 0177-798X | en |
| dc.identifier.other | WOS:001237776300001 | en |
| dc.identifier.other | ORCID:/0000-0002-9735-0677/work/189655222 | en |
| dc.identifier.scopus | 85195170310 | en |
| dc.identifier.uri | https://hdl.handle.net/1885/733795620 | |
| dc.language.iso | en | en |
| dc.source | Theoretical and Applied Climatology | en |
| dc.subject | Mann-kendall | en |
| dc.subject | Climate variability | en |
| dc.subject | Tibetan plateau | en |
| dc.subject | Impacts | en |
| dc.subject | Precipitation | en |
| dc.subject | China | en |
| dc.subject | Temperature | en |
| dc.subject | Headwaters | en |
| dc.subject | Hydrology | en |
| dc.subject | Extremes | en |
| dc.title | Innovative trend analysis for the streamflow sub-time series of the source Region of the Yangtze River | en |
| dc.type | Journal article | en |
| dspace.entity.type | Publication | en |
| local.bibliographicCitation.lastpage | 6770 | en |
| local.bibliographicCitation.startpage | 6751 | en |
| local.contributor.affiliation | Ahmed, Naveed; Hohai University | en |
| local.contributor.affiliation | Lu, Haishen; Hohai University | en |
| local.contributor.affiliation | Adeyeri, Oluwafemi E.; City University of Hong Kong | en |
| local.identifier.citationvolume | 155 | en |
| local.identifier.doi | 10.1007/s00704-024-05029-y | en |
| local.identifier.pure | 7236e162-f1f0-4f25-b99b-3c8402d804f4 | en |
| local.identifier.url | https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=anu_research_portal_plus2&SrcAuth=WosAPI&KeyUT=WOS:001237776300001&DestLinkType=FullRecord&DestApp=WOS_CPL | en |
| local.identifier.url | https://www.scopus.com/pages/publications/85195170310 | en |
| local.type.status | Published | en |