An integrated framework for non-stationary hydrological drought assessment using time-varying parameter standardized streamflow index and time-varying threshold level method
Study region: Weihe River basin (WRB) in northern China. Study focus: In a changing environment, traditional drought assessment methods may not be applicable when assumptions of stationarity are violated. Accordingly, this study proposes a framework that incorporates the time-varying parameter stand...
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Elsevier
2025-06-01
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| Series: | Journal of Hydrology: Regional Studies |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214581825001533 |
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| author | Menghao Wang Shanhu Jiang Liliang Ren Junzeng Xu Shanshui Yuan Chong-Yu Xu |
| author_facet | Menghao Wang Shanhu Jiang Liliang Ren Junzeng Xu Shanshui Yuan Chong-Yu Xu |
| author_sort | Menghao Wang |
| collection | DOAJ |
| description | Study region: Weihe River basin (WRB) in northern China. Study focus: In a changing environment, traditional drought assessment methods may not be applicable when assumptions of stationarity are violated. Accordingly, this study proposes a framework that incorporates the time-varying parameter standardized streamflow index (SSIvar) and threshold level method (TLvar) for the non-stationary hydrological drought assessment. Then, the SSIvar and TLvar methods are compared with time-invariant and transplantation parameter SSI (SSIinvar and SSItran) and TL (TLinvar and TLtran) to validate their effectiveness. New hydrological insights for the region: Validation results showed that SSIvar has the highest Kendall correlation coefficients with standardized precipitation index (SPI) and soil moisture index (SSMI) at 0.81 and 0.78, respectively, outperforming SSIinvar (0.67and 0.62) and SSItran (0.68 and 0.63). The TLvar method behaves in the same way, indicating that the SSIvar and TLvar methods provide a more accurate assessment of non-stationary hydrological drought. Furthermore, the comparison results show that the mean duration and severity of hydrological drought in the WRB increased by 22.37 % and 13.72 % for SSIvar method and 34.69 % and 19.15 % for TLvar method from 1961–1990 to 1991–2020, respectively, revealing that hydrological drought in the WRB has aggravated over the past 30 years. Overall, the combined use of SSIvar and TLvar provides a comprehensive understanding of non-stationary drought, integrating qualitative (e.g., severity levels) and quantitative (e.g., streamflow deficits) measures. |
| format | Article |
| id | doaj-art-0bcdebabc1194431a6290aaa0dd0a9b2 |
| institution | OA Journals |
| issn | 2214-5818 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
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| series | Journal of Hydrology: Regional Studies |
| spelling | doaj-art-0bcdebabc1194431a6290aaa0dd0a9b22025-08-20T01:55:31ZengElsevierJournal of Hydrology: Regional Studies2214-58182025-06-015910232910.1016/j.ejrh.2025.102329An integrated framework for non-stationary hydrological drought assessment using time-varying parameter standardized streamflow index and time-varying threshold level methodMenghao Wang0Shanhu Jiang1Liliang Ren2Junzeng Xu3Shanshui Yuan4Chong-Yu Xu5The National Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing 210098, China; College of Agricultural Science and Engineering, Hohai University, Nanjing 211100, China; Cooperative Innovation Center for Water Safety and Hydro-Science, Hohai University, Nanjing 210098, ChinaThe National Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing 210098, China; Cooperative Innovation Center for Water Safety and Hydro-Science, Hohai University, Nanjing 210098, China; College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China; Corresponding author at: The National Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing 210098, China.The National Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing 210098, China; Cooperative Innovation Center for Water Safety and Hydro-Science, Hohai University, Nanjing 210098, China; College of Hydrology and Water Resources, Hohai University, Nanjing 210098, ChinaThe National Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing 210098, China; College of Agricultural Science and Engineering, Hohai University, Nanjing 211100, China; Cooperative Innovation Center for Water Safety and Hydro-Science, Hohai University, Nanjing 210098, ChinaYangtze Institute for Conservation and Development, Hohai University, Nanjing 210098, ChinaDepartment of Geosciences, University of Oslo, Oslo, NorwayStudy region: Weihe River basin (WRB) in northern China. Study focus: In a changing environment, traditional drought assessment methods may not be applicable when assumptions of stationarity are violated. Accordingly, this study proposes a framework that incorporates the time-varying parameter standardized streamflow index (SSIvar) and threshold level method (TLvar) for the non-stationary hydrological drought assessment. Then, the SSIvar and TLvar methods are compared with time-invariant and transplantation parameter SSI (SSIinvar and SSItran) and TL (TLinvar and TLtran) to validate their effectiveness. New hydrological insights for the region: Validation results showed that SSIvar has the highest Kendall correlation coefficients with standardized precipitation index (SPI) and soil moisture index (SSMI) at 0.81 and 0.78, respectively, outperforming SSIinvar (0.67and 0.62) and SSItran (0.68 and 0.63). The TLvar method behaves in the same way, indicating that the SSIvar and TLvar methods provide a more accurate assessment of non-stationary hydrological drought. Furthermore, the comparison results show that the mean duration and severity of hydrological drought in the WRB increased by 22.37 % and 13.72 % for SSIvar method and 34.69 % and 19.15 % for TLvar method from 1961–1990 to 1991–2020, respectively, revealing that hydrological drought in the WRB has aggravated over the past 30 years. Overall, the combined use of SSIvar and TLvar provides a comprehensive understanding of non-stationary drought, integrating qualitative (e.g., severity levels) and quantitative (e.g., streamflow deficits) measures.http://www.sciencedirect.com/science/article/pii/S2214581825001533Non-stationary drought assessmentHydrological droughtStandardized streamflow indexThreshold level methodWeihe River basin |
| spellingShingle | Menghao Wang Shanhu Jiang Liliang Ren Junzeng Xu Shanshui Yuan Chong-Yu Xu An integrated framework for non-stationary hydrological drought assessment using time-varying parameter standardized streamflow index and time-varying threshold level method Journal of Hydrology: Regional Studies Non-stationary drought assessment Hydrological drought Standardized streamflow index Threshold level method Weihe River basin |
| title | An integrated framework for non-stationary hydrological drought assessment using time-varying parameter standardized streamflow index and time-varying threshold level method |
| title_full | An integrated framework for non-stationary hydrological drought assessment using time-varying parameter standardized streamflow index and time-varying threshold level method |
| title_fullStr | An integrated framework for non-stationary hydrological drought assessment using time-varying parameter standardized streamflow index and time-varying threshold level method |
| title_full_unstemmed | An integrated framework for non-stationary hydrological drought assessment using time-varying parameter standardized streamflow index and time-varying threshold level method |
| title_short | An integrated framework for non-stationary hydrological drought assessment using time-varying parameter standardized streamflow index and time-varying threshold level method |
| title_sort | integrated framework for non stationary hydrological drought assessment using time varying parameter standardized streamflow index and time varying threshold level method |
| topic | Non-stationary drought assessment Hydrological drought Standardized streamflow index Threshold level method Weihe River basin |
| url | http://www.sciencedirect.com/science/article/pii/S2214581825001533 |
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