An integrated framework for non-stationary hydrological drought assessment using time-varying parameter standardized streamflow index and time-varying threshold level method

被引:0
|
作者
Wang, Menghao [1 ,2 ,3 ]
Jiang, Shanhu [1 ,3 ,4 ]
Ren, Liliang [1 ,3 ,4 ]
Xu, Junzeng [1 ,2 ,3 ]
Yuan, Shanshui [5 ]
Xu, Chong-Yu [6 ]
机构
[1] Hohai Univ, Natl Key Lab Water Disaster Prevent, Nanjing 210098, Peoples R China
[2] Hohai Univ, Coll Agr Sci & Engn, Nanjing 211100, Peoples R China
[3] Hohai Univ, Cooperat Innovat Ctr Water Safety & Hydrosci, Nanjing 210098, Peoples R China
[4] Hohai Univ, Coll Hydrol & Water Resources, Nanjing 210098, Peoples R China
[5] Hohai Univ, Yangtze Inst Conservat & Dev, Nanjing 210098, Peoples R China
[6] Univ Oslo, Dept Geosci, Oslo, Norway
基金
中国博士后科学基金; 中国国家自然科学基金;
关键词
Non-stationary drought assessment; Hydrological drought; Standardized streamflow index; Threshold level method; Weihe River basin; WEIHE RIVER-BASIN; EMPIRICAL MODE DECOMPOSITION; FLOOD FREQUENCY-ANALYSIS; CLIMATE-CHANGE; RUNOFF; IMPACTS; SCALE;
D O I
10.1016/j.ejrh.2025.102329
中图分类号
TV21 [水资源调查与水利规划];
学科分类号
081501 ;
摘要
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 SSIvarhas 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.
引用
收藏
页数:16
相关论文
共 50 条
  • [21] Decomposing Non-Stationary Signals With Time-Varying Wave-Shape Functions
    Colominas, Marcelo A.
    Wu, Hau-Tieng
    IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2021, 69 : 5094 - 5104
  • [22] Achieving shrinkage in a time-varying parameter model framework
    Bitto, Angela
    Fruehwirth-Schnatter, Sylvia
    JOURNAL OF ECONOMETRICS, 2019, 210 (01) : 75 - 97
  • [23] A wavelet-based time-varying autoregressive model for non-stationary and irregular time series
    Salcedo, G. E.
    Porto, R. F.
    Roa, S. Y.
    Momo, F. R.
    JOURNAL OF APPLIED STATISTICS, 2012, 39 (11) : 2313 - 2325
  • [24] Modelling non-stationary signals by time-dependent AR process with time-varying gain
    Mukhopadhyay, S
    Sircar, P
    IETE JOURNAL OF RESEARCH, 1997, 43 (05) : 351 - 358
  • [25] Controlling time-varying confounding in difference-in-differences studies using the time-varying treatments framework
    Myint, Leslie
    HEALTH SERVICES AND OUTCOMES RESEARCH METHODOLOGY, 2024, 24 (01) : 95 - 111
  • [26] Controlling time-varying confounding in difference-in-differences studies using the time-varying treatments framework
    Leslie Myint
    Health Services and Outcomes Research Methodology, 2024, 24 : 95 - 111
  • [27] An Alternative Estimation Method for Time-Varying Parameter Models
    Ito, Mikio
    Noda, Akihiko
    Wada, Tatsuma
    ECONOMETRICS, 2022, 10 (02)
  • [28] Non-stationary time-varying vehicular channel characteristics for different roadside scattering environments
    Li, Changzhen
    Chen, Wei
    Pei, Zhonghui
    Chang, Fuxing
    Yu, Junyi
    Luo, Fan
    SCIENTIFIC REPORTS, 2022, 12 (01)
  • [29] Adaptive channel estimation for OFDM systems in time-varying non-stationary wireless channels
    Zeng, Jianqiang
    Minn, Hlaing
    2007 IEEE SARNOFF SYMPOSIUM, 2007, : 304 - 308
  • [30] Time-Varying Threshold Regression Model Using the Kalman Filter Method
    Sirikanchanarak, Duangthip
    Yamaka, Worapon
    Khiewgamdee, Chatchai
    Sriboonchitta, Songsak
    THAI JOURNAL OF MATHEMATICS, 2016, : 133 - 148